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Sweep logs for symbolic-shapes (TORCHDYNAMO_DYNAMIC_SHAPES=1)
Raw
gistfile1.txt
Running BERT_pytorch...
cuda train BERT_pytorch PASS
Running Background_Matting...
cuda train Background_Matting PASS
WARNING:root:DALLE2_pytorch failed to load
Eager model failed to run
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 903, in validate_model
self.model_iter_fn(model, example_inputs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/torchbench.py", line 337, in forward_and_backward_pass
self.grad_scaler.scale(loss).backward()
File "/data/users/ezyang/pytorch-tmp/torch/_tensor.py", line 488, in backward
torch.autograd.backward(
File "/data/users/ezyang/pytorch-tmp/torch/autograd/__init__.py", line 197, in backward
Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1746, in main
device, name, model, example_inputs, batch_size = runner.load_model(
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/torchbench.py", line 282, in load_model
self.validate_model(model, example_inputs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 905, in validate_model
raise NotImplementedError("Eager model failed to run")
NotImplementedError: Eager model failed to run
Running LearningToPaint...
cuda train LearningToPaint PASS
Running Super_SloMo...
cuda train Super_SloMo PASS
Running alexnet...
cuda train alexnet PASS
Running attention_is_all_you_need_pytorch...
cuda train attention_is_all_you_need_pytorch PASS
Running dcgan...
cuda train dcgan PASS
Running densenet121...
cuda train densenet121 PASS
WARNING:root:detectron2_fcos_r_50_fpn failed to load
FCOS train is not supported by upstream detectron2. See GH Issue: https://github.com/facebookresearch/detectron2/issues/4369.
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1746, in main
device, name, model, example_inputs, batch_size = runner.load_model(
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/torchbench.py", line 251, in load_model
benchmark = benchmark_cls(
File "/data/users/ezyang/benchmark/torchbenchmark/util/model.py", line 18, in __call__
obj = type.__call__(cls, *args, **kwargs)
File "/data/users/ezyang/benchmark/torchbenchmark/models/detectron2_fcos_r_50_fpn/__init__.py", line 15, in __init__
super().__init__(variant="COCO-Detection/fcos_R_50_FPN_1x.py", test=test, device=device,
File "/data/users/ezyang/benchmark/torchbenchmark/util/framework/detectron2/model_factory.py", line 100, in __init__
loader = self.setup_train(cfg, args)
File "/data/users/ezyang/benchmark/torchbenchmark/util/framework/detectron2/model_factory.py", line 110, in setup_train
raise NotImplementedError("FCOS train is not supported by upstream detectron2. " \
NotImplementedError: FCOS train is not supported by upstream detectron2. See GH Issue: https://github.com/facebookresearch/detectron2/issues/4369.
WARNING:root:detectron2_maskrcnn_r_50_c4 failed to load
Eager model failed to run
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 903, in validate_model
self.model_iter_fn(model, example_inputs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/torchbench.py", line 336, in forward_and_backward_pass
loss = self.compute_loss(pred)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/torchbench.py", line 326, in compute_loss
return reduce_to_scalar_loss(pred)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/testing.py", line 87, in reduce_to_scalar_loss
return sum([reduce_to_scalar_loss(x) for x in out]) / len(out)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/testing.py", line 87, in <listcomp>
return sum([reduce_to_scalar_loss(x) for x in out]) / len(out)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/testing.py", line 97, in reduce_to_scalar_loss
return sum([reduce_to_scalar_loss(value) for value in out.values()]) / len(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/testing.py", line 97, in <listcomp>
return sum([reduce_to_scalar_loss(value) for value in out.values()]) / len(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/testing.py", line 102, in reduce_to_scalar_loss
raise NotImplementedError("Don't know how to reduce", type(out))
NotImplementedError: ("Don't know how to reduce", <class 'detectron2.structures.instances.Instances'>)
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1746, in main
device, name, model, example_inputs, batch_size = runner.load_model(
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/torchbench.py", line 282, in load_model
self.validate_model(model, example_inputs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 905, in validate_model
raise NotImplementedError("Eager model failed to run")
NotImplementedError: Eager model failed to run
Running dlrm...
ERROR:common:Failed running <class 'range'>(*(9,), **{}):
'SymInt' object cannot be interpreted as an integer
(scroll up for backtrace)
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 53, in _run_node
return node.target(*args, **kwargs)
TypeError: 'SymInt' object cannot be interpreted as an integer
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1041, in check_accuracy
new_result = optimized_model_iter_fn(model, example_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 945, in run_n_iterations
self.model_iter_fn(mod, inputs, collect_outputs=False)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/torchbench.py", line 332, in forward_and_backward_pass
cloned_inputs = clone_inputs(inputs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/torchbench.py", line 335, in <graph break in forward_and_backward_pass>
pred = mod(*cloned_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 240, in catch_errors
return callback(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 437, in _convert_frame
result = inner_convert(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 112, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 87, in time_wrapper
r = func(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 319, in _convert_frame_assert
return _compile(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 374, in _compile
out_code = transform_code_object(code, transform)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/bytecode_transformation.py", line 341, in transform_code_object
transformations(instructions, code_options)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 362, in transform
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1466, in run
super().run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 221, in call_function
return super().call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 191, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 62, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 221, in call_function
return super().call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 191, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 62, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/builtin.py", line 369, in call_function
return DynamicShapeVariable.create(tx, proxy, None, **options)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 634, in create
dyn_shape = _get_fake_value(proxy.node, tx)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 131, in _get_fake_value
return wrap_fake_exception(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 709, in wrap_fake_exception
return fn()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 132, in <lambda>
lambda: _run_node(tx.output, node, args, kwargs, nnmodule)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 62, in _run_node
raise RuntimeError(
RuntimeError: Failed running <class 'range'>(*(9,), **{}):
'SymInt' object cannot be interpreted as an integer
(scroll up for backtrace)
TorchDynamo optimized model failed to run because of following error
cuda train dlrm FAIL
/data/users/ezyang/pytorch-tmp/torch/utils/tensorboard/__init__.py:4: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.
if not hasattr(tensorboard, "__version__") or LooseVersion(
/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/gym/core.py:317: DeprecationWarning: WARN: Initializing wrapper in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.
deprecation(
Running drq...
cuda train drq FAIL (TIMEOUT)
Running fastNLP_Bert...
cuda train fastNLP_Bert PASS
Running functorch_dp_cifar10...
cuda train functorch_dp_cifar10 FAIL (TIMEOUT)
Running functorch_maml_omniglot...
cuda train functorch_maml_omniglot PASS
Running hf_Albert...
cuda train hf_Albert PASS
Running hf_Bart...
cuda train hf_Bart PASS
Running hf_Bert...
cuda train hf_Bert PASS
Running hf_BigBird...
[2022-10-28 21:18:12,960] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
[2022-10-28 21:18:31,350] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
[2022-10-28 21:18:48,911] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
[2022-10-28 21:19:05,973] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
[2022-10-28 21:19:22,545] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
[2022-10-28 21:19:39,380] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
[2022-10-28 21:19:55,624] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
[2022-10-28 21:20:12,111] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
[2022-10-28 21:20:28,583] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
[2022-10-28 21:20:45,996] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
[2022-10-28 21:21:02,718] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
[2022-10-28 21:21:18,346] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
cuda train hf_BigBird FAIL (TIMEOUT)
Running hf_DistilBert...
cuda train hf_DistilBert PASS
Running hf_GPT2...
[2022-10-28 21:24:51,823] torch._dynamo.utils: [ERROR] RMSE (res-fp64): 0.00046, (ref-fp64): 0.00000 and shape=torch.Size([768])
[2022-10-28 21:24:51,823] torch._dynamo.utils: [ERROR] Accuracy failed for key name transformer.h.0.ln_1.bias.grad
cuda train hf_GPT2 FAIL
Running hf_GPT2_large...
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/torchbench.py", line 349, in <module>
main(TorchBenchmarkRunner(), original_dir)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1775, in main
runner.run_one_model(
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 768, in inner
return fn(self, *args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1213, in run_one_model
status = self.check_accuracy(
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1023, in check_accuracy
correct_rerun_result = self.run_n_iterations(
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 946, in run_n_iterations
return self.model_iter_fn(mod, inputs, collect_outputs=True)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/torchbench.py", line 335, in forward_and_backward_pass
pred = mod(*cloned_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/transformers/models/gpt2/modeling_gpt2.py", line 1048, in forward
transformer_outputs = self.transformer(
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/transformers/models/gpt2/modeling_gpt2.py", line 891, in forward
outputs = block(
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/transformers/models/gpt2/modeling_gpt2.py", line 428, in forward
feed_forward_hidden_states = self.mlp(hidden_states)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/transformers/models/gpt2/modeling_gpt2.py", line 356, in forward
hidden_states = self.act(hidden_states)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/transformers/activations.py", line 34, in forward
return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044715 * torch.pow(input, 3.0))))
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 39.59 GiB total capacity; 37.54 GiB already allocated; 3.44 MiB free; 38.41 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
cuda train hf_GPT2_large FAIL
Running hf_Longformer...
[2022-10-28 21:25:53,724] torch._dynamo.variables.builtin: [WARNING] incorrect arg count <bound method BuiltinVariable._call_min_max of BuiltinVariable(max)> missing a required argument: 'b'
[2022-10-28 21:25:53,751] torch._dynamo.variables.builtin: [WARNING] incorrect arg count <bound method BuiltinVariable._call_min_max of BuiltinVariable(max)> missing a required argument: 'b'
[2022-10-28 21:25:53,806] torch._dynamo.variables.builtin: [WARNING] incorrect arg count <bound method BuiltinVariable._call_min_max of BuiltinVariable(max)> missing a required argument: 'b'
[2022-10-28 21:25:58,010] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
[2022-10-28 21:26:02,620] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
[2022-10-28 21:26:04,662] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
[2022-10-28 21:26:08,605] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
Traceback (most recent call last):
File "<string>", line 2, in <lambda>
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/sympy-1.11rc1-py3.9.egg/sympy/core/cache.py", line 70, in wrapper
retval = cfunc(*args, **kwargs)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/sympy-1.11rc1-py3.9.egg/sympy/core/function.py", line 469, in __new__
result = super().__new__(cls, *args, **options)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/sympy-1.11rc1-py3.9.egg/sympy/core/cache.py", line 70, in wrapper
retval = cfunc(*args, **kwargs)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/sympy-1.11rc1-py3.9.egg/sympy/core/function.py", line 309, in __new__
evaluated = cls.eval(*args)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/sympy-1.11rc1-py3.9.egg/sympy/core/mod.py", line 102, in eval
rv = number_eval(p, q)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/sympy-1.11rc1-py3.9.egg/sympy/core/mod.py", line 49, in number_eval
raise ZeroDivisionError("Modulo by zero")
ZeroDivisionError: Modulo by zero
NULL ERROR: /data/users/ezyang/pytorch-tmp/torch/csrc/dynamo/eval_frame.c:239
cuda train hf_Longformer FAIL
Running hf_Reformer...
cuda train hf_Reformer PASS
Running hf_T5...
WARNING:common:fp64 golden ref were not generated for hf_T5
cuda train hf_T5 PASS
Running hf_T5_base...
WARNING:common:fp64 golden ref were not generated for hf_T5_base
cuda train hf_T5_base PASS
Running hf_T5_large...
WARNING:common:fp64 golden ref were not generated for hf_T5_large
cuda train hf_T5_large FAIL (TIMEOUT)
Running lennard_jones...
cuda train lennard_jones PASS
Running maml_omniglot...
cuda train maml_omniglot PASS
Running mnasnet1_0...
cuda train mnasnet1_0 PASS
Running mobilenet_v2...
cuda train mobilenet_v2 PASS
Running mobilenet_v2_quantized_qat...
WARNING:common:fp64 golden ref were not generated for mobilenet_v2_quantized_qat
[2022-10-28 21:41:02,116] torch._dynamo.utils: [ERROR] Accuracy failed for key name classifier.1.bias.grad
cuda train mobilenet_v2_quantized_qat FAIL
Running mobilenet_v3_large...
cuda train mobilenet_v3_large PASS
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Running moco...
ERROR:common:argument of type: <class 'range_iterator'>
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1041, in check_accuracy
new_result = optimized_model_iter_fn(model, example_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 945, in run_n_iterations
self.model_iter_fn(mod, inputs, collect_outputs=False)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/torchbench.py", line 332, in forward_and_backward_pass
cloned_inputs = clone_inputs(inputs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/torchbench.py", line 335, in <graph break in forward_and_backward_pass>
pred = mod(*cloned_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/nn/parallel/distributed.py", line 1093, in forward
output = self._run_ddp_forward(*inputs, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/nn/parallel/distributed.py", line 1047, in _run_ddp_forward
return module_to_run(*inputs[0], **kwargs[0])
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/data/users/ezyang/benchmark/torchbenchmark/models/moco/moco/builder.py", line 130, in forward
self._momentum_update_key_encoder() # update the key encoder
File "/data/users/ezyang/benchmark/torchbenchmark/models/moco/moco/builder.py", line 133, in <graph break in forward>
im_k, idx_unshuffle = self._batch_shuffle_ddp(im_k)
File "/data/users/ezyang/pytorch-tmp/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "/data/users/ezyang/benchmark/torchbenchmark/models/moco/moco/builder.py", line 76, in _batch_shuffle_ddp
x_gather = concat_all_gather(x)
File "/data/users/ezyang/pytorch-tmp/torch/autograd/grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "/data/users/ezyang/benchmark/torchbenchmark/models/moco/moco/builder.py", line 164, in concat_all_gather
@torch.no_grad()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/functorch/_src/aot_autograd.py", line 893, in forward
return compiled_f(
File "/data/users/ezyang/pytorch-tmp/functorch/_src/aot_autograd.py", line 880, in new_func
compiled_fn = create_aot_dispatcher_function(
File "/data/users/ezyang/pytorch-tmp/functorch/_src/aot_autograd.py", line 603, in create_aot_dispatcher_function
return aot_dispatch_base(flat_fn, fake_flat_tensor_args, aot_config)
File "/data/users/ezyang/pytorch-tmp/functorch/_src/aot_autograd.py", line 326, in aot_dispatch_base
fw_module = make_fx(flat_fn, aot_config.decompositions)(*flat_args)
File "/data/users/ezyang/pytorch-tmp/torch/fx/experimental/proxy_tensor.py", line 671, in wrapped
t = dispatch_trace(wrap_key(func, args, fx_tracer), tracer=fx_tracer, concrete_args=tuple(phs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/fx/experimental/proxy_tensor.py", line 422, in dispatch_trace
graph = tracer.trace(root, concrete_args)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/fx/_symbolic_trace.py", line 739, in trace
(self.create_arg(fn(*args)),),
File "/data/users/ezyang/pytorch-tmp/torch/fx/experimental/proxy_tensor.py", line 412, in create_arg
return super().create_arg(a)
File "/data/users/ezyang/pytorch-tmp/torch/fx/_symbolic_trace.py", line 344, in create_arg
return super().create_arg(a)
File "/data/users/ezyang/pytorch-tmp/torch/fx/proxy.py", line 140, in create_arg
return type(a)(self.create_arg(elem) for elem in a)
File "/data/users/ezyang/pytorch-tmp/torch/fx/proxy.py", line 140, in <genexpr>
return type(a)(self.create_arg(elem) for elem in a)
File "/data/users/ezyang/pytorch-tmp/torch/fx/experimental/proxy_tensor.py", line 412, in create_arg
return super().create_arg(a)
File "/data/users/ezyang/pytorch-tmp/torch/fx/_symbolic_trace.py", line 344, in create_arg
return super().create_arg(a)
File "/data/users/ezyang/pytorch-tmp/torch/fx/proxy.py", line 165, in create_arg
raise NotImplementedError(f"argument of type: {type(a)}")
NotImplementedError: argument of type: <class 'range_iterator'>
incomplete graph:
class <lambda>(torch.nn.Module):
def forward(self):
pass
TorchDynamo optimized model failed to run because of following error
cuda train moco FAIL
Running nvidia_deeprecommender...
cuda train nvidia_deeprecommender PASS
Running pytorch_CycleGAN_and_pix2pix...
--dataroot /data/users/ezyang/benchmark/torchbenchmark/data/.data/pytorch_CycleGAN_and_pix2pix_inputs/datasets/horse2zebra --name horse2zebra --model cycle_gan --display_id 0 --n_epochs 3 --n_epochs_decay 3 --gpu_ids 0 --checkpoints_dir /data/users/ezyang/benchmark/torchbenchmark/models/pytorch_CycleGAN_and_pix2pix/.data/checkpoints
cuda train pytorch_CycleGAN_and_pix2pix PASS
Running pytorch_stargan...
cuda train pytorch_stargan PASS
Running pytorch_struct...
cuda train pytorch_struct PASS
Running pytorch_unet...
cuda train pytorch_unet PASS
Running resnet18...
cuda train resnet18 PASS
Running resnet50...
cuda train resnet50 PASS
Running resnet50_quantized_qat...
WARNING:common:fp64 golden ref were not generated for resnet50_quantized_qat
cuda train resnet50_quantized_qat PASS
Running resnext50_32x4d...
cuda train resnext50_32x4d PASS
Running shufflenet_v2_x1_0...
cuda train shufflenet_v2_x1_0 PASS
/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/gym/core.py:317: DeprecationWarning: WARN: Initializing wrapper in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.
deprecation(
/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/gym/wrappers/step_api_compatibility.py:39: DeprecationWarning: WARN: Initializing environment in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.
deprecation(
/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/gym/core.py:256: DeprecationWarning: WARN: Function `env.seed(seed)` is marked as deprecated and will be removed in the future. Please use `env.reset(seed=seed)` instead.
deprecation(
Running soft_actor_critic...
cuda train soft_actor_critic PASS
Running speech_transformer...
ERROR:common:Failed running <class 'range'>(*(s0,), **{}):
'SymInt' object cannot be interpreted as an integer
(scroll up for backtrace)
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 53, in _run_node
return node.target(*args, **kwargs)
TypeError: 'SymInt' object cannot be interpreted as an integer
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1041, in check_accuracy
new_result = optimized_model_iter_fn(model, example_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 945, in run_n_iterations
self.model_iter_fn(mod, inputs, collect_outputs=False)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/torchbench.py", line 332, in forward_and_backward_pass
cloned_inputs = clone_inputs(inputs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/torchbench.py", line 335, in <graph break in forward_and_backward_pass>
pred = mod(*cloned_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 240, in catch_errors
return callback(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 437, in _convert_frame
result = inner_convert(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 112, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 87, in time_wrapper
r = func(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 319, in _convert_frame_assert
return _compile(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 374, in _compile
out_code = transform_code_object(code, transform)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/bytecode_transformation.py", line 341, in transform_code_object
transformations(instructions, code_options)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 362, in transform
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1466, in run
super().run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/nn_module.py", line 221, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 819, in CALL_FUNCTION_KW
self.call_function(fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 191, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 62, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/builtin.py", line 369, in call_function
return DynamicShapeVariable.create(tx, proxy, None, **options)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 634, in create
dyn_shape = _get_fake_value(proxy.node, tx)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 131, in _get_fake_value
return wrap_fake_exception(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 709, in wrap_fake_exception
return fn()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 132, in <lambda>
lambda: _run_node(tx.output, node, args, kwargs, nnmodule)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 62, in _run_node
raise RuntimeError(
RuntimeError: Failed running <class 'range'>(*(s0,), **{}):
'SymInt' object cannot be interpreted as an integer
(scroll up for backtrace)
TorchDynamo optimized model failed to run because of following error
cuda train speech_transformer FAIL
Running squeezenet1_1...
cuda train squeezenet1_1 PASS
Running tacotron2...
[2022-10-28 21:52:07,867] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
ERROR:common:Failed running view(*(FakeTensor(FakeTensor(..., device='meta', size=(s0, s4, s1)), cuda:0), s0, s4, -1), **{}):
view(): argument 'size' must be tuple of ints, but found element of type SymFloat at pos 2
(scroll up for backtrace)
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 55, in _run_node
return getattr(args[0], node.target)(*args[1:], **kwargs)
TypeError: view(): argument 'size' must be tuple of ints, but found element of type SymFloat at pos 2
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1041, in check_accuracy
new_result = optimized_model_iter_fn(model, example_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 945, in run_n_iterations
self.model_iter_fn(mod, inputs, collect_outputs=False)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/torchbench.py", line 332, in forward_and_backward_pass
cloned_inputs = clone_inputs(inputs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/torchbench.py", line 335, in <graph break in forward_and_backward_pass>
pred = mod(*cloned_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/data/users/ezyang/benchmark/torchbenchmark/models/tacotron2/model.py", line 505, in forward
encoder_outputs = self.encoder(embedded_inputs, text_lengths)
File "/data/users/ezyang/benchmark/torchbenchmark/models/tacotron2/model.py", line 507, in <graph break in forward>
mel_outputs, gate_outputs, alignments = self.decoder(
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/data/users/ezyang/benchmark/torchbenchmark/models/tacotron2/model.py", line 396, in forward
decoder_input = self.get_go_frame(memory).unsqueeze(0)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 240, in catch_errors
return callback(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 437, in _convert_frame
result = inner_convert(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 112, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 87, in time_wrapper
r = func(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 319, in _convert_frame_assert
return _compile(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 374, in _compile
out_code = transform_code_object(code, transform)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/bytecode_transformation.py", line 341, in transform_code_object
transformations(instructions, code_options)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 362, in transform
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1466, in run
super().run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 221, in call_function
return super().call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 191, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 62, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/misc.py", line 571, in call_function
return self.obj.call_method(tx, self.name, args, kwargs).add_options(self)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 612, in call_method
return self.__class__.create(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 200, in create
example_value = _get_fake_value(proxy.node, tx)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 131, in _get_fake_value
return wrap_fake_exception(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 709, in wrap_fake_exception
return fn()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 132, in <lambda>
lambda: _run_node(tx.output, node, args, kwargs, nnmodule)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 62, in _run_node
raise RuntimeError(
RuntimeError: Failed running view(*(FakeTensor(FakeTensor(..., device='meta', size=(s0, s4, s1)), cuda:0), s0, s4, -1), **{}):
view(): argument 'size' must be tuple of ints, but found element of type SymFloat at pos 2
(scroll up for backtrace)
TorchDynamo optimized model failed to run because of following error
cuda train tacotron2 FAIL
Running timm_efficientdet...
ERROR:common:Failed running <built-in method clamp of type object at 0x7f5722bf8b20>(*(s1 - s2 + 2*ceiling(s2/2) - 2,), **{'min': 0}):
clamp() received an invalid combination of arguments - got (SymInt, min=int), but expected one of:
* (Tensor input, Tensor min, Tensor max, *, Tensor out)
* (Tensor input, Number min, Number max, *, Tensor out)
(scroll up for backtrace)
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 53, in _run_node
return node.target(*args, **kwargs)
TypeError: clamp() received an invalid combination of arguments - got (SymInt, min=int), but expected one of:
* (Tensor input, Tensor min, Tensor max, *, Tensor out)
* (Tensor input, Number min, Number max, *, Tensor out)
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1041, in check_accuracy
new_result = optimized_model_iter_fn(model, example_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 945, in run_n_iterations
self.model_iter_fn(mod, inputs, collect_outputs=False)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/torchbench.py", line 332, in forward_and_backward_pass
cloned_inputs = clone_inputs(inputs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/torchbench.py", line 335, in <graph break in forward_and_backward_pass>
pred = mod(*cloned_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 240, in catch_errors
return callback(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 437, in _convert_frame
result = inner_convert(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 112, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 87, in time_wrapper
r = func(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 319, in _convert_frame_assert
return _compile(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 374, in _compile
out_code = transform_code_object(code, transform)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/bytecode_transformation.py", line 341, in transform_code_object
transformations(instructions, code_options)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 362, in transform
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1466, in run
super().run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/nn_module.py", line 221, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/nn_module.py", line 221, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/nn_module.py", line 221, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 191, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 62, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 191, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 62, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 191, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 62, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/builtin.py", line 344, in call_function
result = handler(tx, *args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/builtin.py", line 398, in _call_min_max
result = variables.TorchVariable(torch.clamp).call_function(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/torch.py", line 404, in call_function
tensor_variable = TensorVariable.create(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 200, in create
example_value = _get_fake_value(proxy.node, tx)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 131, in _get_fake_value
return wrap_fake_exception(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 709, in wrap_fake_exception
return fn()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 132, in <lambda>
lambda: _run_node(tx.output, node, args, kwargs, nnmodule)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 62, in _run_node
raise RuntimeError(
RuntimeError: Failed running <built-in method clamp of type object at 0x7f5722bf8b20>(*(s1 - s2 + 2*ceiling(s2/2) - 2,), **{'min': 0}):
clamp() received an invalid combination of arguments - got (SymInt, min=int), but expected one of:
* (Tensor input, Tensor min, Tensor max, *, Tensor out)
* (Tensor input, Number min, Number max, *, Tensor out)
(scroll up for backtrace)
TorchDynamo optimized model failed to run because of following error
cuda train timm_efficientdet FAIL
Running timm_efficientnet...
cuda train timm_efficientnet PASS
Running timm_regnet...
cuda train timm_regnet PASS
Running timm_resnest...
cuda train timm_resnest PASS
Running timm_vision_transformer...
cuda train timm_vision_transformer PASS
Running timm_vision_transformer_large...
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/torchbench.py", line 349, in <module>
main(TorchBenchmarkRunner(), original_dir)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1775, in main
runner.run_one_model(
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 768, in inner
return fn(self, *args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1213, in run_one_model
status = self.check_accuracy(
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1023, in check_accuracy
correct_rerun_result = self.run_n_iterations(
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 945, in run_n_iterations
self.model_iter_fn(mod, inputs, collect_outputs=False)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/torchbench.py", line 337, in forward_and_backward_pass
self.grad_scaler.scale(loss).backward()
File "/data/users/ezyang/pytorch-tmp/torch/_tensor.py", line 488, in backward
torch.autograd.backward(
File "/data/users/ezyang/pytorch-tmp/torch/autograd/__init__.py", line 197, in backward
Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 34.00 MiB (GPU 0; 39.59 GiB total capacity; 37.32 GiB already allocated; 21.44 MiB free; 38.39 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
cuda train timm_vision_transformer_large FAIL
Running timm_vovnet...
cuda train timm_vovnet PASS
Running tts_angular...
ERROR:common:Failed running self_lstm(*(FakeTensor(FakeTensor(..., device='meta', size=(s0, s1, 40)), cuda:0),), **{}):
Cannot call sizes() on tensor with symbolic sizes/strides
(scroll up for backtrace)
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 58, in _run_node
return nnmodule(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/rnn.py", line 776, in forward
result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,
RuntimeError: Cannot call sizes() on tensor with symbolic sizes/strides
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1041, in check_accuracy
new_result = optimized_model_iter_fn(model, example_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 945, in run_n_iterations
self.model_iter_fn(mod, inputs, collect_outputs=False)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/torchbench.py", line 332, in forward_and_backward_pass
cloned_inputs = clone_inputs(inputs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/torchbench.py", line 335, in <graph break in forward_and_backward_pass>
pred = mod(*cloned_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/data/users/ezyang/benchmark/torchbenchmark/models/tts_angular/model.py", line 59, in forward
d = self.layers(x)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/container.py", line 204, in forward
input = module(input)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/data/users/ezyang/benchmark/torchbenchmark/models/tts_angular/model.py", line 17, in forward
self.lstm.flatten_parameters()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 240, in catch_errors
return callback(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 437, in _convert_frame
result = inner_convert(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 112, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 87, in time_wrapper
r = func(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 319, in _convert_frame_assert
return _compile(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 374, in _compile
out_code = transform_code_object(code, transform)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/bytecode_transformation.py", line 341, in transform_code_object
transformations(instructions, code_options)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 362, in transform
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1466, in run
super().run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/nn_module.py", line 201, in call_function
return variables.TensorVariable.create(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 200, in create
example_value = _get_fake_value(proxy.node, tx)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 131, in _get_fake_value
return wrap_fake_exception(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 709, in wrap_fake_exception
return fn()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 132, in <lambda>
lambda: _run_node(tx.output, node, args, kwargs, nnmodule)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 62, in _run_node
raise RuntimeError(
RuntimeError: Failed running self_lstm(*(FakeTensor(FakeTensor(..., device='meta', size=(s0, s1, 40)), cuda:0),), **{}):
Cannot call sizes() on tensor with symbolic sizes/strides
(scroll up for backtrace)
TorchDynamo optimized model failed to run because of following error
cuda train tts_angular FAIL
Running vgg16...
cuda train vgg16 PASS
Running vision_maskrcnn...
ERROR:common:Failed running <function interpolate at 0x7f2bb50c68b0>(*(FakeTensor(FakeTensor(..., device='meta', size=(1, s0, s1, s2)), cuda:0),), **{'size': None, 'scale_factor': 1.873536229133606, 'mode': 'bilinear', 'recompute_scale_factor': True, 'align_corners': False}):
sym_int NYI
(scroll up for backtrace)
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 53, in _run_node
return node.target(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/nn/functional.py", line 3954, in interpolate
return torch._C._nn.upsample_bilinear2d(input, output_size, align_corners, scale_factors)
File "/data/users/ezyang/pytorch-tmp/torch/_subclasses/fake_tensor.py", line 785, in __torch_dispatch__
return decomposition_table[func](*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_decomp/decompositions.py", line 68, in inner
r = f(*tree_map(increase_prec, args), **tree_map(increase_prec, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_decomp/decompositions.py", line 1901, in upsample_bilinear2d_vec
i = torch.arange(sym_int(out_h), dtype=input.dtype, device=input.device)
File "/data/users/ezyang/pytorch-tmp/torch/fx/experimental/symbolic_shapes.py", line 116, in sym_int
return a.__sym_int__()
File "/data/users/ezyang/pytorch-tmp/torch/__init__.py", line 251, in __sym_int__
return SymInt(self.node.sym_int())
File "/data/users/ezyang/pytorch-tmp/torch/fx/experimental/symbolic_shapes.py", line 205, in sym_int
raise NotImplementedError("sym_int NYI")
NotImplementedError: sym_int NYI
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1041, in check_accuracy
new_result = optimized_model_iter_fn(model, example_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 945, in run_n_iterations
self.model_iter_fn(mod, inputs, collect_outputs=False)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/torchbench.py", line 332, in forward_and_backward_pass
cloned_inputs = clone_inputs(inputs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/torchbench.py", line 335, in <graph break in forward_and_backward_pass>
pred = mod(*cloned_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/data/users/ezyang/vision/torchvision/models/detection/generalized_rcnn.py", line 83, in forward
images, targets = self.transform(images, targets)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/data/users/ezyang/vision/torchvision/models/detection/transform.py", line 130, in forward
image, target_index = self.resize(image, target_index)
File "/data/users/ezyang/vision/torchvision/models/detection/transform.py", line 181, in resize
image, target = _resize_image_and_masks(image, size, float(self.max_size), target, self.fixed_size)
File "/data/users/ezyang/vision/torchvision/models/detection/transform.py", line 50, in _resize_image_and_masks
scale_factor = scale.item()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 240, in catch_errors
return callback(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 437, in _convert_frame
result = inner_convert(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 112, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 87, in time_wrapper
r = func(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 319, in _convert_frame_assert
return _compile(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 374, in _compile
out_code = transform_code_object(code, transform)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/bytecode_transformation.py", line 341, in transform_code_object
transformations(instructions, code_options)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 362, in transform
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1466, in run
super().run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 819, in CALL_FUNCTION_KW
self.call_function(fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/torch.py", line 404, in call_function
tensor_variable = TensorVariable.create(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 200, in create
example_value = _get_fake_value(proxy.node, tx)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 131, in _get_fake_value
return wrap_fake_exception(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 709, in wrap_fake_exception
return fn()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 132, in <lambda>
lambda: _run_node(tx.output, node, args, kwargs, nnmodule)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 62, in _run_node
raise RuntimeError(
RuntimeError: Failed running <function interpolate at 0x7f2bb50c68b0>(*(FakeTensor(FakeTensor(..., device='meta', size=(1, s0, s1, s2)), cuda:0),), **{'size': None, 'scale_factor': 1.873536229133606, 'mode': 'bilinear', 'recompute_scale_factor': True, 'align_corners': False}):
sym_int NYI
(scroll up for backtrace)
TorchDynamo optimized model failed to run because of following error
cuda train vision_maskrcnn FAIL
Running yolov3...
cuda train yolov3 PASS
Running AlbertForMaskedLM...
cuda train AlbertForMaskedLM PASS
Running AlbertForQuestionAnswering...
cuda train AlbertForQuestionAnswering PASS
Running AllenaiLongformerBase...
[2022-10-28 22:03:26,958] torch._dynamo.variables.builtin: [WARNING] incorrect arg count <bound method BuiltinVariable._call_min_max of BuiltinVariable(max)> missing a required argument: 'b'
[2022-10-28 22:03:26,987] torch._dynamo.variables.builtin: [WARNING] incorrect arg count <bound method BuiltinVariable._call_min_max of BuiltinVariable(max)> missing a required argument: 'b'
[2022-10-28 22:03:27,046] torch._dynamo.variables.builtin: [WARNING] incorrect arg count <bound method BuiltinVariable._call_min_max of BuiltinVariable(max)> missing a required argument: 'b'
[2022-10-28 22:03:31,182] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
ERROR:common:Output 2 of CompiledFunctionBackward is a view and is being modified inplace. This view is the output of a function that returns multiple views. Such functions do not allow the output views to be modified inplace. You should replace the inplace operation by an out-of-place one.
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1041, in check_accuracy
new_result = optimized_model_iter_fn(model, example_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 945, in run_n_iterations
self.model_iter_fn(mod, inputs, collect_outputs=False)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/huggingface.py", line 438, in forward_and_backward_pass
cloned_inputs = clone_inputs(inputs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/huggingface.py", line 441, in <graph break in forward_and_backward_pass>
pred = mod(**cloned_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/transformers/models/longformer/modeling_longformer.py", line 1813, in forward
outputs = self.longformer(
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/transformers/models/longformer/modeling_longformer.py", line 1696, in forward
padding_len, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds = self._pad_to_window_size(
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/transformers/models/longformer/modeling_longformer.py", line 1715, in <graph break in forward>
encoder_outputs = self.encoder(
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/transformers/models/longformer/modeling_longformer.py", line 1265, in forward
is_global_attn = is_index_global_attn.flatten().any().item()
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/transformers/models/longformer/modeling_longformer.py", line 1297, in <graph break in forward>
layer_outputs = layer_module(
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/transformers/models/longformer/modeling_longformer.py", line 1221, in forward
self_attn_outputs = self.attention(
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/transformers/models/longformer/modeling_longformer.py", line 1157, in forward
self_outputs = self.self(
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/transformers/models/longformer/modeling_longformer.py", line 542, in forward
def forward(
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/transformers/models/longformer/modeling_longformer.py", line 570, in <graph break in forward>
assert (
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 137, in __call__
return self.forward(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 134, in forward
return optimized_forward(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "<eval_with_key>.27", line 5, in forward
query_vectors /= 8.0; itruediv = query_vectors; query_vectors = None
RuntimeError: Output 2 of CompiledFunctionBackward is a view and is being modified inplace. This view is the output of a function that returns multiple views. Such functions do not allow the output views to be modified inplace. You should replace the inplace operation by an out-of-place one.
TorchDynamo optimized model failed to run because of following error
cuda train AllenaiLongformerBase FAIL
Running BartForCausalLM...
cuda train BartForCausalLM PASS
Running BartForConditionalGeneration...
cuda train BartForConditionalGeneration PASS
Running BertForMaskedLM...
cuda train BertForMaskedLM PASS
Running BertForQuestionAnswering...
cuda train BertForQuestionAnswering PASS
Running BigBird...
[2022-10-28 22:08:23,658] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
[2022-10-28 22:08:40,872] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
[2022-10-28 22:08:55,906] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
[2022-10-28 22:09:11,017] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
[2022-10-28 22:09:26,310] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
[2022-10-28 22:09:41,361] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
[2022-10-28 22:09:56,962] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
[2022-10-28 22:10:12,006] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
[2022-10-28 22:10:27,334] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
[2022-10-28 22:10:43,706] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
[2022-10-28 22:11:00,024] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
[2022-10-28 22:11:16,335] torch._dynamo.optimizations.training: [WARNING] Unable to use Aot Autograd because of presence of mutation
cuda train BigBird FAIL (TIMEOUT)
Running BlenderbotSmallForCausalLM...
cuda train BlenderbotSmallForCausalLM PASS
Running BlenderbotSmallForConditionalGeneration...
cuda train BlenderbotSmallForConditionalGeneration PASS
Running CamemBert...
cuda train CamemBert PASS
Running DebertaForMaskedLM...
cuda train DebertaForMaskedLM PASS
Running DebertaForQuestionAnswering...
[2022-10-28 22:20:47,771] torch._dynamo.utils: [ERROR] RMSE (res-fp64): 0.00017, (ref-fp64): 0.00000 and shape=torch.Size([50265, 768])
[2022-10-28 22:20:47,771] torch._dynamo.utils: [ERROR] Accuracy failed for key name deberta.embeddings.word_embeddings.weight.grad
cuda train DebertaForQuestionAnswering FAIL
WARNING:__main__:Sequence Length not defined for DistilBertForMaskedLM. Choosing 128 arbitrarily
Running DistilBertForMaskedLM...
cuda train DistilBertForMaskedLM PASS
WARNING:__main__:Sequence Length not defined for DistilBertForQuestionAnswering. Choosing 128 arbitrarily
Running DistilBertForQuestionAnswering...
cuda train DistilBertForQuestionAnswering PASS
Running DistillGPT2...
[2022-10-28 22:22:29,819] torch._dynamo.utils: [ERROR] RMSE (res-fp64): 0.00027, (ref-fp64): 0.00000 and shape=torch.Size([50257, 768])
[2022-10-28 22:22:29,819] torch._dynamo.utils: [ERROR] Accuracy failed for key name transformer.wte.weight.grad
cuda train DistillGPT2 FAIL
If you want to use `ElectraForCausalLM` as a standalone, add `is_decoder=True.`
Running ElectraForCausalLM...
cuda train ElectraForCausalLM PASS
Running ElectraForQuestionAnswering...
cuda train ElectraForQuestionAnswering PASS
Running GPT2ForSequenceClassification...
[2022-10-28 22:25:09,615] torch._dynamo.utils: [ERROR] RMSE (res-fp64): 0.00157, (ref-fp64): 0.00000 and shape=torch.Size([50257, 768])
[2022-10-28 22:25:09,615] torch._dynamo.utils: [ERROR] Accuracy failed for key name transformer.wte.weight.grad
cuda train GPT2ForSequenceClassification FAIL
Running GoogleFnet...
ERROR:common:aten.view_as_real.default - couldn't find symbolic meta function/decomposition
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1041, in check_accuracy
new_result = optimized_model_iter_fn(model, example_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 945, in run_n_iterations
self.model_iter_fn(mod, inputs, collect_outputs=False)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/huggingface.py", line 438, in forward_and_backward_pass
cloned_inputs = clone_inputs(inputs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/huggingface.py", line 441, in <graph break in forward_and_backward_pass>
pred = mod(**cloned_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/transformers/models/fnet/modeling_fnet.py", line 763, in forward
outputs = self.fnet(
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/transformers/models/fnet/modeling_fnet.py", line 604, in forward
encoder_outputs = self.encoder(
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/transformers/models/fnet/modeling_fnet.py", line 308, in forward
layer_outputs = layer_module(hidden_states)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/transformers/models/fnet/modeling_fnet.py", line 267, in forward
self_fourier_outputs = self.fourier(hidden_states)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/transformers/models/fnet/modeling_fnet.py", line 220, in forward
self_outputs = self.self(hidden_states)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 240, in catch_errors
return callback(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 437, in _convert_frame
result = inner_convert(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 112, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 87, in time_wrapper
r = func(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 319, in _convert_frame_assert
return _compile(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 374, in _compile
out_code = transform_code_object(code, transform)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/bytecode_transformation.py", line 341, in transform_code_object
transformations(instructions, code_options)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 351, in transform
tracer = InstructionTranslator(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1418, in __init__
self.symbolic_locals = collections.OrderedDict(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1419, in <genexpr>
(k, VariableBuilder(self, LocalSource(k))(f_locals[k]))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/builder.py", line 129, in __call__
return self._wrap(value).clone(**self.options())
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/builder.py", line 203, in _wrap
output = [
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/builder.py", line 204, in <listcomp>
VariableBuilder(self.tx, GetItemSource(self.get_source(), i))(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/builder.py", line 129, in __call__
return self._wrap(value).clone(**self.options())
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/builder.py", line 193, in _wrap
return self.wrap_tensor(value)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/builder.py", line 539, in wrap_tensor
tensor_variable = TensorVariable.create(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 210, in create
example_value = fake_wrapper(example_value)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 725, in wrap_to_fake_tensor_and_record
return wrap_fake_exception(lambda: make_fake_tensor(e, tx.fake_mode, tx))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 709, in wrap_fake_exception
return fn()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 725, in <lambda>
return wrap_fake_exception(lambda: make_fake_tensor(e, tx.fake_mode, tx))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 671, in make_fake_tensor
fake_tensor = fake_mode.from_tensor(
File "/data/users/ezyang/pytorch-tmp/torch/_subclasses/fake_tensor.py", line 931, in from_tensor
return self.fake_tensor_converter(self, tensor, shape_env=self.shape_env)
File "/data/users/ezyang/pytorch-tmp/torch/_subclasses/fake_tensor.py", line 262, in __call__
return self.from_real_tensor(fake_mode, t, make_constant, shape_env=shape_env)
File "/data/users/ezyang/pytorch-tmp/torch/_subclasses/fake_tensor.py", line 234, in from_real_tensor
out = self.meta_converter(
File "/data/users/ezyang/pytorch-tmp/torch/_subclasses/meta_utils.py", line 369, in __call__
r = self.meta_tensor(t, shape_env=shape_env, callback=callback)
File "/data/users/ezyang/pytorch-tmp/torch/_subclasses/meta_utils.py", line 226, in meta_tensor
base = torch.view_as_real(base)
File "/data/users/ezyang/pytorch-tmp/torch/_subclasses/fake_tensor.py", line 541, in __torch_dispatch__
return func(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_ops.py", line 257, in __call__
return self._op(*args, **kwargs or {})
File "/data/users/ezyang/pytorch-tmp/torch/_subclasses/fake_tensor.py", line 806, in __torch_dispatch__
raise RuntimeError(
RuntimeError: aten.view_as_real.default - couldn't find symbolic meta function/decomposition
TorchDynamo optimized model failed to run because of following error
cuda train GoogleFnet FAIL
Running LayoutLMForMaskedLM...
cuda train LayoutLMForMaskedLM PASS
Running LayoutLMForSequenceClassification...
cuda train LayoutLMForSequenceClassification PASS
WARNING:__main__:Sequence Length not defined for M2M100ForConditionalGeneration. Choosing 128 arbitrarily
Running M2M100ForConditionalGeneration...
cuda train M2M100ForConditionalGeneration PASS
WARNING:__main__:Sequence Length not defined for MBartForCausalLM. Choosing 128 arbitrarily
Running MBartForCausalLM...
cuda train MBartForCausalLM PASS
WARNING:__main__:Sequence Length not defined for MBartForConditionalGeneration. Choosing 128 arbitrarily
Running MBartForConditionalGeneration...
cuda train MBartForConditionalGeneration PASS
WARNING:__main__:Sequence Length not defined for MT5ForConditionalGeneration. Choosing 128 arbitrarily
Running MT5ForConditionalGeneration...
WARNING:common:fp64 golden ref were not generated for MT5ForConditionalGeneration
cuda train MT5ForConditionalGeneration PASS
If you want to use `MegatronBertForCausalLM` as a standalone, add `is_decoder=True.`
WARNING:__main__:Sequence Length not defined for MegatronBertForCausalLM. Choosing 128 arbitrarily
Running MegatronBertForCausalLM...
cuda train MegatronBertForCausalLM PASS
WARNING:__main__:Sequence Length not defined for MegatronBertForQuestionAnswering. Choosing 128 arbitrarily
Running MegatronBertForQuestionAnswering...
cuda train MegatronBertForQuestionAnswering PASS
WARNING:__main__:Sequence Length not defined for MobileBertForMaskedLM. Choosing 128 arbitrarily
Running MobileBertForMaskedLM...
cuda train MobileBertForMaskedLM PASS
WARNING:__main__:Sequence Length not defined for MobileBertForQuestionAnswering. Choosing 128 arbitrarily
Running MobileBertForQuestionAnswering...
cuda train MobileBertForQuestionAnswering PASS
WARNING:__main__:Sequence Length not defined for OPTForCausalLM. Choosing 128 arbitrarily
Running OPTForCausalLM...
[2022-10-28 22:43:48,986] torch._dynamo.utils: [ERROR] RMSE (res-fp64): 0.00047, (ref-fp64): 0.00001 and shape=torch.Size([50272, 768])
[2022-10-28 22:43:48,987] torch._dynamo.utils: [ERROR] Accuracy failed for key name model.decoder.embed_tokens.weight.grad
cuda train OPTForCausalLM FAIL
WARNING:__main__:Sequence Length not defined for PLBartForCausalLM. Choosing 128 arbitrarily
Running PLBartForCausalLM...
cuda train PLBartForCausalLM PASS
WARNING:__main__:Sequence Length not defined for PLBartForConditionalGeneration. Choosing 128 arbitrarily
Running PLBartForConditionalGeneration...
cuda train PLBartForConditionalGeneration PASS
WARNING:__main__:Sequence Length not defined for PegasusForCausalLM. Choosing 128 arbitrarily
Running PegasusForCausalLM...
cuda train PegasusForCausalLM PASS
WARNING:__main__:Sequence Length not defined for PegasusForConditionalGeneration. Choosing 128 arbitrarily
Running PegasusForConditionalGeneration...
cuda train PegasusForConditionalGeneration PASS
If you want to use `RobertaLMHeadModel` as a standalone, add `is_decoder=True.`
Running RobertaForCausalLM...
cuda train RobertaForCausalLM PASS
Running RobertaForQuestionAnswering...
cuda train RobertaForQuestionAnswering PASS
WARNING:__main__:Sequence Length not defined for Speech2Text2ForCausalLM. Choosing 128 arbitrarily
Running Speech2Text2ForCausalLM...
cuda train Speech2Text2ForCausalLM PASS
Running T5ForConditionalGeneration...
WARNING:common:fp64 golden ref were not generated for T5ForConditionalGeneration
cuda train T5ForConditionalGeneration PASS
Running T5Small...
WARNING:common:fp64 golden ref were not generated for T5Small
cuda train T5Small PASS
WARNING:__main__:Sequence Length not defined for TrOCRForCausalLM. Choosing 128 arbitrarily
Running TrOCRForCausalLM...
cuda train TrOCRForCausalLM PASS
WARNING:__main__:Sequence Length not defined for XGLMForCausalLM. Choosing 128 arbitrarily
Running XGLMForCausalLM...
cuda train XGLMForCausalLM PASS
Running XLNetLMHeadModel...
cuda train XLNetLMHeadModel PASS
Running YituTechConvBert...
cuda train YituTechConvBert PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running adv_inception_v3...
cuda train adv_inception_v3 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running beit_base_patch16_224...
cuda train beit_base_patch16_224 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running botnet26t_256...
[2022-10-28 23:07:57,274] torch._dynamo.utils: [ERROR] RMSE (res-fp64): 0.00340, (ref-fp64): 0.00019 and shape=torch.Size([24, 3, 3, 3])
[2022-10-28 23:07:57,274] torch._dynamo.utils: [ERROR] Accuracy failed for key name stem.conv1.conv.weight.grad
cuda train botnet26t_256 FAIL
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running cait_m36_384...
ERROR:common:view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1041, in check_accuracy
new_result = optimized_model_iter_fn(model, example_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 945, in run_n_iterations
self.model_iter_fn(mod, inputs, collect_outputs=False)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/timm_models.py", line 317, in forward_and_backward_pass
cloned_inputs = clone_inputs(inputs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/timm_models.py", line 324, in <graph break in forward_and_backward_pass>
self.grad_scaler.scale(loss).backward()
File "/data/users/ezyang/pytorch-tmp/torch/_tensor.py", line 488, in backward
torch.autograd.backward(
File "/data/users/ezyang/pytorch-tmp/torch/autograd/__init__.py", line 197, in backward
Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
File "/data/users/ezyang/pytorch-tmp/torch/autograd/function.py", line 270, in apply
return user_fn(self, *args)
File "/data/users/ezyang/pytorch-tmp/functorch/_src/aot_autograd.py", line 503, in backward
out = call_func_with_args(
File "/data/users/ezyang/pytorch-tmp/functorch/_src/aot_autograd.py", line 296, in call_func_with_args
out = normalize_as_list(f(args))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/functorch/_src/aot_autograd.py", line 271, in g
return f(*args)
File "/data/users/ezyang/pytorch-tmp/torch/fx/graph_module.py", line 660, in call_wrapped
return self._wrapped_call(self, *args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/fx/graph_module.py", line 279, in __call__
raise e
File "/data/users/ezyang/pytorch-tmp/torch/fx/graph_module.py", line 269, in __call__
return super(self.cls, obj).__call__(*args, **kwargs) # type: ignore[misc]
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "<eval_with_key>.6", line 345, in forward
view_795 = torch.ops.aten.view.default(permute_194, [sym_size_250, 16]); permute_194 = sym_size_250 = None
File "/data/users/ezyang/pytorch-tmp/torch/_ops.py", line 257, in __call__
return self._op(*args, **kwargs or {})
RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.
TorchDynamo optimized model failed to run because of following error
cuda train cait_m36_384 FAIL
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running coat_lite_mini...
ERROR:common:'example_value'
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1041, in check_accuracy
new_result = optimized_model_iter_fn(model, example_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 945, in run_n_iterations
self.model_iter_fn(mod, inputs, collect_outputs=False)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/timm_models.py", line 317, in forward_and_backward_pass
cloned_inputs = clone_inputs(inputs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/timm_models.py", line 320, in <graph break in forward_and_backward_pass>
pred = mod(*cloned_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/timm/models/coat.py", line 607, in forward
x_feat = self.forward_features(x)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/timm/models/coat.py", line 516, in forward_features
x1 = blk(x1, size=(H1, W1))
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/timm/models/coat.py", line 516, in <graph break in forward_features>
x1 = blk(x1, size=(H1, W1))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 240, in catch_errors
return callback(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 437, in _convert_frame
result = inner_convert(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 112, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 87, in time_wrapper
r = func(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 319, in _convert_frame_assert
return _compile(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 374, in _compile
out_code = transform_code_object(code, transform)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/bytecode_transformation.py", line 341, in transform_code_object
transformations(instructions, code_options)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 362, in transform
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1466, in run
super().run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/nn_module.py", line 221, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 835, in LOAD_ATTR
result = BuiltinVariable(getattr).call_function(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/builtin.py", line 344, in call_function
result = handler(tx, *args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/builtin.py", line 722, in call_getattr
obj.var_getattr(tx, name).clone(source=source).add_options(options)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 436, in var_getattr
result = self.call_method(tx, "size", [], {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 514, in call_method
items.append(DynamicShapeVariable.create(tx, proxy, element))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 634, in create
dyn_shape = _get_fake_value(proxy.node, tx)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 113, in _get_fake_value
args, kwargs = torch.fx.node.map_arg((node.args, node.kwargs), visit)
File "/data/users/ezyang/pytorch-tmp/torch/fx/node.py", line 601, in map_arg
return map_aggregate(a, lambda x: fn(x) if isinstance(x, Node) else x)
File "/data/users/ezyang/pytorch-tmp/torch/fx/node.py", line 609, in map_aggregate
t = tuple(map_aggregate(elem, fn) for elem in a)
File "/data/users/ezyang/pytorch-tmp/torch/fx/node.py", line 609, in <genexpr>
t = tuple(map_aggregate(elem, fn) for elem in a)
File "/data/users/ezyang/pytorch-tmp/torch/fx/node.py", line 609, in map_aggregate
t = tuple(map_aggregate(elem, fn) for elem in a)
File "/data/users/ezyang/pytorch-tmp/torch/fx/node.py", line 609, in <genexpr>
t = tuple(map_aggregate(elem, fn) for elem in a)
File "/data/users/ezyang/pytorch-tmp/torch/fx/node.py", line 619, in map_aggregate
return fn(a)
File "/data/users/ezyang/pytorch-tmp/torch/fx/node.py", line 601, in <lambda>
return map_aggregate(a, lambda x: fn(x) if isinstance(x, Node) else x)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 111, in visit
return n.meta["example_value"]
KeyError: 'example_value'
TorchDynamo optimized model failed to run because of following error
cuda train coat_lite_mini FAIL
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running convit_base...
WARNING:common:fp64 golden ref were not generated for convit_base
ERROR:common:Failed running repeat_interleave(*(FakeTensor(FakeTensor(..., device='meta', size=(14, 14), dtype=torch.int64), cpu), 14), **{'dim': 0}):
aten.repeat_interleave.Tensor
(scroll up for backtrace)
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 55, in _run_node
return getattr(args[0], node.target)(*args[1:], **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_subclasses/fake_tensor.py", line 815, in __torch_dispatch__
op_impl_out = op_impl(self, func, *args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_subclasses/fake_tensor.py", line 358, in dyn_shape
raise DynamicOutputShapeException(func)
torch._subclasses.fake_tensor.DynamicOutputShapeException: aten.repeat_interleave.Tensor
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1041, in check_accuracy
new_result = optimized_model_iter_fn(model, example_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 945, in run_n_iterations
self.model_iter_fn(mod, inputs, collect_outputs=False)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/timm_models.py", line 317, in forward_and_backward_pass
cloned_inputs = clone_inputs(inputs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/timm_models.py", line 320, in <graph break in forward_and_backward_pass>
pred = mod(*cloned_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/timm/models/convit.py", line 333, in forward
x = self.forward_features(x)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/timm/models/convit.py", line 323, in forward_features
x = blk(x)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/timm/models/convit.py", line 214, in forward
x = x + self.drop_path(self.attn(self.norm1(x)))
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/timm/models/convit.py", line 83, in forward
def forward(self, x):
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 240, in catch_errors
return callback(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 437, in _convert_frame
result = inner_convert(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 112, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 87, in time_wrapper
r = func(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 319, in _convert_frame_assert
return _compile(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 374, in _compile
out_code = transform_code_object(code, transform)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/bytecode_transformation.py", line 341, in transform_code_object
transformations(instructions, code_options)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 362, in transform
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1466, in run
super().run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 221, in call_function
return super().call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 191, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 62, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 819, in CALL_FUNCTION_KW
self.call_function(fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/misc.py", line 571, in call_function
return self.obj.call_method(tx, self.name, args, kwargs).add_options(self)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 612, in call_method
return self.__class__.create(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 200, in create
example_value = _get_fake_value(proxy.node, tx)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 131, in _get_fake_value
return wrap_fake_exception(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 709, in wrap_fake_exception
return fn()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 132, in <lambda>
lambda: _run_node(tx.output, node, args, kwargs, nnmodule)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 62, in _run_node
raise RuntimeError(
RuntimeError: Failed running repeat_interleave(*(FakeTensor(FakeTensor(..., device='meta', size=(14, 14), dtype=torch.int64), cpu), 14), **{'dim': 0}):
aten.repeat_interleave.Tensor
(scroll up for backtrace)
TorchDynamo optimized model failed to run because of following error
cuda train convit_base FAIL
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running convmixer_768_32...
cuda train convmixer_768_32 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running convnext_base...
[2022-10-28 23:14:31,742] torch._dynamo.utils: [ERROR] RMSE (res-fp64): 0.00106, (ref-fp64): 0.00000 and shape=torch.Size([128, 3, 4, 4])
[2022-10-28 23:14:31,743] torch._dynamo.utils: [ERROR] Accuracy failed for key name stem.0.weight.grad
cuda train convnext_base FAIL
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running crossvit_9_240...
cuda train crossvit_9_240 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running cspdarknet53...
Traceback (most recent call last):
File "<string>", line 2, in <lambda>
NameError: name 'torch' is not defined
NULL ERROR: /data/users/ezyang/pytorch-tmp/torch/csrc/dynamo/eval_frame.c:239
cuda train cspdarknet53 FAIL
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running deit_base_distilled_patch16_224...
cuda train deit_base_distilled_patch16_224 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running dla102...
cuda train dla102 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running dm_nfnet_f0...
ERROR:common:Failed running <built-in method clamp of type object at 0x7f1be0a73b20>(*(-s2 + 2*ceiling(s2/2) + 1,), **{'min': 0}):
clamp() received an invalid combination of arguments - got (SymInt, min=int), but expected one of:
* (Tensor input, Tensor min, Tensor max, *, Tensor out)
* (Tensor input, Number min, Number max, *, Tensor out)
(scroll up for backtrace)
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 53, in _run_node
return node.target(*args, **kwargs)
TypeError: clamp() received an invalid combination of arguments - got (SymInt, min=int), but expected one of:
* (Tensor input, Tensor min, Tensor max, *, Tensor out)
* (Tensor input, Number min, Number max, *, Tensor out)
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1041, in check_accuracy
new_result = optimized_model_iter_fn(model, example_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 945, in run_n_iterations
self.model_iter_fn(mod, inputs, collect_outputs=False)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/timm_models.py", line 317, in forward_and_backward_pass
cloned_inputs = clone_inputs(inputs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/timm_models.py", line 320, in <graph break in forward_and_backward_pass>
pred = mod(*cloned_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 240, in catch_errors
return callback(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 437, in _convert_frame
result = inner_convert(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 112, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 87, in time_wrapper
r = func(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 319, in _convert_frame_assert
return _compile(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 374, in _compile
out_code = transform_code_object(code, transform)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/bytecode_transformation.py", line 341, in transform_code_object
transformations(instructions, code_options)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 362, in transform
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1466, in run
super().run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 221, in call_function
return super().call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 191, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 62, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/nn_module.py", line 183, in call_function
tx.call_function(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/nn_module.py", line 221, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 191, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 62, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 191, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 62, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/builtin.py", line 344, in call_function
result = handler(tx, *args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/builtin.py", line 398, in _call_min_max
result = variables.TorchVariable(torch.clamp).call_function(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/torch.py", line 404, in call_function
tensor_variable = TensorVariable.create(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 200, in create
example_value = _get_fake_value(proxy.node, tx)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 131, in _get_fake_value
return wrap_fake_exception(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 709, in wrap_fake_exception
return fn()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 132, in <lambda>
lambda: _run_node(tx.output, node, args, kwargs, nnmodule)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 62, in _run_node
raise RuntimeError(
RuntimeError: Failed running <built-in method clamp of type object at 0x7f1be0a73b20>(*(-s2 + 2*ceiling(s2/2) + 1,), **{'min': 0}):
clamp() received an invalid combination of arguments - got (SymInt, min=int), but expected one of:
* (Tensor input, Tensor min, Tensor max, *, Tensor out)
* (Tensor input, Number min, Number max, *, Tensor out)
(scroll up for backtrace)
TorchDynamo optimized model failed to run because of following error
cuda train dm_nfnet_f0 FAIL
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running dpn107...
cuda train dpn107 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running eca_botnext26ts_256...
[2022-10-28 23:26:35,394] torch._dynamo.utils: [ERROR] RMSE (res-fp64): 0.00420, (ref-fp64): 0.00004 and shape=torch.Size([24, 3, 3, 3])
[2022-10-28 23:26:35,394] torch._dynamo.utils: [ERROR] Accuracy failed for key name stem.conv1.conv.weight.grad
cuda train eca_botnext26ts_256 FAIL
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running eca_halonext26ts...
Traceback (most recent call last):
File "<string>", line 2, in <lambda>
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/sympy-1.11rc1-py3.9.egg/sympy/core/cache.py", line 70, in wrapper
retval = cfunc(*args, **kwargs)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/sympy-1.11rc1-py3.9.egg/sympy/core/function.py", line 469, in __new__
result = super().__new__(cls, *args, **options)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/sympy-1.11rc1-py3.9.egg/sympy/core/cache.py", line 70, in wrapper
retval = cfunc(*args, **kwargs)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/sympy-1.11rc1-py3.9.egg/sympy/core/function.py", line 309, in __new__
evaluated = cls.eval(*args)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/sympy-1.11rc1-py3.9.egg/sympy/core/mod.py", line 102, in eval
rv = number_eval(p, q)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/sympy-1.11rc1-py3.9.egg/sympy/core/mod.py", line 49, in number_eval
raise ZeroDivisionError("Modulo by zero")
ZeroDivisionError: Modulo by zero
ERROR RUNNING GUARDS forward /home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/timm/models/byobnet.py:1558
___guarded_code.valid and
___check_obj_id(self, 139831987502000) and
self.training == False and
(isinstance(x, torch.Tensor) and Eq(x.size()[0], 2) & Eq(23, 23) & (x.size()[0] <= 65535) & (x.size()[0] <= 880801) & Eq(384/8, 48) & Eq(4*16, 64) & Eq(4*16, 64) & Eq(8*16, 128) & Eq(8*16, 128) & Eq(16*16, 256) & Eq(16*16, x.size()[3]) & Ne(384/8, 1) & Eq(x.size()[0]*(Mod(384, 8)), 0) & ((x.size()[0]*16)//2 >= 2) & Ne(x.size()[0], (x.size()[0]*16)//2) & Ne(x.size()[0]*384, 8*x.size()[0]) & Eq((x.size()[0]*16)//2, 8*x.size()[0]) & Ne(Mod(x.size()[0], (x.size()[0]*16)//2), 0) & (-x.size()[1] + 4*16 + 3 >= 0) & (-x.size()[1] + 4*16 + 3 >= 1) & (8*16 - 5 + 5 >= 0) & (8*16 - 5 + 5 >= 1) & (16*16 - 5 + 5 >= 0) & (16*16 - 5 + 5 >= 1) & (-x.size()[1] + 4*16 + 3 > 1) & (8*16 - 5 + 5 > 1) & (16*16 - 5 + 5 > 1) & (0 < -x.size()[1] + 4*16 + 3) & (0 < 8*16 - 5 + 5) & (0 < 16*16 - 5 + 5) & Ne(1, -x.size()[1] + 4*16 + 3) & Ne(1, 8*16 - 5 + 5) & Ne(1, 16*16 - 5 + 5) & Ne(-x.size()[1] + 4*16 + 3, -1) & Ne(-x.size()[1] + 4*16 + 3, 0) & Ne(8*16 - 5 + 5, -1) & Ne(8*16 - 5 + 5, 0) & Ne(16*16 - 5 + 5, -1) & Ne(16*16 - 5 + 5, 0) & Eq(4*16, -x.size()[1] + 4*16 + 3) & Eq(8*16, 8*16 - 5 + 5) & Eq(16*16, 16*16 - 5 + 5) & ((-x.size()[1] + x.size()[3] + 2)//2 + 1 >= 0) & ((-x.size()[1] + x.size()[3] + 2)//2 + 1 >= 1) & ((-x.size()[1] + x.size()[3] + 2)//2 + 1 >= 2) & ((-x.size()[1] + x.size()[3] + 2)//2 + 1 > 1) & Ne((-x.size()[1] + x.size()[3] + 2)//2 + 1, -1) & Ne((-x.size()[1] + x.size()[3] + 2)//2 + 1, 0) & Ne((-x.size()[1] + x.size()[3] + 2)//2 + 1, 1) & Ne(Mod(1, -x.size()[1] + 4*16 + 3), 0) & Ne(Mod(1, 8*16 - 5 + 5), 0) & Ne(Mod(1, 16*16 - 5 + 5), 0) & Ne(Mod((-x.size()[1] + x.size()[3] + 2)//2, 2), 0) & Ne(-4*x.size()[0]*x.size()[1] + 16*x.size()[0]*16 + 12*x.size()[0], 0) & Ne(-x.size()[0]*x.size()[1] + 4*x.size()[0]*16 + 3*x.size()[0], 0) & Ne(-x.size()[0]*x.size()[1] + 4*x.size()[0]*16 + 3*x.size()[0], x.size()[0]) & Ne(8*x.size()[0]*16 - x.size()[0]*5 + 5*x.size()[0], 0) & Ne(8*x.size()[0]*16 - x.size()[0]*5 + 5*x.size()[0], x.size()[0]) & Ne(16*x.size()[0]*16 - x.size()[0]*5 + 5*x.size()[0], 0) & Ne(16*x.size()[0]*16 - x.size()[0]*5 + 5*x.size()[0], x.size()[0]) & Ne(32*x.size()[0]*16 - 4*x.size()[0]*5 + 20*x.size()[0], 0) & Ne(64*x.size()[0]*16 - 4*x.size()[0]*5 + 20*x.size()[0], 0) & (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 7 >= 0) & (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 7 >= 1) & (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 7 >= 2) & (-x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 4 >= 0) & (-x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 4 >= 1) & (-x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 4 >= 2) & (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 7 > 1) & (-x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 4 > 1) & Ne(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 7, -1) & Ne(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 7, 0) & Ne(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 7, 1) & Ne(-x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 4, -1) & Ne(-x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 4, 0) & Ne(-x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 4, 1) & Ne(-4*x.size()[0]*x.size()[1] + 16*x.size()[0]*16 + 12*x.size()[0] + 4*x.size()[1] - 12, 0) & Ne(32*x.size()[0]*16 - 4*x.size()[0]*5 + 20*x.size()[0] + 4*5 - 20, 0) & Ne(64*x.size()[0]*16 - 4*x.size()[0]*5 + 20*x.size()[0] + 4*5 - 20, 0) & ((-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 1 >= 0) & ((-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 1 >= 1) & ((-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 1 >= 2) & ((-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 1 > 1) & Ne((-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 1, -1) & Ne((-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 1, 0) & Ne((-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 1, 1) & Eq(-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 7, 64) & Eq(-x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 4, 64) & (-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 7 >= 0) & (-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 7 >= 1) & (-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 7 >= 2) & (-x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 4 >= 0) & (-x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 4 >= 1) & (-x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 4 >= 2) & (-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 7 > 1) & (-x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 4 > 1) & Ne(1, -2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 7) & Ne(1, -x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 4) & Ne(-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 7, -1) & Ne(-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 7, 0) & Ne(-x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 4, -1) & Ne(-x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 4, 0) & ((-x.size()[1] + x.size()[3] + 2)//2**2 + 2*(-x.size()[1] + x.size()[3] + 2)//2 + 1 >= 0) & ((-x.size()[1] + x.size()[3] + 2)//2**2 + 2*(-x.size()[1] + x.size()[3] + 2)//2 + 1 > 1) & Ne((-x.size()[1] + x.size()[3] + 2)//2**2 + 2*(-x.size()[1] + x.size()[3] + 2)//2 + 1, 0) & Ne((-x.size()[1] + x.size()[3] + 2)//2**2 + 2*(-x.size()[1] + x.size()[3] + 2)//2 + 1, 1) & Eq((-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 1, 32) & ((-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 1 >= 0) & ((-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 1 >= 1) & ((-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 1 >= 2) & ((-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2 + 1 >= 0) & ((-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2 + 1 >= 1) & ((-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2 + 1 >= 2) & ((-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 1 > 1) & ((-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2 + 1 > 1) & Ne(1, (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 1) & Ne((-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 1, -1) & Ne((-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 1, 0) & Ne((-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2 + 1, -1) & Ne((-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2 + 1, 0) & Ne((-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2 + 1, 1) & (24*(-x.size()[1] + x.size()[3] + 2)//2**2 + 2*24*(-x.size()[1] + x.size()[3] + 2)//2 + 24 > 1) & Ne(24*(-x.size()[1] + x.size()[3] + 2)//2**2 + 2*24*(-x.size()[1] + x.size()[3] + 2)//2 + 24, 0) & Eq(-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 4, 32) & (-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 4 >= 0) & (-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 4 >= 1) & (-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 4 >= 2) & (-4*x.size()[0]*x.size()[1] + 16*x.size()[0]*16 + 12*x.size()[0] + 4*x.size()[1] - 12 <= -4*x.size()[0]*x.size()[1] + 16*x.size()[0]*16 + 12*x.size()[0]) & (32*x.size()[0]*16 - 4*x.size()[0]*5 + 20*x.size()[0] + 4*5 - 20 <= 32*x.size()[0]*16 - 4*x.size()[0]*5 + 20*x.size()[0]) & (64*x.size()[0]*16 - 4*x.size()[0]*5 + 20*x.size()[0] + 4*5 - 20 <= 64*x.size()[0]*16 - 4*x.size()[0]*5 + 20*x.size()[0]) & (-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 4 > 1) & Ne(1, -x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 4) & Ne(-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 4, -1) & Ne(-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 4, 0) & Ne(-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 4, 0) & Ne(x.size()[0]*24*(-x.size()[1] + x.size()[3] + 2)//2**2 + 2*x.size()[0]*24*(-x.size()[1] + x.size()[3] + 2)//2 + x.size()[0]*24, 0) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1 >= 0) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1 >= 2) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 3 >= 0) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 3 >= 2) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 5 >= 0) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 5 >= 1) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 5 >= 2) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 5 >= 8) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 5 >= 12) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 3 > 0) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 5 > 0) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 5 > 2) & (1 < (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 5) & Ne(0, (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 3) & Ne(2, (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 5) & Ne((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1, 0) & Ne((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1, 1) & Ne((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 3, 1) & Ne((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 5, 0) & Ne((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 5, 1) & Ne((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 5, 12) & ((-x.size()[1] + x.size()[3] + 2)//2**2 + 2*(-x.size()[1] + x.size()[3] + 2)//2 + 1 >= (-x.size()[1] + x.size()[3] + 2)//2 + 1) & ((-x.size()[1] + x.size()[3] + 2)//2**2 + 2*(-x.size()[1] + x.size()[3] + 2)//2 + 1 > (-x.size()[1] + x.size()[3] + 2)//2 + 1) & (2*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 10 >= 0) & (2*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 12 >= 0) & (8*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 40 >= 0) & (((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8 >= 0) & (((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8 >= 1) & (-(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 5 <= 2) & (1 < 8*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 40) & (8 < 8*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 40) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8, 0) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8, 1) & Eq((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1, 16) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1 >= 0) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1 >= 1) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1 >= 2) & ((-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2 + 1 >= 0) & ((-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2 + 1 >= 1) & ((-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2 + 1 >= 2) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1 > 1) & ((-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2 + 1 > 1) & (8 < (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1) & Ne(1, (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1) & Ne((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1, -1) & Ne((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1, 0) & Ne((-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2 + 1, -1) & Ne((-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2 + 1, 0) & Ne((-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2 + 1, 1) & Eq(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2, 4) & (((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 1 >= 0) & (((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 1 >= 2) & (((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2 >= 0) & (((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2 >= 1) & (((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2 >= 2) & (((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 1 > 1) & (((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2 > 1) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 1, 0) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 1, 1) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2, -1) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2, 0) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2, 1) & Eq(Mod((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1, 8), 0) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2/8 + 1/8 >= 0) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2/8 + 1/8 >= 2) & (8*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 8 >= 0) & (8*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 8 >= 1) & (1 < (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2/8 + 1/8) & (8 < 8*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 8) & Ne((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2/8 + 1/8, -1) & Ne((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2/8 + 1/8, 0) & Ne((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2/8 + 1/8, 1) & Eq((-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 1, -x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 4) & (144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 144 >= 0) & (144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 144 >= 144) & ((-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 1 >= 0) & (2304*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2 >= 2304) & ((-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 1 > 1) & (1 < 144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) & (1 < 2304*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) & Ne(144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 144, 0) & Ne(144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 144, 1) & Ne((-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 1, 0) & Ne((-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 1, 1) & (384*x.size()[0]*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2 > 0) & Ne(384*x.size()[0]*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2, 0) & Eq(-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 7, -x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 4) & (x.size()[1]**2 - 2*x.size()[1]*(-x.size()[1] + x.size()[3] + 2)//2 - 8*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2**2 + 8*(-x.size()[1] + x.size()[3] + 2)//2 + 16 >= 0) & (64*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 2*64*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 64 > 1) & (x.size()[1]**2 - 2*x.size()[1]*(-x.size()[1] + x.size()[3] + 2)//2 - 8*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2**2 + 8*(-x.size()[1] + x.size()[3] + 2)//2 + 16 > 1) & (1 < x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 2*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + x.size()[3]) & Ne(x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 2*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + x.size()[3], 0) & Ne(64*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 2*64*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 64, 0) & Ne(x.size()[1]**2 - 2*x.size()[1]*(-x.size()[1] + x.size()[3] + 2)//2 - 8*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2**2 + 8*(-x.size()[1] + x.size()[3] + 2)//2 + 16, 0) & Ne(x.size()[1]**2 - 2*x.size()[1]*(-x.size()[1] + x.size()[3] + 2)//2 - 8*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2**2 + 8*(-x.size()[1] + x.size()[3] + 2)//2 + 16, 1) & (4*x.size()[1]**2 - 4*x.size()[1]*(-x.size()[1] + x.size()[3] + 2)//2 - 28*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2**2 + 14*(-x.size()[1] + x.size()[3] + 2)//2 + 49 >= 0) & (4*x.size()[1]**2 - 4*x.size()[1]*(-x.size()[1] + x.size()[3] + 2)//2 - 28*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2**2 + 14*(-x.size()[1] + x.size()[3] + 2)//2 + 49 > 1) & Ne(4*x.size()[1]**2 - 4*x.size()[1]*(-x.size()[1] + x.size()[3] + 2)//2 - 28*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2**2 + 14*(-x.size()[1] + x.size()[3] + 2)//2 + 49, 0) & Ne(4*x.size()[1]**2 - 4*x.size()[1]*(-x.size()[1] + x.size()[3] + 2)//2 - 28*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2**2 + 14*(-x.size()[1] + x.size()[3] + 2)//2 + 49, 1) & (4*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 8*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 4*16 > 1) & Ne(x.size()[0]*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 2*x.size()[0]*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + x.size()[0]*x.size()[3], 0) & Ne(x.size()[0]*64*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 2*x.size()[0]*64*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + x.size()[0]*64, 0) & (24*(-x.size()[1] + x.size()[3] + 2)//2**2 + 2*24*(-x.size()[1] + x.size()[3] + 2)//2 + 24 >= (-x.size()[1] + x.size()[3] + 2)//2**2 + 2*(-x.size()[1] + x.size()[3] + 2)//2 + 1) & (24*(-x.size()[1] + x.size()[3] + 2)//2**2 + 2*24*(-x.size()[1] + x.size()[3] + 2)//2 + 24 > (-x.size()[1] + x.size()[3] + 2)//2**2 + 2*(-x.size()[1] + x.size()[3] + 2)//2 + 1) & Eq((-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 1, (-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2 + 1) & (x.size()[1]**2*32 - 2*x.size()[1]*32*(-x.size()[1] + x.size()[3] + 2)//2 - 8*x.size()[1]*32 + 32*(-x.size()[1] + x.size()[3] + 2)//2**2 + 8*32*(-x.size()[1] + x.size()[3] + 2)//2 + 16*32 > 1) & Ne(x.size()[1]**2*32 - 2*x.size()[1]*32*(-x.size()[1] + x.size()[3] + 2)//2 - 8*x.size()[1]*32 + 32*(-x.size()[1] + x.size()[3] + 2)//2**2 + 8*32*(-x.size()[1] + x.size()[3] + 2)//2 + 16*32, 0) & (4*x.size()[1]**2*64 - 4*x.size()[1]*64*(-x.size()[1] + x.size()[3] + 2)//2 - 28*x.size()[1]*64 + 64*(-x.size()[1] + x.size()[3] + 2)//2**2 + 14*64*(-x.size()[1] + x.size()[3] + 2)//2 + 49*64 > 1) & Ne(4*x.size()[1]**2*64 - 4*x.size()[1]*64*(-x.size()[1] + x.size()[3] + 2)//2 - 28*x.size()[1]*64 + 64*(-x.size()[1] + x.size()[3] + 2)//2**2 + 14*64*(-x.size()[1] + x.size()[3] + 2)//2 + 49*64, 0) & Eq((-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 1, -x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 4) & ((-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 2*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 1 >= 0) & ((-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2 + 1 >= 0) & ((-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 2*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 1 > 1) & Ne((-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 2*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 1, 0) & Ne((-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 2*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 1, 1) & Ne((-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2 + 1, 0) & Ne((-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2 + 1, 1) & (x.size()[1]**2 - 2*x.size()[1]*(-x.size()[1] + x.size()[3] + 2)//2 - 8*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2**2 + 8*(-x.size()[1] + x.size()[3] + 2)//2 + 16 >= -x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 4) & (x.size()[1]**2 - 2*x.size()[1]*(-x.size()[1] + x.size()[3] + 2)//2 - 8*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2**2 + 8*(-x.size()[1] + x.size()[3] + 2)//2 + 16 > -x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 4) & ((-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 1 >= (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 1) & ((-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 1 > (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 1) & (4*x.size()[1]**2 - 4*x.size()[1]*(-x.size()[1] + x.size()[3] + 2)//2 - 28*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2**2 + 14*(-x.size()[1] + x.size()[3] + 2)//2 + 49 >= -2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 7) & (512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 2*512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 512 > 1) & (4*x.size()[1]**2 - 4*x.size()[1]*(-x.size()[1] + x.size()[3] + 2)//2 - 28*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2**2 + 14*(-x.size()[1] + x.size()[3] + 2)//2 + 49 > -2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 7) & (1 < 512*(-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2**2 + 2*512*(-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2 + 512) & Ne(512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 2*512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 512, 0) & Ne(512*(-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2**2 + 2*512*(-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2 + 512, 0) & Ne(x.size()[0]*x.size()[1]**2*32 - 2*x.size()[0]*x.size()[1]*32*(-x.size()[1] + x.size()[3] + 2)//2 - 8*x.size()[0]*x.size()[1]*32 + x.size()[0]*32*(-x.size()[1] + x.size()[3] + 2)//2**2 + 8*x.size()[0]*32*(-x.size()[1] + x.size()[3] + 2)//2 + 16*x.size()[0]*32, 0) & Ne(4*x.size()[0]*x.size()[1]**2*64 - 4*x.size()[0]*x.size()[1]*64*(-x.size()[1] + x.size()[3] + 2)//2 - 28*x.size()[0]*x.size()[1]*64 + x.size()[0]*64*(-x.size()[1] + x.size()[3] + 2)//2**2 + 14*x.size()[0]*64*(-x.size()[1] + x.size()[3] + 2)//2 + 49*x.size()[0]*64, 0) & (8*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 16*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 8*16 > 1) & Ne(8*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 16*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 8*16, 0) & Ne(x.size()[0]*512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 2*x.size()[0]*512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + x.size()[0]*512, 0) & Ne(x.size()[0]*512*(-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2**2 + 2*x.size()[0]*512*(-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2 + x.size()[0]*512, 0) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 5 >= (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 5) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1 < (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 3) & Ne(8*x.size()[0]*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 16*x.size()[0]*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 8*x.size()[0]*16, 0) & (x.size()[1]**2 - 2*x.size()[1]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 8*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 8*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 16 >= 0) & (x.size()[1]**2 - 2*x.size()[1]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 8*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 8*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 16 > 1) & Ne(x.size()[1]**2 - 2*x.size()[1]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 8*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 8*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 16, 0) & Ne(x.size()[1]**2 - 2*x.size()[1]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 8*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 8*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 16, 1) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 5 < 8*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 40) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1 >= 0) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 10*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 25 >= 0) & (4*x.size()[1]**2 - 4*x.size()[1]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 28*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 14*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 49 >= 0) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1 > 1) & (4*x.size()[1]**2 - 4*x.size()[1]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 28*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 14*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 49 > 1) & Ne((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1, 0) & Ne((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1, 1) & Ne((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 10*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 25, 0) & Ne((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 10*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 25, 1) & Ne(4*x.size()[1]**2 - 4*x.size()[1]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 28*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 14*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 49, 0) & Ne(4*x.size()[1]**2 - 4*x.size()[1]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 28*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 14*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 49, 1) & Ne(8*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 40, 12*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 60) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8), 0) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8), 1) & (256*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 512*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 256 > 1) & Eq(64, ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2) & Eq((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1, (-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2 + 1) & (64 >= ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2) & (((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2 >= 0) & (((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2 >= 2) & (384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 384 > 1) & (1 < ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2) & (8 < ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2, ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2, -1) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2, 0) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2, 1) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2, 8) & Eq(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8, 8) & Eq(Mod(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2, 8), 0) & (23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2 >= 0) & (8*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 8 >= (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1) & (384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 10*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 25*384 >= 0) & (((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 >= 0) & (((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 >= 2) & (144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2 >= 0) & (144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2 >= 144) & (144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2 > 144) & (1 < 23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2) & (1 < 384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 10*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 25*384) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8*(Mod(1, ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)), 0) & Ne((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2/8 + 1/8, (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1) & Ne(8*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 8, (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2, ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 1) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8, -1) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8, 0) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8, 1) & Ne(144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2, 0) & Ne(144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2, 1) & (23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 >= 0) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1 >= 0) & ((-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2**2 + 2*(-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2 + 1 >= 0) & (23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 > 0) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1 > 1) & (8 < (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1) & Ne(23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8, 0) & Ne(23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8, 1) & Ne((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1, 0) & Ne((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1, 1) & Ne((-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2**2 + 2*(-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2 + 1, 0) & Ne((-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2**2 + 2*(-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2 + 1, 1) & (23 - ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 >= 0) & (((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 + 1 >= 0) & (23 - ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 > 0) & (x.size()[1]**2*x.size()[3] - 2*x.size()[1]*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 8*x.size()[1]*x.size()[3] + x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 8*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 16*x.size()[3] > 1) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 + 1, -1) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 + 1, 0) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 + 1, 1) & Ne(x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0]*384, 0) & Ne(x.size()[1]**2*x.size()[3] - 2*x.size()[1]*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 8*x.size()[1]*x.size()[3] + x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 8*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 16*x.size()[3], 0) & Ne(Mod(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 1, ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 0) & (23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 + 23 >= 0) & (((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 1 >= 0) & (((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 1 >= 2) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2/64 + (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2/32 + 1/64 >= 0) & (23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 + 23 > 0) & (((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 1 > 1) & (16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 32*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 16 > 0) & (4*x.size()[1]**2*x.size()[3] - 4*x.size()[1]*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 28*x.size()[1]*x.size()[3] + x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 14*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 49*x.size()[3] > 1) & (1 < 23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 + 23) & (1 < 4*x.size()[1]**2*128 - 4*x.size()[1]*128*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 28*x.size()[1]*128 + 128*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 14*128*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 49*128) & Ne(0, 23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 + 23) & Ne(23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 + 23, 1) & Ne(x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 4*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 3*x.size()[0]*384, 0) & Ne(x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 6*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 5*x.size()[0]*384, 0) & Ne(x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 8*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 15*x.size()[0]*384, 0) & Ne(x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 10*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 25*x.size()[0]*384, 0) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 1, 0) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 1, 1) & Ne((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2/64 + (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2/32 + 1/64, -1) & Ne(16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 32*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 16, 0) & Ne(4*x.size()[1]**2*x.size()[3] - 4*x.size()[1]*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 28*x.size()[1]*x.size()[3] + x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 14*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 49*x.size()[3], 0) & Ne(4*x.size()[1]**2*128 - 4*x.size()[1]*128*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 28*x.size()[1]*128 + 128*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 14*128*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 49*128, 0) & Eq(8*(Mod(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8, 1)), 0) & (1024*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*1024*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1024 > 1) & (4*x.size()[1]**2*16 - 8*x.size()[1]*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 32*x.size()[1]*16 + 4*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 32*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 64*16 > 1) & (16*x.size()[1]**2*16 - 16*x.size()[1]*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 112*x.size()[1]*16 + 4*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 56*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 196*16 > 1) & (32*x.size()[1]**2*16 - 32*x.size()[1]*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 224*x.size()[1]*16 + 8*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 112*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 392*16 > 1) & (1 < 1024*(-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2**2 + 2*1024*(-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2 + 1024) & Ne(1024*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*1024*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1024, 0) & Ne(1024*(-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2**2 + 2*1024*(-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2 + 1024, 0) & Ne(4*x.size()[1]**2*16 - 8*x.size()[1]*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 32*x.size()[1]*16 + 4*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 32*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 64*16, 0) & Ne(16*x.size()[1]**2*16 - 16*x.size()[1]*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 112*x.size()[1]*16 + 4*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 56*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 196*16, 0) & (144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 288*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 144 >= 0) & (144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 288*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 144 >= 1) & (144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 288*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 144 >= 144) & (2304*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 4608*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 2304 >= 0) & (6912*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 13824*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 6912 >= 0) & (1 < 144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 288*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 144) & (1 < 2304*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 4608*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 2304) & (1 < 4608*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 9216*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 4608) & (1 < 6912*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 13824*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 6912) & (144 < 6912*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 13824*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 6912) & Ne(144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 288*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 144, 0) & Ne(144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 288*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 144, 1) & Ne(144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 288*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 144, 144) & ((-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 2*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 1 >= (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 1) & ((-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2 + 1 >= (-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2 + 1) & ((-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 2*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 1 > (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 1) & ((-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2 + 1 > (-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2 + 1) & (x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 2*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + x.size()[3] >= (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 1) & (64*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 2*64*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 64 >= (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 1) & (2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 4*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 2*x.size()[0] > 0) & (x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 2*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + x.size()[3] > (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 1) & (64*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 2*64*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 64 > (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 1) & (16*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 32*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 16*16 > 1) & (1 < 8*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 16*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 8*16) & Ne(2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 4*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 2*x.size()[0], 0) & Ne(16*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 32*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 16*16, 0) & Ne(x.size()[0]*1024*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*1024*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0]*1024, 0) & Ne(x.size()[0]*1024*(-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2**2 + 2*x.size()[0]*1024*(-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2 + x.size()[0]*1024, 0) & Ne(x.size()[0]*x.size()[1]**2*x.size()[3] - 2*x.size()[0]*x.size()[1]*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 8*x.size()[0]*x.size()[1]*x.size()[3] + x.size()[0]*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 8*x.size()[0]*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 16*x.size()[0]*x.size()[3], 0) & (18*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 36*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 18*384 >= 0) & (55296*x.size()[0]*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 110592*x.size()[0]*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 55296*x.size()[0] > 0) & Ne(18432*x.size()[0]*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 36864*x.size()[0]*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 18432*x.size()[0], 0) & Ne(36864*x.size()[0]*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 73728*x.size()[0]*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 36864*x.size()[0], 0) & Ne(55296*x.size()[0]*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 110592*x.size()[0]*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 55296*x.size()[0], 0) & Ne(221184*x.size()[0]*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 442368*x.size()[0]*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 221184*x.size()[0], 0) & Ne(4*x.size()[0]*x.size()[1]**2*x.size()[3] - 4*x.size()[0]*x.size()[1]*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 28*x.size()[0]*x.size()[1]*x.size()[3] + x.size()[0]*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 14*x.size()[0]*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 49*x.size()[0]*x.size()[3], 0) & Ne(4*x.size()[0]*x.size()[1]**2*128 - 4*x.size()[0]*x.size()[1]*128*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 28*x.size()[0]*x.size()[1]*128 + x.size()[0]*128*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 14*x.size()[0]*128*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 49*x.size()[0]*128, 0) & (8*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 16*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 8*x.size()[0]*16 > 0) & (1 < 8*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 16*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 8*x.size()[0]*16) & (8 < 8*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 16*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 8*x.size()[0]*16) & Ne(8*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 16*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 8*x.size()[0]*16, 0) & Ne(16*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 32*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 16*x.size()[0]*16, 0) & Ne(32*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 64*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 32*x.size()[0]*16, 0) & Ne(4*x.size()[0]*x.size()[1]**2*16 - 8*x.size()[0]*x.size()[1]*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 32*x.size()[0]*x.size()[1]*16 + 4*x.size()[0]*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 32*x.size()[0]*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 64*x.size()[0]*16, 0) & Ne(16*x.size()[0]*x.size()[1]**2*16 - 16*x.size()[0]*x.size()[1]*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 112*x.size()[0]*x.size()[1]*16 + 4*x.size()[0]*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 56*x.size()[0]*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 196*x.size()[0]*16, 0) & (x.size()[1]**2 - 2*x.size()[1]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 8*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 8*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 16 >= -x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 4) & (144*x.size()[0]*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 288*x.size()[0]*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 144*x.size()[0]*384 > 0) & (x.size()[1]**2 - 2*x.size()[1]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 8*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 8*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 16 > -x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 4) & Ne(144*x.size()[0]*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 288*x.size()[0]*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 144*x.size()[0]*384, 0) & Ne(576*x.size()[0]*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 1152*x.size()[0]*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 576*x.size()[0]*384, 0) & (x.size()[1]**2 - 2*x.size()[1]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 - 8*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 8*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 16 >= 0) & (4*x.size()[1]**2 - 4*x.size()[1]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 28*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 14*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 49 >= -2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 7) & (x.size()[1]**2 - 2*x.size()[1]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 - 8*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 8*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 16 > 1) & (4*x.size()[1]**2 - 4*x.size()[1]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 28*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 14*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 49 > -2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 7) & Ne(x.size()[1]**2 - 2*x.size()[1]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 - 8*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 8*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 16, 0) & Ne(x.size()[1]**2 - 2*x.size()[1]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 - 8*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 8*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 16, 1) & Eq(8*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2*(Mod(x.size()[0]*16, 2)) + 16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2*(Mod(x.size()[0]*16, 2)) + 8*(Mod(x.size()[0]*16, 2)), 0) & (x.size()[1]**2*32 - 2*x.size()[1]*32*(-x.size()[1] + x.size()[3] + 2)//2 - 8*x.size()[1]*32 + 32*(-x.size()[1] + x.size()[3] + 2)//2**2 + 8*32*(-x.size()[1] + x.size()[3] + 2)//2 + 16*32 >= x.size()[1]**2 - 2*x.size()[1]*(-x.size()[1] + x.size()[3] + 2)//2 - 8*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2**2 + 8*(-x.size()[1] + x.size()[3] + 2)//2 + 16) & (x.size()[1]**2*32 - 2*x.size()[1]*32*(-x.size()[1] + x.size()[3] + 2)//2 - 8*x.size()[1]*32 + 32*(-x.size()[1] + x.size()[3] + 2)//2**2 + 8*32*(-x.size()[1] + x.size()[3] + 2)//2 + 16*32 > x.size()[1]**2 - 2*x.size()[1]*(-x.size()[1] + x.size()[3] + 2)//2 - 8*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2**2 + 8*(-x.size()[1] + x.size()[3] + 2)//2 + 16) & (x.size()[1]**2 - 2*x.size()[1]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 - 8*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2**2 + 8*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 16 >= 0) & (x.size()[1]**2 - 2*x.size()[1]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 - 8*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2**2 + 8*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 16 > 1) & Ne(x.size()[1]**2 - 2*x.size()[1]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 - 8*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2**2 + 8*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 16, 0) & (4*x.size()[1]**2*64 - 4*x.size()[1]*64*(-x.size()[1] + x.size()[3] + 2)//2 - 28*x.size()[1]*64 + 64*(-x.size()[1] + x.size()[3] + 2)//2**2 + 14*64*(-x.size()[1] + x.size()[3] + 2)//2 + 49*64 >= 4*x.size()[1]**2 - 4*x.size()[1]*(-x.size()[1] + x.size()[3] + 2)//2 - 28*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2**2 + 14*(-x.size()[1] + x.size()[3] + 2)//2 + 49) & (4*x.size()[1]**2*64 - 4*x.size()[1]*64*(-x.size()[1] + x.size()[3] + 2)//2 - 28*x.size()[1]*64 + 64*(-x.size()[1] + x.size()[3] + 2)//2**2 + 14*64*(-x.size()[1] + x.size()[3] + 2)//2 + 49*64 > 4*x.size()[1]**2 - 4*x.size()[1]*(-x.size()[1] + x.size()[3] + 2)//2 - 28*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2**2 + 14*(-x.size()[1] + x.size()[3] + 2)//2 + 49) & (x.size()[1]**2*512 - 2*x.size()[1]*512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 - 8*x.size()[1]*512 + 512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 8*512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 16*512 > 1) & (1 < x.size()[1]**2*x.size()[3] - 2*x.size()[1]*x.size()[3]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 - 8*x.size()[1]*x.size()[3] + x.size()[3]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 8*x.size()[3]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 16*x.size()[3]) & Ne(x.size()[1]**2*x.size()[3] - 2*x.size()[1]*x.size()[3]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 - 8*x.size()[1]*x.size()[3] + x.size()[3]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 8*x.size()[3]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 16*x.size()[3], 0) & Ne(x.size()[1]**2*512 - 2*x.size()[1]*512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 - 8*x.size()[1]*512 + 512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 8*512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 16*512, 0) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 5 < (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 10*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 25) & (8*x.size()[1]**2*16 - 16*x.size()[1]*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 - 64*x.size()[1]*16 + 8*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 64*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 128*16 > 1) & Ne(8*x.size()[1]**2*16 - 16*x.size()[1]*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 - 64*x.size()[1]*16 + 8*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 64*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 128*16, 0) & (8*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 40 < (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 10*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 25) & (x.size()[1]**2*512 - 2*x.size()[1]*512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 - 8*x.size()[1]*512 + 512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2**2 + 8*512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 16*512 > 1) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 5 < 384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 10*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 25*384) & Ne(x.size()[0]*x.size()[1]**2*x.size()[3] - 2*x.size()[0]*x.size()[1]*x.size()[3]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 - 8*x.size()[0]*x.size()[1]*x.size()[3] + x.size()[0]*x.size()[3]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 8*x.size()[0]*x.size()[3]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 16*x.size()[0]*x.size()[3], 0) & Ne(x.size()[0]*x.size()[1]**2*512 - 2*x.size()[0]*x.size()[1]*512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 - 8*x.size()[0]*x.size()[1]*512 + x.size()[0]*512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 8*x.size()[0]*512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 16*x.size()[0]*512, 0) & ((8*x.size()[0]*16)//((x.size()[0]*16)//2)*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*(8*x.size()[0]*16)//((x.size()[0]*16)//2)*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + (8*x.size()[0]*16)//((x.size()[0]*16)//2) >= 0) & (16*x.size()[1]**2*16 - 32*x.size()[1]*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 - 128*x.size()[1]*16 + 16*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2**2 + 128*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 256*16 > 1) & (1 < (8*x.size()[0]*16)//((x.size()[0]*16)//2)*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*(8*x.size()[0]*16)//((x.size()[0]*16)//2)*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + (8*x.size()[0]*16)//((x.size()[0]*16)//2)) & (8 < (8*x.size()[0]*16)//((x.size()[0]*16)//2)*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*(8*x.size()[0]*16)//((x.size()[0]*16)//2)*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + (8*x.size()[0]*16)//((x.size()[0]*16)//2)) & Ne(8*x.size()[0]*x.size()[1]**2*16 - 16*x.size()[0]*x.size()[1]*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 - 64*x.size()[0]*x.size()[1]*16 + 8*x.size()[0]*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 64*x.size()[0]*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 128*x.size()[0]*16, 0) & (((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2 >= 0) & (((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2 >= 1) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1 >= (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1) & ((-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2**2 + 2*(-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2 + 1 >= (-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2 + 1) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1 > (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1) & ((-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2**2 + 2*(-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2 + 1 > (-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2 + 1) & Ne(1, ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2, 0) & (144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2 >= 0) & (1 < 144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2) & Ne(144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2, 0) & Eq((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 16) & (512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 2*512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 512 >= (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 2*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 1) & (512*(-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2**2 + 2*512*(-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2 + 512 >= (-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2 + 1) & ((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) >= 0) & ((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) >= 2) & (512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 2*512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 512 > (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 2*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 1) & (512*(-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2**2 + 2*512*(-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2 + 512 > (-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 6)//2 + 1) & (8*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 8 < (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1) & Ne(4*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 40*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 100*x.size()[0]*384 - 16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 96, 0) & Ne(4*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 40*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 100*x.size()[0]*384 - 16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 88, 0) & Ne(4*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 40*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 100*x.size()[0]*384 - 16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 80, 0) & Ne(4*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 40*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 100*x.size()[0]*384 - 8*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 40, 0) & Ne((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), -1) & Ne((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 0) & Ne((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 1) & Eq(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 1, ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) & Eq(8*x.size()[0], (x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2/64 + (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2/32 + 1/64 >= (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2/8 + 1/8) & Ne((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2/64 + (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2/32 + 1/64, (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2/8 + 1/8) & Eq(Mod(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 1, ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 0) & (8*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 16*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 8*16 >= (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 2*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 1) & (8*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 16*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 8*16 > (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 2*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 1) & Eq(16, (128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))) & (16 >= (128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))) & ((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)) >= 0) & ((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)) >= 2) & ((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)) > 1) & Ne((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)), -1) & Ne((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)), 0) & Ne((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)), 1) & Eq(144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 288*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 144, 144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) & Eq((144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 288*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 144)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 144) & (x.size()[1]**2 - 2*x.size()[1]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 - 8*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 8*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 16 >= -x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 4) & (x.size()[1]**2 - 2*x.size()[1]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 - 8*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 8*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 16 > -x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 4) & Ne(6912*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 13824*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 6912, 2304*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) & Ne((144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 288*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 144)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 12) & Eq(Mod(144, (144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 288*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 144)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)), 0) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1 < 8*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 16*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 8*x.size()[0]*16) & Ne(Mod(12, (144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 288*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 144)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)), 0) & (8*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 8 < 8*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 16*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 8*x.size()[0]*16) & Ne((x.size()[0]*16)//2*(8*x.size()[0]*16)//((x.size()[0]*16)//2)*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*(x.size()[0]*16)//2*(8*x.size()[0]*16)//((x.size()[0]*16)//2)*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + (x.size()[0]*16)//2*(8*x.size()[0]*16)//((x.size()[0]*16)//2), 0) & Ne(144*x.size()[0]*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 288*x.size()[0]*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 144*x.size()[0]*384, 384*x.size()[0]*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) & Eq(48*x.size()[0]*(Mod(3*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 6*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 3*384, 8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)), 0) & Ne(4*(x.size()[0]*16)//2*(8*x.size()[0]*16)//((x.size()[0]*16)//2)*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 8*(x.size()[0]*16)//2*(8*x.size()[0]*16)//((x.size()[0]*16)//2)*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 4*(x.size()[0]*16)//2*(8*x.size()[0]*16)//((x.size()[0]*16)//2), 0) & Ne((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 10*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 25, (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 6*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 5) & Ne((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 10*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 25, (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 8*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 15) & (x.size()[1]**2*x.size()[3] - 2*x.size()[1]*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 8*x.size()[1]*x.size()[3] + x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 8*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 16*x.size()[3] >= x.size()[1]**2 - 2*x.size()[1]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 8*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 8*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 16) & (x.size()[1]**2*x.size()[3] - 2*x.size()[1]*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 8*x.size()[1]*x.size()[3] + x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 8*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 16*x.size()[3] > x.size()[1]**2 - 2*x.size()[1]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 8*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 8*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 16) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 10*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 25 < 384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 10*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 25*384) & (4*x.size()[1]**2*16 - 8*x.size()[1]*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 32*x.size()[1]*16 + 4*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 32*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 64*16 >= x.size()[1]**2 - 2*x.size()[1]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 8*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 8*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 16) & (4*x.size()[1]**2*16 - 8*x.size()[1]*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 32*x.size()[1]*16 + 4*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 32*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 64*16 > x.size()[1]**2 - 2*x.size()[1]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 8*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 8*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 16) & (((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8) >= ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) & (4*x.size()[1]**2*x.size()[3] - 4*x.size()[1]*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 28*x.size()[1]*x.size()[3] + x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 14*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 49*x.size()[3] >= 4*x.size()[1]**2 - 4*x.size()[1]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 28*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 14*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 49) & (4*x.size()[1]**2*128 - 4*x.size()[1]*128*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 28*x.size()[1]*128 + 128*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 14*128*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 49*128 >= 4*x.size()[1]**2 - 4*x.size()[1]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 28*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 14*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 49) & (4*x.size()[1]**2*x.size()[3] - 4*x.size()[1]*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 28*x.size()[1]*x.size()[3] + x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 14*x.size()[3]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 49*x.size()[3] > 4*x.size()[1]**2 - 4*x.size()[1]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 28*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 14*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 49) & (4*x.size()[1]**2*128 - 4*x.size()[1]*128*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 28*x.size()[1]*128 + 128*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 14*128*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 49*128 > 4*x.size()[1]**2 - 4*x.size()[1]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 28*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 14*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 49) & (16*x.size()[1]**2*16 - 16*x.size()[1]*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 112*x.size()[1]*16 + 4*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 56*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 196*16 >= 4*x.size()[1]**2 - 4*x.size()[1]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 28*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 14*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 49) & (16*x.size()[1]**2*16 - 16*x.size()[1]*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 112*x.size()[1]*16 + 4*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 56*16*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 196*16 > 4*x.size()[1]**2 - 4*x.size()[1]*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 - 28*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2**2 + 14*(-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 49) & (8*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 8 < (8*x.size()[0]*16)//((x.size()[0]*16)//2)*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*(8*x.size()[0]*16)//((x.size()[0]*16)//2)*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + (8*x.size()[0]*16)//((x.size()[0]*16)//2)) & (((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2 >= ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2, ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) & (23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2 >= 23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8) & (23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 + ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 >= 0) & (23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 + ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 >= 2) & Ne(23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 + ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8, -1) & Ne(23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 + ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8, 0) & Ne(23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 + ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8, 1) & Eq(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 64) & (((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) >= 0) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), -1) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 0) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 1) & Ne(12*x.size()[0]*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 60*x.size()[0]*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 12*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 60*x.size()[0]*384, 0) & (8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) >= 0) & (8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) >= 2) & (1024*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*1024*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1024 >= (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1) & (1024*(-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2**2 + 2*1024*(-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2 + 1024 >= (-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2**2 + 2*(-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2 + 1) & (2304*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) > 0) & (1024*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*1024*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1024 > (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1) & (1024*(-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2**2 + 2*1024*(-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2 + 1024 > (-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2**2 + 2*(-x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 3)//2 + 1) & (23 + 1 < 23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 + ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8) & Ne(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), -1) & Ne(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 0) & Ne(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 1) & Ne(2304*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 0) & Ne(9216*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 0) & Ne(23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 + 23, ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 + 1) & Eq((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)*(Mod(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2, 1)), 0) & Ne((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)*(Mod(1, ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)), 0) & (8*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 16*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 8*16 >= (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1) & (16*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 32*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 16*16 >= (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1) & (4*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 40*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 100*x.size()[0]*384 <= 4*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 40*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 100*x.size()[0]*384) & (16*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 32*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 16*16 > (-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1) & (144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 288*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 144 >= 144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 288*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 144) & (144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 288*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 144 < 6912*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 13824*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 6912) & Ne(6912*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 13824*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 6912, 2304*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 4608*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 2304) & Ne(6912*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 13824*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 6912, 4608*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 9216*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 4608) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1 < 8*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 16*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 8*x.size()[0]*16) & (16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 32*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 16 < 8*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 16*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 8*x.size()[0]*16) & Ne(8*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 16*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 8*x.size()[0]*16, 16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 32*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 16) & (144*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 288*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 144*384 >= 144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 288*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 144) & Ne(221184*x.size()[0]*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 442368*x.size()[0]*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 221184*x.size()[0] - 18432*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 - 36864*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 - 18432, 0) & Ne(221184*x.size()[0]*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 442368*x.size()[0]*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 221184*x.size()[0] - 9216*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 - 18432*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 - 9216, 0) & (221184*x.size()[0]*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 442368*x.size()[0]*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 221184*x.size()[0] <= 221184*x.size()[0]*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 442368*x.size()[0]*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 221184*x.size()[0]) & (32*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 64*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 32*x.size()[0]*16 <= 32*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 64*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 32*x.size()[0]*16) & (576*x.size()[0]*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 1152*x.size()[0]*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 576*x.size()[0]*384 <= 576*x.size()[0]*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 1152*x.size()[0]*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 576*x.size()[0]*384) & ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 1 < (8*x.size()[0]*16)//((x.size()[0]*16)//2)*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*(8*x.size()[0]*16)//((x.size()[0]*16)//2)*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + (8*x.size()[0]*16)//((x.size()[0]*16)//2)) & (144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2 >= 144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2) & (144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2 > 144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2) & (x.size()[1]**2*x.size()[3] - 2*x.size()[1]*x.size()[3]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 - 8*x.size()[1]*x.size()[3] + x.size()[3]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 8*x.size()[3]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 16*x.size()[3] >= x.size()[1]**2 - 2*x.size()[1]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 - 8*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 8*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 16) & (x.size()[1]**2*512 - 2*x.size()[1]*512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 - 8*x.size()[1]*512 + 512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 8*512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 16*512 >= x.size()[1]**2 - 2*x.size()[1]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 - 8*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 8*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 16) & (x.size()[1]**2*x.size()[3] - 2*x.size()[1]*x.size()[3]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 - 8*x.size()[1]*x.size()[3] + x.size()[3]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 8*x.size()[3]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 16*x.size()[3] > x.size()[1]**2 - 2*x.size()[1]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 - 8*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 8*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 16) & (x.size()[1]**2*512 - 2*x.size()[1]*512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 - 8*x.size()[1]*512 + 512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 8*512*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 16*512 > x.size()[1]**2 - 2*x.size()[1]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 - 8*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 8*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 16) & ((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2 >= 0) & (8*x.size()[1]**2*16 - 16*x.size()[1]*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 - 64*x.size()[1]*16 + 8*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 64*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 128*16 >= x.size()[1]**2 - 2*x.size()[1]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 - 8*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 8*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 16) & (4*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 40*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 100*x.size()[0]*384 - 8*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 48 <= 4*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 40*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 100*x.size()[0]*384) & (4*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 40*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 100*x.size()[0]*384 - 8*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 40 <= 4*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 40*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 100*x.size()[0]*384) & (8*x.size()[1]**2*16 - 16*x.size()[1]*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 - 64*x.size()[1]*16 + 8*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 64*16*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 128*16 > x.size()[1]**2 - 2*x.size()[1]*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 - 8*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2**2 + 8*(-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + x.size()[3] + 2)//2 + 6)//2 + 9)//2 + 16) & (1 < (128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2) & Ne((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2, 0) & Ne((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2, 1) & ((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 >= 0) & Ne((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8, 0) & Ne((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8, 1) & (3*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2/(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + 3*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8/(4*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + 3*384/(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) >= 0) & (3*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2/(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + 3*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8/(4*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + 3*384/(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) >= 12) & Ne(3*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2/(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + 3*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8/(4*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + 3*384/(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), -1) & Ne(3*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2/(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + 3*384*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8/(4*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + 3*384/(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 12) & Ne((x.size()[0]*16)//2*(8*x.size()[0]*16)//((x.size()[0]*16)//2)*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*(x.size()[0]*16)//2*(8*x.size()[0]*16)//((x.size()[0]*16)//2)*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + (x.size()[0]*16)//2*(8*x.size()[0]*16)//((x.size()[0]*16)//2), 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 4*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 2*x.size()[0]) & (4*(x.size()[0]*16)//2*(8*x.size()[0]*16)//((x.size()[0]*16)//2)*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 8*(x.size()[0]*16)//2*(8*x.size()[0]*16)//((x.size()[0]*16)//2)*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 4*(x.size()[0]*16)//2*(8*x.size()[0]*16)//((x.size()[0]*16)//2) <= 32*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2**2 + 64*x.size()[0]*16*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (-x.size()[1] + 16*16 + 2)//2 + 6)//2 + 9)//2 + 6)//2 + 32*x.size()[0]*16) & (((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) >= 0) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), -1) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 0) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 1) & Ne(23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 + ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8, ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8) & (23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) > 0) & (1 < 23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)) & (23 < 23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)) & Ne(144*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 0) & Ne(576*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 0) & Ne(23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 0) & Eq(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)*(Mod(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2, 1)), 0) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)*(Mod(1, ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2)), 0) & Ne(4*23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 0) & (23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 + ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 < 23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 + 23) & Ne(23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 + 23, 23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 + ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8) & ((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2 >= 0) & (1 < (128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2) & Ne(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)*(Mod(1, ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8)), 0) & Eq(23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(Mod(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 1))/8, 0) & (221184*x.size()[0]*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 442368*x.size()[0]*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 221184*x.size()[0] - 18432*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 - 36864*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 - 18432 <= 221184*x.size()[0]*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 442368*x.size()[0]*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 221184*x.size()[0]) & Ne(4*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 40*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 100*x.size()[0]*384 + 32*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 160*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 - 4*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 8*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 140, 0) & Ne(4*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 40*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 100*x.size()[0]*384 + 32*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 192*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 - 4*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 4*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 168, 0) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), (x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)) & (((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2 < (128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2) & Ne((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2, ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) & (23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 < 23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)) & Ne(23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)) & (9216*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) <= 9216*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)) & ((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2 >= (128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))) & ((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 >= (128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))) & (4*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 40*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 100*x.size()[0]*384 + 32*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 160*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 - 4*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 8*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 140 <= 4*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 40*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 100*x.size()[0]*384) & (4*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 40*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 100*x.size()[0]*384 + 32*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 192*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 - 4*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 4*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 168 <= 4*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 40*x.size()[0]*384*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 100*x.size()[0]*384) & (((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2 < (128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2) & ((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) > 0) & Ne((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 0) & Ne((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 1024) & ((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)/1024 >= 0) & Ne((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)/1024, -1) & Ne((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)/1024, 1) & Ne(4*(128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 0) & Eq(Mod((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 1024), 0) & Eq(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)*(Mod(1, (128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/1024)), 0) & Ne((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)*(Mod(1, (128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/1024)), 0) & Ne(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)) & Ne(((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))//(128*x.size()[0]), ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)) & Eq(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2, ((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))//(128*x.size()[0])) & Ne((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)/1024, ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) & Ne(Mod(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8), ((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))//(128*x.size()[0])), 0) & (1 < 23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + 8*23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)) & (23 < 23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + 8*23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)) & Ne(23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + 8*23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 0) & Eq(Mod(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2, ((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))//(128*x.size()[0])), 0) & Ne(4*23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + 32*23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 0) & Eq(23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(Mod(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 1))/8 + 23*(Mod(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 1)), 0) & ((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2 >= (128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2) & (576*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) <= 576*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)) & (23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) > 0) & (1 < 23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)) & Ne(23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 0) & (4*23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) <= 4*23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)) & Ne(4*23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + 4*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 0) & Ne(4*23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + 4*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) - 4, 0) & Ne((128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)/1024, (x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)) & (23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 + 23 < 23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + 8*23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)) & Ne(23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + 8*23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), 23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 + 23) & Ne(4*23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + 32*23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) - 4*23 + ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/2, 0) & Eq(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2), (128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)/1024) & Ne(27648*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) - 19008*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 55296*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) - 38016*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 576*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2 + 27648*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) - 19008, 0) & (23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 + ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/8 < 23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)) & (27648*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) - 19008*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 55296*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) - 38016*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 576*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2 + 27648*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) - 19008 <= 221184*x.size()[0]*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8**2 + 442368*x.size()[0]*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 - 7)//8 + 221184*x.size()[0]) & (4*(128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) <= 4*(128*x.size()[0])//((x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2))*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)) & (4*23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + 32*23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) <= 4*23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + 32*23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)) & (4*23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + 32*23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) - 4*23 + ((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2/2 <= 4*23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + 32*23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)) & (4*23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + 4*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) <= 4*23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + 4*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)) & (4*23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + 4*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) - 4 <= 4*23*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2) + 4*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//(((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8)**2*(x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2**2 + 2*x.size()[0]*(-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + x.size()[0])//(8*((-2*x.size()[1] + (-3*x.size()[1] + (-2*x.size()[1] + (258 - x.size()[1])//2 + 6)//2 + 9)//2 + 6)//2 + 1)//8**2)) and x.size()[3] == x.size()[2] == x.stride()[2]) and
___check_tensors(x)NULL ERROR: /data/users/ezyang/pytorch-tmp/torch/csrc/dynamo/eval_frame.c:239
cuda train eca_halonext26ts FAIL
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running ese_vovnet19b_dw...
cuda train ese_vovnet19b_dw PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running fbnetc_100...
cuda train fbnetc_100 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running fbnetv3_b...
cuda train fbnetv3_b PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running gernet_l...
cuda train gernet_l PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running ghostnet_100...
cuda train ghostnet_100 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running gluon_inception_v3...
cuda train gluon_inception_v3 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running gluon_xception65...
cuda train gluon_xception65 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running gmixer_24_224...
cuda train gmixer_24_224 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running gmlp_s16_224...
cuda train gmlp_s16_224 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running hrnet_w18...
ERROR:common:maximum recursion depth exceeded during compilation
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1041, in check_accuracy
new_result = optimized_model_iter_fn(model, example_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 945, in run_n_iterations
self.model_iter_fn(mod, inputs, collect_outputs=False)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/timm_models.py", line 317, in forward_and_backward_pass
cloned_inputs = clone_inputs(inputs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/timm_models.py", line 320, in <graph break in forward_and_backward_pass>
pred = mod(*cloned_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 240, in catch_errors
return callback(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 437, in _convert_frame
result = inner_convert(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 112, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 87, in time_wrapper
r = func(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 319, in _convert_frame_assert
return _compile(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 414, in _compile
check_fn = CheckFunctionManager(output, output.guards, locals, globals)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/guards.py", line 592, in __init__
self.check_fn = self.compile_check_fn(local_builder, global_builder)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/guards.py", line 753, in compile_check_fn
exec(py_code, global_builder.scope, out)
RecursionError: maximum recursion depth exceeded during compilation
TorchDynamo optimized model failed to run because of following error
cuda train hrnet_w18 FAIL
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running inception_v3...
cuda train inception_v3 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running jx_nest_base...
ERROR:common:module 'operator' has no attribute 'sym_sqrt'
While executing %sym_sqrt : [#users=1] = call_function[target=torch.fx.experimental.symbolic_shapes.sym_sqrt](args = (%getitem_1,), kwargs = {})
Original traceback:
Module stack: {}
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/timm/models/nest.py", line 166, in deblockify
grid_size = int(math.sqrt(T))
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1041, in check_accuracy
new_result = optimized_model_iter_fn(model, example_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 945, in run_n_iterations
self.model_iter_fn(mod, inputs, collect_outputs=False)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/timm_models.py", line 317, in forward_and_backward_pass
cloned_inputs = clone_inputs(inputs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/timm_models.py", line 320, in <graph break in forward_and_backward_pass>
pred = mod(*cloned_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/timm/models/nest.py", line 372, in forward
x = self.forward_features(x)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/timm/models/nest.py", line 360, in forward_features
x = self.levels(x)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/container.py", line 204, in forward
input = module(input)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/timm/models/nest.py", line 213, in forward
x = deblockify(x, self.block_size) # (B, H', W', C')
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/timm/models/nest.py", line 158, in deblockify
@register_notrace_function # reason: int receives Proxy
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/functorch/_src/aot_autograd.py", line 893, in forward
return compiled_f(
File "/data/users/ezyang/pytorch-tmp/functorch/_src/aot_autograd.py", line 880, in new_func
compiled_fn = create_aot_dispatcher_function(
File "/data/users/ezyang/pytorch-tmp/functorch/_src/aot_autograd.py", line 600, in create_aot_dispatcher_function
aot_dispatch_autograd(flat_fn, fake_flat_tensor_args, aot_config)
File "/data/users/ezyang/pytorch-tmp/functorch/_src/aot_autograd.py", line 434, in aot_dispatch_autograd
fx_g = make_fx(joint_forward_backward, aot_config.decompositions)(*joint_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/fx/experimental/proxy_tensor.py", line 671, in wrapped
t = dispatch_trace(wrap_key(func, args, fx_tracer), tracer=fx_tracer, concrete_args=tuple(phs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/fx/experimental/proxy_tensor.py", line 422, in dispatch_trace
graph = tracer.trace(root, concrete_args)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/fx/_symbolic_trace.py", line 739, in trace
(self.create_arg(fn(*args)),),
File "/data/users/ezyang/pytorch-tmp/torch/fx/_symbolic_trace.py", line 614, in flatten_fn
tree_out = root_fn(*tree_args)
File "/data/users/ezyang/pytorch-tmp/torch/fx/experimental/proxy_tensor.py", line 439, in wrapped
out = f(*tensors)
File "/data/users/ezyang/pytorch-tmp/functorch/_src/aot_autograd.py", line 153, in joint_forward_backward
outs = fn(*primals)
File "/data/users/ezyang/pytorch-tmp/functorch/_src/aot_autograd.py", line 410, in <lambda>
joint_forward_backward = create_joint_forward_backward(lambda *args: flat_fn(*add_dupe_args(args)))
File "/data/users/ezyang/pytorch-tmp/functorch/_src/aot_autograd.py", line 841, in functional_call
out = Interpreter(mod).run(*args[params_len:], **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/fx/interpreter.py", line 130, in run
self.env[node] = self.run_node(node)
File "/data/users/ezyang/pytorch-tmp/torch/fx/interpreter.py", line 171, in run_node
return getattr(self, n.op)(n.target, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/fx/interpreter.py", line 243, in call_function
return target(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/fx/experimental/symbolic_shapes.py", line 109, in sym_sqrt
return a.__sym_sqrt__()
File "/data/users/ezyang/pytorch-tmp/torch/fx/experimental/symbolic_shapes.py", line 372, in unary_magic_impl
return wrap_node(getattr(self.node, method)())
File "/data/users/ezyang/pytorch-tmp/torch/fx/experimental/symbolic_shapes.py", line 342, in unary_magic_impl
op = getattr(operator, method)
AttributeError: module 'operator' has no attribute 'sym_sqrt'
While executing %sym_sqrt : [#users=1] = call_function[target=torch.fx.experimental.symbolic_shapes.sym_sqrt](args = (%getitem_1,), kwargs = {})
Original traceback:
Module stack: {}
File "/home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/timm/models/nest.py", line 166, in deblockify
grid_size = int(math.sqrt(T))
incomplete graph:
class joint_forward_backward(torch.nn.Module):
def forward(self, orig_primals, orig_tangents):
orig_primals_1: f32[s0, s1, s2, s3], [s1*s2*s3, s2*s3, s3, 1], orig_tangents_1, orig_tangents_2, orig_tangents_3, = fx_pytree.tree_flatten_spec([orig_primals, orig_tangents], self._in_spec)
pass
TorchDynamo optimized model failed to run because of following error
cuda train jx_nest_base FAIL
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running lcnet_050...
cuda train lcnet_050 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running levit_128...
WARNING:common:fp64 golden ref were not generated for levit_128
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/timm_models.py", line 334, in <module>
main(TimmRunnner())
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1775, in main
runner.run_one_model(
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 768, in inner
return fn(self, *args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1213, in run_one_model
status = self.check_accuracy(
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1017, in check_accuracy
correct_result = self.run_n_iterations(
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 946, in run_n_iterations
return self.model_iter_fn(mod, inputs, collect_outputs=True)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/timm_models.py", line 324, in forward_and_backward_pass
self.grad_scaler.scale(loss).backward()
File "/data/users/ezyang/pytorch-tmp/torch/_tensor.py", line 488, in backward
torch.autograd.backward(
File "/data/users/ezyang/pytorch-tmp/torch/autograd/__init__.py", line 197, in backward
Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
RuntimeError: Trying to backward through the graph a second time (or directly access saved tensors after they have already been freed). Saved intermediate values of the graph are freed when you call .backward() or autograd.grad(). Specify retain_graph=True if you need to backward through the graph a second time or if you need to access saved tensors after calling backward.
cuda train levit_128 FAIL
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running mixer_b16_224...
cuda train mixer_b16_224 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running mixnet_l...
cuda train mixnet_l PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running mnasnet_100...
cuda train mnasnet_100 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running mobilenetv2_100...
cuda train mobilenetv2_100 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running mobilenetv3_large_100...
cuda train mobilenetv3_large_100 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running mobilevit_s...
Traceback (most recent call last):
File "<string>", line 2, in <lambda>
NameError: name 'ceiling' is not defined
NULL ERROR: /data/users/ezyang/pytorch-tmp/torch/csrc/dynamo/eval_frame.c:239
cuda train mobilevit_s FAIL
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running nfnet_l0...
ERROR:common:Failed running reshape_as(*(FakeTensor(FakeTensor(..., device='meta', size=(1, 16, s1**3),
grad_fn=<NativeBatchNormBackward0>), cuda:0), FakeTensor(Parameter(FakeTensor(..., device='meta', size=(16, s1, s1, s1), requires_grad=True)), cuda:0)), **{}):
Cannot call sizes() on tensor with symbolic sizes/strides
(scroll up for backtrace)
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 55, in _run_node
return getattr(args[0], node.target)(*args[1:], **kwargs)
RuntimeError: Cannot call sizes() on tensor with symbolic sizes/strides
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1041, in check_accuracy
new_result = optimized_model_iter_fn(model, example_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 945, in run_n_iterations
self.model_iter_fn(mod, inputs, collect_outputs=False)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/timm_models.py", line 317, in forward_and_backward_pass
cloned_inputs = clone_inputs(inputs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/timm_models.py", line 320, in <graph break in forward_and_backward_pass>
pred = mod(*cloned_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 240, in catch_errors
return callback(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 437, in _convert_frame
result = inner_convert(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 112, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 87, in time_wrapper
r = func(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 319, in _convert_frame_assert
return _compile(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 374, in _compile
out_code = transform_code_object(code, transform)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/bytecode_transformation.py", line 341, in transform_code_object
transformations(instructions, code_options)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 362, in transform
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1466, in run
super().run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 221, in call_function
return super().call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 191, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 62, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/nn_module.py", line 183, in call_function
tx.call_function(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/nn_module.py", line 221, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/misc.py", line 571, in call_function
return self.obj.call_method(tx, self.name, args, kwargs).add_options(self)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 612, in call_method
return self.__class__.create(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 200, in create
example_value = _get_fake_value(proxy.node, tx)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 131, in _get_fake_value
return wrap_fake_exception(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 709, in wrap_fake_exception
return fn()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 132, in <lambda>
lambda: _run_node(tx.output, node, args, kwargs, nnmodule)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 62, in _run_node
raise RuntimeError(
RuntimeError: Failed running reshape_as(*(FakeTensor(FakeTensor(..., device='meta', size=(1, 16, s1**3),
grad_fn=<NativeBatchNormBackward0>), cuda:0), FakeTensor(Parameter(FakeTensor(..., device='meta', size=(16, s1, s1, s1), requires_grad=True)), cuda:0)), **{}):
Cannot call sizes() on tensor with symbolic sizes/strides
(scroll up for backtrace)
TorchDynamo optimized model failed to run because of following error
cuda train nfnet_l0 FAIL
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running pit_b_224...
cuda train pit_b_224 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running pnasnet5large...
ERROR:common:Failed running <built-in method clamp of type object at 0x7fcdc0549b20>(*(s5 - (-s1 + s2)//2 + 2*ceiling((-s1 + s2)//2/2 + 1/2) - 3,), **{'min': 0}):
clamp() received an invalid combination of arguments - got (SymInt, min=int), but expected one of:
* (Tensor input, Tensor min, Tensor max, *, Tensor out)
* (Tensor input, Number min, Number max, *, Tensor out)
(scroll up for backtrace)
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 53, in _run_node
return node.target(*args, **kwargs)
TypeError: clamp() received an invalid combination of arguments - got (SymInt, min=int), but expected one of:
* (Tensor input, Tensor min, Tensor max, *, Tensor out)
* (Tensor input, Number min, Number max, *, Tensor out)
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1041, in check_accuracy
new_result = optimized_model_iter_fn(model, example_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 945, in run_n_iterations
self.model_iter_fn(mod, inputs, collect_outputs=False)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/timm_models.py", line 317, in forward_and_backward_pass
cloned_inputs = clone_inputs(inputs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/timm_models.py", line 320, in <graph break in forward_and_backward_pass>
pred = mod(*cloned_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 240, in catch_errors
return callback(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 437, in _convert_frame
result = inner_convert(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 112, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 87, in time_wrapper
r = func(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 319, in _convert_frame_assert
return _compile(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 374, in _compile
out_code = transform_code_object(code, transform)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/bytecode_transformation.py", line 341, in transform_code_object
transformations(instructions, code_options)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 362, in transform
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1466, in run
super().run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 221, in call_function
return super().call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 191, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 62, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/nn_module.py", line 221, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 221, in call_function
return super().call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 191, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 62, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/nn_module.py", line 221, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/nn_module.py", line 221, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/nn_module.py", line 221, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 191, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 62, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 191, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 62, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 191, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 62, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/builtin.py", line 344, in call_function
result = handler(tx, *args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/builtin.py", line 398, in _call_min_max
result = variables.TorchVariable(torch.clamp).call_function(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/torch.py", line 404, in call_function
tensor_variable = TensorVariable.create(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 200, in create
example_value = _get_fake_value(proxy.node, tx)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 131, in _get_fake_value
return wrap_fake_exception(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 709, in wrap_fake_exception
return fn()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 132, in <lambda>
lambda: _run_node(tx.output, node, args, kwargs, nnmodule)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 62, in _run_node
raise RuntimeError(
RuntimeError: Failed running <built-in method clamp of type object at 0x7fcdc0549b20>(*(s5 - (-s1 + s2)//2 + 2*ceiling((-s1 + s2)//2/2 + 1/2) - 3,), **{'min': 0}):
clamp() received an invalid combination of arguments - got (SymInt, min=int), but expected one of:
* (Tensor input, Tensor min, Tensor max, *, Tensor out)
* (Tensor input, Number min, Number max, *, Tensor out)
(scroll up for backtrace)
TorchDynamo optimized model failed to run because of following error
cuda train pnasnet5large FAIL
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running poolformer_m36...
cuda train poolformer_m36 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running regnety_002...
cuda train regnety_002 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running repvgg_a2...
cuda train repvgg_a2 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running res2net101_26w_4s...
cuda train res2net101_26w_4s PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running res2net50_14w_8s...
cuda train res2net50_14w_8s PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running res2next50...
cuda train res2next50 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running resmlp_12_224...
cuda train resmlp_12_224 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running resnest101e...
cuda train resnest101e PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running rexnet_100...
cuda train rexnet_100 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running sebotnet33ts_256...
cuda train sebotnet33ts_256 FAIL (TIMEOUT)
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running selecsls42b...
cuda train selecsls42b PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running spnasnet_100...
cuda train spnasnet_100 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running swin_base_patch4_window7_224...
ERROR:common:Failed running view(*(FakeTensor(FakeTensor(..., device='meta', size=(64*s0, 7, 7, 128),
grad_fn=<ViewBackward0>), cuda:0), s0, 8, 8, 7, 7, -1), **{}):
view() received an invalid combination of arguments - got (SymFloat, int, int, int, int, int), but expected one of:
* (torch.dtype dtype)
* (tuple of ints size)
(scroll up for backtrace)
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 55, in _run_node
return getattr(args[0], node.target)(*args[1:], **kwargs)
TypeError: view() received an invalid combination of arguments - got (SymFloat, int, int, int, int, int), but expected one of:
* (torch.dtype dtype)
* (tuple of ints size)
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1041, in check_accuracy
new_result = optimized_model_iter_fn(model, example_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 945, in run_n_iterations
self.model_iter_fn(mod, inputs, collect_outputs=False)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/timm_models.py", line 317, in forward_and_backward_pass
cloned_inputs = clone_inputs(inputs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/timm_models.py", line 320, in <graph break in forward_and_backward_pass>
pred = mod(*cloned_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 240, in catch_errors
return callback(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 437, in _convert_frame
result = inner_convert(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 112, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 87, in time_wrapper
r = func(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 319, in _convert_frame_assert
return _compile(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 374, in _compile
out_code = transform_code_object(code, transform)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/bytecode_transformation.py", line 341, in transform_code_object
transformations(instructions, code_options)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 362, in transform
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1466, in run
super().run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 221, in call_function
return super().call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 191, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 62, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/nn_module.py", line 183, in call_function
tx.call_function(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/nn_module.py", line 221, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/nn_module.py", line 183, in call_function
tx.call_function(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/nn_module.py", line 221, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 191, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 62, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/misc.py", line 571, in call_function
return self.obj.call_method(tx, self.name, args, kwargs).add_options(self)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 612, in call_method
return self.__class__.create(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 200, in create
example_value = _get_fake_value(proxy.node, tx)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 131, in _get_fake_value
return wrap_fake_exception(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 709, in wrap_fake_exception
return fn()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 132, in <lambda>
lambda: _run_node(tx.output, node, args, kwargs, nnmodule)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 62, in _run_node
raise RuntimeError(
RuntimeError: Failed running view(*(FakeTensor(FakeTensor(..., device='meta', size=(64*s0, 7, 7, 128),
grad_fn=<ViewBackward0>), cuda:0), s0, 8, 8, 7, 7, -1), **{}):
view() received an invalid combination of arguments - got (SymFloat, int, int, int, int, int), but expected one of:
* (torch.dtype dtype)
* (tuple of ints size)
(scroll up for backtrace)
TorchDynamo optimized model failed to run because of following error
cuda train swin_base_patch4_window7_224 FAIL
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running swsl_resnext101_32x16d...
cuda train swsl_resnext101_32x16d PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running tf_efficientnet_b0...
ERROR:common:Failed running <built-in method clamp of type object at 0x7f5c45f05b20>(*(s1 - s2 + 2*ceiling(s2/2) - 2,), **{'min': 0}):
clamp() received an invalid combination of arguments - got (SymInt, min=int), but expected one of:
* (Tensor input, Tensor min, Tensor max, *, Tensor out)
* (Tensor input, Number min, Number max, *, Tensor out)
(scroll up for backtrace)
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 53, in _run_node
return node.target(*args, **kwargs)
TypeError: clamp() received an invalid combination of arguments - got (SymInt, min=int), but expected one of:
* (Tensor input, Tensor min, Tensor max, *, Tensor out)
* (Tensor input, Number min, Number max, *, Tensor out)
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1041, in check_accuracy
new_result = optimized_model_iter_fn(model, example_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 945, in run_n_iterations
self.model_iter_fn(mod, inputs, collect_outputs=False)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/timm_models.py", line 317, in forward_and_backward_pass
cloned_inputs = clone_inputs(inputs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/timm_models.py", line 320, in <graph break in forward_and_backward_pass>
pred = mod(*cloned_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 240, in catch_errors
return callback(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 437, in _convert_frame
result = inner_convert(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 112, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 87, in time_wrapper
r = func(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 319, in _convert_frame_assert
return _compile(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 374, in _compile
out_code = transform_code_object(code, transform)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/bytecode_transformation.py", line 341, in transform_code_object
transformations(instructions, code_options)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 362, in transform
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1466, in run
super().run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 221, in call_function
return super().call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 191, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 62, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/nn_module.py", line 221, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 191, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 62, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 191, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 62, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 191, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 62, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/builtin.py", line 344, in call_function
result = handler(tx, *args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/builtin.py", line 398, in _call_min_max
result = variables.TorchVariable(torch.clamp).call_function(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/torch.py", line 404, in call_function
tensor_variable = TensorVariable.create(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 200, in create
example_value = _get_fake_value(proxy.node, tx)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 131, in _get_fake_value
return wrap_fake_exception(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 709, in wrap_fake_exception
return fn()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 132, in <lambda>
lambda: _run_node(tx.output, node, args, kwargs, nnmodule)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 62, in _run_node
raise RuntimeError(
RuntimeError: Failed running <built-in method clamp of type object at 0x7f5c45f05b20>(*(s1 - s2 + 2*ceiling(s2/2) - 2,), **{'min': 0}):
clamp() received an invalid combination of arguments - got (SymInt, min=int), but expected one of:
* (Tensor input, Tensor min, Tensor max, *, Tensor out)
* (Tensor input, Number min, Number max, *, Tensor out)
(scroll up for backtrace)
TorchDynamo optimized model failed to run because of following error
cuda train tf_efficientnet_b0 FAIL
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running tf_mixnet_l...
ERROR:common:Failed running <built-in method clamp of type object at 0x7fbb858a3b20>(*(s1 - s2 + 2*ceiling(s2/2) - 2,), **{'min': 0}):
clamp() received an invalid combination of arguments - got (SymInt, min=int), but expected one of:
* (Tensor input, Tensor min, Tensor max, *, Tensor out)
* (Tensor input, Number min, Number max, *, Tensor out)
(scroll up for backtrace)
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 53, in _run_node
return node.target(*args, **kwargs)
TypeError: clamp() received an invalid combination of arguments - got (SymInt, min=int), but expected one of:
* (Tensor input, Tensor min, Tensor max, *, Tensor out)
* (Tensor input, Number min, Number max, *, Tensor out)
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 1041, in check_accuracy
new_result = optimized_model_iter_fn(model, example_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 157, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/common.py", line 945, in run_n_iterations
self.model_iter_fn(mod, inputs, collect_outputs=False)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/timm_models.py", line 317, in forward_and_backward_pass
cloned_inputs = clone_inputs(inputs)
File "/data/users/ezyang/pytorch-tmp/benchmarks/dynamo/timm_models.py", line 320, in <graph break in forward_and_backward_pass>
pred = mod(*cloned_inputs)
File "/data/users/ezyang/pytorch-tmp/torch/nn/modules/module.py", line 1423, in _call_impl
return forward_call(*input, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/eval_frame.py", line 240, in catch_errors
return callback(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 437, in _convert_frame
result = inner_convert(frame, cache_size)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 112, in _fn
return fn(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 87, in time_wrapper
r = func(*args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 319, in _convert_frame_assert
return _compile(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 374, in _compile
out_code = transform_code_object(code, transform)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/bytecode_transformation.py", line 341, in transform_code_object
transformations(instructions, code_options)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/convert_frame.py", line 362, in transform
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1466, in run
super().run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 221, in call_function
return super().call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 191, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 62, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/nn_module.py", line 221, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 191, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 62, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 191, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 62, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 191, in call_function
return super(UserFunctionVariable, self).call_function(tx, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/functions.py", line 62, in call_function
return tx.inline_user_function_return(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 296, in inline_user_function_return
result = InliningInstructionTranslator.inline_call(self, fn, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1538, in inline_call
return cls.inline_call_(parent, func, args, kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 1592, in inline_call_
tracer.run()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 352, in run
and self.step()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 325, in step
getattr(self, inst.opname)(inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 177, in wrapper
return inner_fn(self, inst)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 770, in CALL_FUNCTION
self.call_function(fn, args, {})
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/symbolic_convert.py", line 267, in call_function
self.push(fn.call_function(self, args, kwargs))
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/builtin.py", line 344, in call_function
result = handler(tx, *args, **kwargs)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/builtin.py", line 398, in _call_min_max
result = variables.TorchVariable(torch.clamp).call_function(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/torch.py", line 404, in call_function
tensor_variable = TensorVariable.create(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 200, in create
example_value = _get_fake_value(proxy.node, tx)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 131, in _get_fake_value
return wrap_fake_exception(
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/utils.py", line 709, in wrap_fake_exception
return fn()
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 132, in <lambda>
lambda: _run_node(tx.output, node, args, kwargs, nnmodule)
File "/data/users/ezyang/pytorch-tmp/torch/_dynamo/variables/tensor.py", line 62, in _run_node
raise RuntimeError(
RuntimeError: Failed running <built-in method clamp of type object at 0x7fbb858a3b20>(*(s1 - s2 + 2*ceiling(s2/2) - 2,), **{'min': 0}):
clamp() received an invalid combination of arguments - got (SymInt, min=int), but expected one of:
* (Tensor input, Tensor min, Tensor max, *, Tensor out)
* (Tensor input, Number min, Number max, *, Tensor out)
(scroll up for backtrace)
TorchDynamo optimized model failed to run because of following error
cuda train tf_mixnet_l FAIL
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running tinynet_a...
cuda train tinynet_a PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running tnt_s_patch16_224...
Traceback (most recent call last):
File "<string>", line 2, in <lambda>
NameError: name 'ceiling' is not defined
ERROR RUNNING GUARDS forward /home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/timm/models/tnt.py:264
___guarded_code.valid and
___check_type_id(x, 93921486862560) and
___check_obj_id(self, 140538489405344) and
self.training == False and
(isinstance(x, torch.Tensor) and Eq(4, 4) & Eq(x.size()[0], 2) & Eq(x.size()[1], 3) & Eq(x.stride()[2], 224) & Eq(384, 384) & (x.size()[0] <= 9223372036854775807) & (24 <= 9223372036854775807) & Ne(x.size()[0], 9223372036854775807) & Ne(768, 2) & Ne(24, 9223372036854775807) & Ne(48, 2) & Eq(384, 16*24) & Eq(768//12, 64) & Eq(48//8, 6) & Eq(Mod(768, 2), 0) & Eq(Mod(48, 2), 0) & (384 >= 16*24) & (768 >= 768//12) & (48 >= 48//8) & (768//12 >= 2) & (48//8 >= 2) & (1 < 768//12) & (1 < 48//8) & Ne(768/2, 1) & Ne(768/2, 6) & Ne(48/2, 1) & Ne(48/2, 4) & Ne(768//12, 0) & Ne(768//12, 1) & Ne(48//8, 0) & Ne(48//8, 1) & Eq(768, 12*768//12) & Eq(48, 8*48//8) & Eq(Mod(768/2, 6), 0) & Eq(Mod(48/2, 4), 0) & (768 >= 6*768//12) & (48 >= 4*48//8) & (1 < 6*768//12) & (1 < 4*48//8) & (1 < 64*48//8) & (768//12 < 768/2) & (48//8 < 48/2) & (48//8 < 16*48) & Ne(768, 6*768//12) & Ne(48, 4*48//8) & Ne(16*48//8, 0) & Eq(768/2, 6*768//12) & Eq(48/2, 4*48//8) & Eq(16*48, 128*48//8) & ((230 - 7)//4 - 2 >= 0) & ((230 - 7)//4 + 1 >= 0) & ((230 - 7)//4 + 1 >= 1) & ((230 - 7)//4 + 1 > 1) & Ne((230 - 7)//4 + 1, 0) & Ne((230 - 7)//4 + 1, 1) & (ceiling((230 - 7)//4/4 - 1/2) >= 0) & (ceiling((230 - 7)//4/4 - 1/2) >= 1) & (ceiling((230 - 7)//4/4 - 1/2) >= 2) & (ceiling((230 - 7)//4/4 - 1/2) > 1) & (0 < ceiling((230 - 7)//4/4 - 1/2)) & Ne(0, ceiling((230 - 7)//4/4 - 1/2)) & Ne(ceiling((230 - 7)//4/4 - 1/2), -1) & Ne(ceiling((230 - 7)//4/4 - 1/2), 1) & (((230 - 7)//4 - 3)//4 + 1 > 0) & Eq(ceiling((230 - 7)//4/4 - 1/2)**2, 196) & (4*ceiling((230 - 7)//4/4 - 1/2) >= 0) & (ceiling((230 - 7)//4/4 - 1/2)**2 >= 1) & (ceiling((230 - 7)//4/4 - 1/2)**2 >= 2) & (ceiling((230 - 7)//4/4 - 1/2)**2 > 1) & Ne(0, 4*ceiling((230 - 7)//4/4 - 1/2)) & Ne(8*ceiling((230 - 7)//4/4 - 1/2), 0) & Ne(32*ceiling((230 - 7)//4/4 - 1/2), 0) & Ne(ceiling((230 - 7)//4/4 - 1/2)**2, 0) & Ne(ceiling((230 - 7)//4/4 - 1/2)**2, 1) & Eq(16, 4*ceiling((230 - 7)//4/4 - 1/2)**2/49) & Eq(384, 96*ceiling((230 - 7)//4/4 - 1/2)**2/49) & Eq(197, ceiling((230 - 7)//4/4 - 1/2)**2 + 1) & Eq(x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2, 392) & Eq(ceiling((230 - 7)//4/4 - 1/2)**2 + 1, 197) & (x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2 >= 2) & (ceiling((230 - 7)//4/4 - 1/2)**2 + 1 >= 2) & (96*ceiling((230 - 7)//4/4 - 1/2)**2/49 >= 24) & (384*ceiling((230 - 7)//4/4 - 1/2)**2 >= 384) & (ceiling((230 - 7)//4/4 - 1/2)**2 + 1 <= 9223372036854775807) & (x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2 > 0) & (x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2 > 1) & (ceiling((230 - 7)//4/4 - 1/2)**2 + 1 > 1) & (384*ceiling((230 - 7)//4/4 - 1/2)**2 > 384) & (1 < 384*ceiling((230 - 7)//4/4 - 1/2)**2) & Ne(1, x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2) & Ne(x.size()[0], x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2) & Ne(196*x.size()[0], ceiling((230 - 7)//4/4 - 1/2)**2) & Ne(x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2, 0) & Ne(ceiling((230 - 7)//4/4 - 1/2)**2 + 1, 1) & Ne(4*ceiling((230 - 7)//4/4 - 1/2)**2/49, 4) & Ne(96*ceiling((230 - 7)//4/4 - 1/2)**2/49, 16) & Ne(96*ceiling((230 - 7)//4/4 - 1/2)**2/49, 24) & Ne(16*ceiling((230 - 7)//4/4 - 1/2)**2, 0) & Ne(384*ceiling((230 - 7)//4/4 - 1/2)**2, 0) & (16*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2 >= 2) & (16*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2 > 0) & (64*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2 > 0) & (384*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2 > 0) & (1024*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2 > 0) & (1 < 384*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2) & (1 < 16*24*ceiling((230 - 7)//4/4 - 1/2)**2) & (6 < 384*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2) & (24 < 384*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2) & (384 < 384*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2) & Ne(4*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2, 0) & Ne(16*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2, 0) & Ne(64*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2, 0) & Ne(256*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2, 0) & Ne(384*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2, 0) & Ne(1024*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2, 0) & Ne(1536*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2, 0) & Ne(4096*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2, 0) & Eq(196*x.size()[0], x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2) & Eq(x.size()[0]*(Mod(ceiling((230 - 7)//4/4 - 1/2)**2, 196)), 0) & Eq(Mod(16, 4*ceiling((230 - 7)//4/4 - 1/2)**2/49), 0) & Eq(Mod(384, 96*ceiling((230 - 7)//4/4 - 1/2)**2/49), 0) & (x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2 + x.size()[0] >= 2) & (16*x.size()[0]*24*ceiling((230 - 7)//4/4 - 1/2)**2 > 0) & (16*x.size()[0]*96*ceiling((230 - 7)//4/4 - 1/2)**2 > 0) & (1536*ceiling((230 - 7)//4/4 - 1/2)**2 + 1536 > 1536) & (384*ceiling((230 - 7)//4/4 - 1/2)**2 + 384 > 384) & (1 < 16*x.size()[0]*48*ceiling((230 - 7)//4/4 - 1/2)**2) & (6 < 16*x.size()[0]*48*ceiling((230 - 7)//4/4 - 1/2)**2) & (24 < 16*x.size()[0]*48*ceiling((230 - 7)//4/4 - 1/2)**2) & (48 < 16*x.size()[0]*48*ceiling((230 - 7)//4/4 - 1/2)**2) & (768 < 16*x.size()[0]*48*ceiling((230 - 7)//4/4 - 1/2)**2) & (768 < 768*ceiling((230 - 7)//4/4 - 1/2)**2 + 768) & Ne(x.size()[0], x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2 + x.size()[0]) & Ne(x.size()[0]*(Mod(1, ceiling((230 - 7)//4/4 - 1/2)**2)), 0) & Ne(16*x.size()[0]*24*ceiling((230 - 7)//4/4 - 1/2)**2, 0) & Ne(64*x.size()[0]*24*ceiling((230 - 7)//4/4 - 1/2)**2, 0) & Ne(16*x.size()[0]*48*ceiling((230 - 7)//4/4 - 1/2)**2, 0) & Ne(64*x.size()[0]*48*ceiling((230 - 7)//4/4 - 1/2)**2, 0) & Ne(16*x.size()[0]*96*ceiling((230 - 7)//4/4 - 1/2)**2, 0) & Ne(64*x.size()[0]*96*ceiling((230 - 7)//4/4 - 1/2)**2, 0) & Ne(64*ceiling((230 - 7)//4/4 - 1/2)**2 + 64, 64) & Ne(384*ceiling((230 - 7)//4/4 - 1/2)**2 + 384, 384) & Ne(Mod(4, 4*ceiling((230 - 7)//4/4 - 1/2)**2/49), 0) & Ne(Mod(16, 96*ceiling((230 - 7)//4/4 - 1/2)**2/49), 0) & Eq(384*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2, 75264*x.size()[0]) & Ne(384*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2, 196*x.size()[0]) & Ne(384*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2, 4704*x.size()[0]) & Eq(16*x.size()[0]*24*ceiling((230 - 7)//4/4 - 1/2)**2, 75264*x.size()[0]) & ((230 - 7)//4**2 + 2*(230 - 7)//4 + 1 >= 0) & (1536*ceiling((230 - 7)//4/4 - 1/2)**2 + 1536 <= 1536*197) & (64*x.size()[0]*48//8*ceiling((230 - 7)//4/4 - 1/2)**2 > 0) & (768//12 < 768*ceiling((230 - 7)//4/4 - 1/2)**2 + 768) & Ne(x.size()[0]*(Mod(1, ceiling((230 - 7)//4/4 - 1/2)**2 + 1)), 0) & Ne(64*x.size()[0]*48//8*ceiling((230 - 7)//4/4 - 1/2)**2, 0) & Ne(128*x.size()[0]*48//8*ceiling((230 - 7)//4/4 - 1/2)**2, 0) & Ne(256*x.size()[0]*48//8*ceiling((230 - 7)//4/4 - 1/2)**2, 0) & Ne((230 - 7)//4**2 + 2*(230 - 7)//4 + 1, 0) & Ne((230 - 7)//4**2 + 2*(230 - 7)//4 + 1, 1) & Eq(96*x.size()[0]*(Mod(4*ceiling((230 - 7)//4/4 - 1/2)**2, 49)), 0) & Eq(4*x.size()[0]*(Mod(47*ceiling((230 - 7)//4/4 - 1/2)**2, 49)), 0) & (64 < x.size()[0]*768*ceiling((230 - 7)//4/4 - 1/2)**2 + x.size()[0]*768) & (384 < x.size()[0]*768*ceiling((230 - 7)//4/4 - 1/2)**2 + x.size()[0]*768) & (768 < x.size()[0]*768*ceiling((230 - 7)//4/4 - 1/2)**2 + x.size()[0]*768) & Ne(768//12*ceiling((230 - 7)//4/4 - 1/2)**2 + 768//12, 0) & Ne(768//12*ceiling((230 - 7)//4/4 - 1/2)**2 + 768//12, 1) & (24*(230 - 7)//4**2 + 2*24*(230 - 7)//4 + 24 >= 0) & (1 < 24*(230 - 7)//4**2 + 2*24*(230 - 7)//4 + 24) & Ne(24*(230 - 7)//4**2 + 2*24*(230 - 7)//4 + 24, 0) & (4*ceiling((230 - 7)//4/4 - 1/2) >= ceiling((230 - 7)//4/4 - 1/2)) & (ceiling((230 - 7)//4/4 - 1/2)**2 >= ceiling((230 - 7)//4/4 - 1/2)) & (1 < 6*768//12*ceiling((230 - 7)//4/4 - 1/2)**2 + 6*768//12) & Ne(4*ceiling((230 - 7)//4/4 - 1/2), ceiling((230 - 7)//4/4 - 1/2)) & Ne(ceiling((230 - 7)//4/4 - 1/2)**2, ceiling((230 - 7)//4/4 - 1/2)) & (6*x.size()[0]*768//12*ceiling((230 - 7)//4/4 - 1/2)**2 + 6*x.size()[0]*768//12 > 0) & (ceiling((230 - 7)//4/4 - 1/2) < 4*ceiling((230 - 7)//4/4 - 1/2)**2) & Ne((Mod(1, ceiling((230 - 7)//4/4 - 1/2)))*ceiling((230 - 7)//4/4 - 1/2), 0) & Ne(6*x.size()[0]*768//12*ceiling((230 - 7)//4/4 - 1/2)**2 + 6*x.size()[0]*768//12, 0) & Ne(12*x.size()[0]*768//12*ceiling((230 - 7)//4/4 - 1/2)**2 + 12*x.size()[0]*768//12, 0) & Ne(24*x.size()[0]*768//12*ceiling((230 - 7)//4/4 - 1/2)**2 + 24*x.size()[0]*768//12, 0) & Ne(x.size()[0]*24*(230 - 7)//4**2 + 2*x.size()[0]*24*(230 - 7)//4 + x.size()[0]*24, 0) & Ne(64*x.size()[0]*48*ceiling((230 - 7)//4/4 - 1/2)**2 - 4*48 + 16*48//8, 0) & Ne(64*x.size()[0]*48*ceiling((230 - 7)//4/4 - 1/2)**2 - 2*48 + 16*48//8, 0) & ((230 - 7)//4**2 + 2*(230 - 7)//4 + 1 >= (230 - 7)//4 + 1) & ((230 - 7)//4**2 + 2*(230 - 7)//4 + 1 > (230 - 7)//4 + 1) & (4*ceiling((230 - 7)//4/4 - 1/2) < 4*ceiling((230 - 7)//4/4 - 1/2)**2) & (4*ceiling((230 - 7)//4/4 - 1/2) < 16*ceiling((230 - 7)//4/4 - 1/2)**2) & Ne(4*x.size()[0]*24*(230 - 7)//4**2 + 8*x.size()[0]*24*(230 - 7)//4 + 4*x.size()[0]*24, 0) & Ne(384*ceiling((230 - 7)//4/4 - 1/2)**2, ceiling((230 - 7)//4/4 - 1/2)**2) & (4*ceiling((230 - 7)//4/4 - 1/2) < 16*24*ceiling((230 - 7)//4/4 - 1/2)**2) & (ceiling((230 - 7)//4/4 - 1/2)**2 < 16*24*ceiling((230 - 7)//4/4 - 1/2)**2) & (4*ceiling((230 - 7)//4/4 - 1/2)**2 < 16*ceiling((230 - 7)//4/4 - 1/2)**2) & (4*ceiling((230 - 7)//4/4 - 1/2)**2/49 < 384*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2) & (96*ceiling((230 - 7)//4/4 - 1/2)**2/49 < 384*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2) & (16*ceiling((230 - 7)//4/4 - 1/2)**2 < 16*24*ceiling((230 - 7)//4/4 - 1/2)**2) & (384*ceiling((230 - 7)//4/4 - 1/2)**2 < 384*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2) & Ne(x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2, 4*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2) & Ne(x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2, 16*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2) & Ne(384*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2, x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2) & Ne(ceiling((230 - 7)//4/4 - 1/2)**4 + 2*ceiling((230 - 7)//4/4 - 1/2)**2 + 1, 1) & Ne(384*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2, 16*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2) & Ne(384*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2, 64*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2) & Eq(768*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2, 16*x.size()[0]*48*ceiling((230 - 7)//4/4 - 1/2)**2) & (768*ceiling((230 - 7)//4/4 - 1/2)**2 + 768 < x.size()[0]*768*ceiling((230 - 7)//4/4 - 1/2)**2 + x.size()[0]*768) & Eq(768*x.size()[0]*ceiling((230 - 7)//4/4 - 1/2)**2 + 768*x.size()[0], x.size()[0]*768*ceiling((230 - 7)//4/4 - 1/2)**2 + x.size()[0]*768) & Eq(768*ceiling((230 - 7)//4/4 - 1/2)**2 + 768, 12*768//12*ceiling((230 - 7)//4/4 - 1/2)**2 + 12*768//12) & (24*(230 - 7)//4**2 + 2*24*(230 - 7)//4 + 24 >= (230 - 7)//4**2 + 2*(230 - 7)//4 + 1) & (24*(230 - 7)//4**2 + 2*24*(230 - 7)//4 + 24 > (230 - 7)//4**2 + 2*(230 - 7)//4 + 1) & (64*x.size()[0]*48*ceiling((230 - 7)//4/4 - 1/2)**2 - 4*48 + 16*48//8 <= 64*x.size()[0]*48*ceiling((230 - 7)//4/4 - 1/2)**2) & (64*x.size()[0]*48*ceiling((230 - 7)//4/4 - 1/2)**2 - 2*48 + 16*48//8 <= 64*x.size()[0]*48*ceiling((230 - 7)//4/4 - 1/2)**2) & (ceiling((230 - 7)//4/4 - 1/2)**4 + 2*ceiling((230 - 7)//4/4 - 1/2)**2 + 1 > ceiling((230 - 7)//4/4 - 1/2)**2 + 1) & (4*x.size()[0]*768*ceiling((230 - 7)//4/4 - 1/2)**2 + 4*x.size()[0]*768 - 4*768 + 24*768//12 <= 4*x.size()[0]*768*ceiling((230 - 7)//4/4 - 1/2)**2 + 4*x.size()[0]*768) & (4*x.size()[0]*768*ceiling((230 - 7)//4/4 - 1/2)**2 + 4*x.size()[0]*768 - 2*768 + 24*768//12 <= 4*x.size()[0]*768*ceiling((230 - 7)//4/4 - 1/2)**2 + 4*x.size()[0]*768) and x.stride()[2] == x.size()[3] == x.size()[2]) and
___check_tensors(x)NULL ERROR: /data/users/ezyang/pytorch-tmp/torch/csrc/dynamo/eval_frame.c:239
cuda train tnt_s_patch16_224 FAIL
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running twins_pcpvt_base...
cuda train twins_pcpvt_base FAIL (TIMEOUT)
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running visformer_small...
cuda train visformer_small PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running vit_base_patch16_224...
cuda train vit_base_patch16_224 PASS
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running volo_d1_224...
Traceback (most recent call last):
File "<string>", line 2, in <lambda>
NameError: name 'ceiling' is not defined
ERROR RUNNING GUARDS forward /home/ezyang/local/pytorch-tmp-env/lib/python3.9/site-packages/timm/models/volo.py:640
___guarded_code.valid and
___check_obj_id(self, 139639121794096) and
self.training == False and
(isinstance(x, torch.Tensor) and Eq(x.size()[0], 2) & Eq(192, 192) & Eq(384, 384) & (x.size()[0] <= 65535) & (x.size()[0] <= 880801) & (x.size()[0] <= 9223372036854775807) & Ne(x.size()[0], 9223372036854775807) & Ne(1152, 3) & Ne(768, 2) & Ne(486, 6) & Eq(9, 486//54) & Eq(32, 1152//36) & Eq(768//24, 32) & Eq(Mod(1152, 3), 0) & Eq(Mod(768, 2), 0) & Eq(Mod(486, 6), 0) & (384 >= 1152//36) & (1152 >= 1152//36) & (768 >= 768//24) & (486 >= 486//54) & (1152//36 >= 1) & (1152//36 >= 2) & (768//24 >= 1) & (768//24 >= 2) & (486//54 >= 1) & (486//54 >= 2) & (486 > 486//54) & (486//54 > 1) & (1 < 1152//36) & (1 < 768//24) & Ne(384, 1152//36) & Ne(1152/3, 1) & Ne(1152/3, 12) & Ne(768/2, 1) & Ne(768/2, 12) & Ne(486/6, 1) & Ne(486/6, 9) & Ne(1152//36, 0) & Ne(1152//36, 1) & Ne(768//24, 0) & Ne(768//24, 1) & Ne(486//54, 0) & Ne(486//54, 1) & Eq(1152, 36*1152//36) & Eq(768, 24*768//24) & Eq(486, 54*486//54) & Eq(12*1152//36, 384) & Eq(12*768//24, 384) & Eq(Mod(1152/3, 12), 0) & Eq(Mod(768/2, 12), 0) & Eq(Mod(486/6, 9), 0) & (1152 >= 12*1152//36) & (768 >= 12*768//24) & (486/6 >= 486//54) & (486/6 > 486//54) & (1 < 12*1152//36) & (1 < 12*768//24) & (1 < 54*486//54) & (1152//36 < 1152/3) & (768//24 < 768/2) & Ne(1152, 12*1152//36) & Ne(768, 12*768//24) & Ne(486, 9*486//54) & Ne(12*1152//36, 0) & Ne(12*768//24, 0) & Ne(9*486//54, 0) & Ne(9*486//54, 1) & (12*x.size()[0]*768//24 > 0) & (1 < 12*x.size()[0]*768//24) & (384 < 12*x.size()[0]*768//24) & Ne(12*x.size()[0]*768//24, 0) & Ne(48*x.size()[0]*768//24, 0) & Eq(1152/3, 12*1152//36) & Eq(768/2, 12*768//24) & Eq(486/6, 9*486//54) & Eq(12*(Mod(1152//36, 32)), 0) & Eq(12*(Mod(768//24, 32)), 0) & (9*486//54 > 486//54) & Ne(Mod(1, 12*768//24), 0) & Eq(384*x.size()[0], 12*x.size()[0]*768//24) & Ne(54*486//54, 9*486//54) & ((x.stride()[2] - 7 + 6)//2 + 1 >= 0) & ((x.stride()[2] - 7 + 6)//2 + 1 >= 1) & ((x.stride()[2] - 7 + 6)//2 + 1 >= 2) & ((x.stride()[2] - 7 + 6)//2 + 1 > 1) & Ne((x.stride()[2] - 7 + 6)//2 + 1, -1) & Ne((x.stride()[2] - 7 + 6)//2 + 1, 0) & Ne((x.stride()[2] - 7 + 6)//2 + 1, 1) & (-2*x.size()[1] + (x.stride()[2] - 7 + 6)//2 + 7 >= 0) & (-2*x.size()[1] + (x.stride()[2] - 7 + 6)//2 + 7 >= 1) & (-2*x.size()[1] + (x.stride()[2] - 7 + 6)//2 + 7 >= 2) & (-x.size()[1] + (x.stride()[2] - 7 + 6)//2 + 4 >= 0) & (-x.size()[1] + (x.stride()[2] - 7 + 6)//2 + 4 >= 1) & (-x.size()[1] + (x.stride()[2] - 7 + 6)//2 + 4 >= 2) & (-2*x.size()[1] + (x.stride()[2] - 7 + 6)//2 + 7 > 1) & (-x.size()[1] + (x.stride()[2] - 7 + 6)//2 + 4 > 1) & Ne(-2*x.size()[1] + (x.stride()[2] - 7 + 6)//2 + 7, -1) & Ne(-2*x.size()[1] + (x.stride()[2] - 7 + 6)//2 + 7, 0) & Ne(-2*x.size()[1] + (x.stride()[2] - 7 + 6)//2 + 7, 1) & Ne(-x.size()[1] + (x.stride()[2] - 7 + 6)//2 + 4, -1) & Ne(-x.size()[1] + (x.stride()[2] - 7 + 6)//2 + 4, 0) & Ne(-x.size()[1] + (x.stride()[2] - 7 + 6)//2 + 4, 1) & Eq((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1, 28) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1 >= 0) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1 >= 1) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1 >= 2) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 2 >= 0) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 2 >= 2) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 3 >= 0) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 3 >= 1) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 3 >= 2) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 4 >= 0) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1 >= 0) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1 >= 1) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1 >= 2) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1 > 0) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1 > 1) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 2 > 0) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 3 > 0) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 3 > 1) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1 > 0) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1 > 1) & Ne(0, (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 2) & Ne(1, (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 3) & Ne((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1, -1) & Ne((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1, 0) & Ne((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1, 1) & Ne((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 2, 1) & Ne((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 3, 0) & Ne((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1, -1) & Ne((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1, 0) & Ne((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1, 1) & Ne(Mod((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4, 2), 0) & (486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 486 >= 0) & (486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 486 >= 486) & (576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 576 >= 0) & (576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 576 >= 576) & (192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192 >= 0) & (192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192 >= 192) & (192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 192 >= 0) & (192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 192 >= 192) & ((x.stride()[2] - 7 + 6)//2**2 + 2*(x.stride()[2] - 7 + 6)//2 + 1 >= 0) & (-(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 - 3 <= 1) & (486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 486 > 486) & (576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 576 > 576) & (192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192 > 1) & (192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192 > 192) & ((x.stride()[2] - 7 + 6)//2**2 + 2*(x.stride()[2] - 7 + 6)//2 + 1 > 1) & Ne(486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 486, 0) & Ne(486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 486, 1) & Ne(576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 576, 0) & Ne(576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 576, 1) & Ne(192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192, 0) & Ne(192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192, 1) & Ne(192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 192, 0) & Ne(192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 192, 1) & Ne((x.stride()[2] - 7 + 6)//2**2 + 2*(x.stride()[2] - 7 + 6)//2 + 1, 0) & Ne((x.stride()[2] - 7 + 6)//2**2 + 2*(x.stride()[2] - 7 + 6)//2 + 1, 1) & (ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2) >= 0) & (ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2) >= 1) & (ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2) >= 2) & (ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2) > 1) & (0 < ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)) & Ne(0, ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)) & Ne(ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2), -1) & Ne(ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2), 1) & (64*(x.stride()[2] - 7 + 6)//2**2 + 2*64*(x.stride()[2] - 7 + 6)//2 + 64 >= 0) & (3*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2) >= 0) & (ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 >= 0) & (ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 >= 1) & (ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 >= 2) & (64*(x.stride()[2] - 7 + 6)//2**2 + 2*64*(x.stride()[2] - 7 + 6)//2 + 64 > 1) & Ne(0, 3*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)) & Ne(64*(x.stride()[2] - 7 + 6)//2**2 + 2*64*(x.stride()[2] - 7 + 6)//2 + 64, 0) & Ne(8*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2), 0) & Ne(24*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2), 0) & Ne(ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2, -1) & Ne(ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2, 0) & Ne(ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2, 1) & Eq(14, (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) & Eq((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1, 14) & ((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1 >= 0) & ((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1 >= 1) & ((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1 >= 2) & (3*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 >= 0) & (9*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 >= 0) & (9*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 >= 1) & (288*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 >= 0) & (1728*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 >= 0) & ((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1 > 1) & (1 < 288*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2) & (1 < 1728*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2) & Ne((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1, -1) & Ne((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1, 0) & Ne((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1, 1) & Ne(9*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2, 0) & Ne(9*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2, 1) & Ne(288*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2, 0) & Ne(288*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2, 1) & (1728*x.size()[0]*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 > 0) & (1 < 1728*x.size()[0]*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2) & Ne(1728*x.size()[0]*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2, 0) & Ne(6912*x.size()[0]*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2, 0) & (1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152 >= 0) & (1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152 >= 1152) & (384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384 >= 0) & (384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384 >= 384) & (1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152 > 1152) & (384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384 > 384) & Ne(1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152, 0) & Ne(1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152, 1) & Ne(384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384, 0) & Ne(384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384, 1) & Ne(x.size()[0]*64*(x.stride()[2] - 7 + 6)//2**2 + 2*x.size()[0]*64*(x.stride()[2] - 7 + 6)//2 + x.size()[0]*64, 0) & Eq(14*384, 384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384) & ((x.stride()[2] - 7 + 6)//2**2 + 2*(x.stride()[2] - 7 + 6)//2 + 1 >= (x.stride()[2] - 7 + 6)//2 + 1) & ((x.stride()[2] - 7 + 6)//2**2 + 2*(x.stride()[2] - 7 + 6)//2 + 1 > (x.stride()[2] - 7 + 6)//2 + 1) & Ne(4*x.size()[0]*64*(x.stride()[2] - 7 + 6)//2**2 + 8*x.size()[0]*64*(x.stride()[2] - 7 + 6)//2 + 4*x.size()[0]*64, 0) & (1 < 14*384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 14*384) & (12*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 12*1152//36 >= 0) & Ne(12*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 12*1152//36, 0) & Ne(12*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 12*1152//36, 1) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 3 >= (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 3) & (x.size()[1]**2 - 2*x.size()[1]*(x.stride()[2] - 7 + 6)//2 - 8*x.size()[1] + (x.stride()[2] - 7 + 6)//2**2 + 8*(x.stride()[2] - 7 + 6)//2 + 16 >= 0) & (x.size()[1]**2 - 2*x.size()[1]*(x.stride()[2] - 7 + 6)//2 - 8*x.size()[1] + (x.stride()[2] - 7 + 6)//2**2 + 8*(x.stride()[2] - 7 + 6)//2 + 16 > 1) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1 < (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 2) & (2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 < (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1) & Ne(x.size()[1]**2 - 2*x.size()[1]*(x.stride()[2] - 7 + 6)//2 - 8*x.size()[1] + (x.stride()[2] - 7 + 6)//2**2 + 8*(x.stride()[2] - 7 + 6)//2 + 16, 0) & Ne(x.size()[1]**2 - 2*x.size()[1]*(x.stride()[2] - 7 + 6)//2 - 8*x.size()[1] + (x.stride()[2] - 7 + 6)//2**2 + 8*(x.stride()[2] - 7 + 6)//2 + 16, 1) & (12*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 12*1152//36 >= 12*1152//36) & (4*x.size()[1]**2 - 4*x.size()[1]*(x.stride()[2] - 7 + 6)//2 - 28*x.size()[1] + (x.stride()[2] - 7 + 6)//2**2 + 14*(x.stride()[2] - 7 + 6)//2 + 49 >= 0) & (4*x.size()[1]**2 - 4*x.size()[1]*(x.stride()[2] - 7 + 6)//2 - 28*x.size()[1] + (x.stride()[2] - 7 + 6)//2**2 + 14*(x.stride()[2] - 7 + 6)//2 + 49 > 1) & Ne(4*x.size()[1]**2 - 4*x.size()[1]*(x.stride()[2] - 7 + 6)//2 - 28*x.size()[1] + (x.stride()[2] - 7 + 6)//2**2 + 14*(x.stride()[2] - 7 + 6)//2 + 49, 0) & Ne(4*x.size()[1]**2 - 4*x.size()[1]*(x.stride()[2] - 7 + 6)//2 - 28*x.size()[1] + (x.stride()[2] - 7 + 6)//2**2 + 14*(x.stride()[2] - 7 + 6)//2 + 49, 1) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1 >= 0) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1 >= 1) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 6*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 9 >= 0) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1 >= 0) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1 >= 2) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1 > 1) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1 > 0) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1 > 1) & (0 < (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1) & Ne((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1, 0) & Ne((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1, 1) & Ne((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 6*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 9, 0) & Ne((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 6*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 9, 1) & Ne((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1, -1) & Ne((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1, 0) & Ne((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1, 1) & (192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192 >= 192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192) & (192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 576 >= 192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 576) & (192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 192 >= 192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 192) & (192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 384*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192 >= 0) & (192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 384*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192 >= 192) & (192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 1152*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1728 >= 0) & (3*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 6*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 3 >= 0) & (9*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 18*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 9 >= 0) & (54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 108*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 54 >= 0) & (192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 384*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 192 >= 0) & (192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 384*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 192 >= 192) & (288*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 288 >= 0) & (288*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 288 >= 288) & (1728*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 3456*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1728 >= 0) & (1728*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 3456*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1728 >= 288) & (192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 384*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192 > 1) & (6*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 12*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 6 > 1) & (0 < 54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 108*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 54) & (1 < 192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 1152*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1728) & (1 < 54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 108*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 54) & (1 < 192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 384*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 192) & (1 < 288*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 288) & (1 < 1728*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 3456*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1728) & (9 < 9*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 18*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 9) & (32 < 288*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 288) & (32 < 1728*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 3456*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1728) & (288 < 288*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 288) & (288 < 1728*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 3456*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1728) & Ne(32, 288*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 288) & Ne((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1, ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2) & Ne(192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 384*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192, 0) & Ne(192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 384*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192, 1) & Ne(192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 1152*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1728, 0) & Ne(192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 1152*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1728, 1) & Ne(6*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 12*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 6, 0) & Ne(6*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 12*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 6, 1) & Ne(6*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 12*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 6, 6) & Ne(9*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 18*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 9, 0) & Ne(9*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 18*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 9, 1) & Ne(192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 384*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 192, 1) & Ne(288*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 288, 0) & Ne(288*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 288, 1) & Eq(x.size()[0]*(Mod((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4, 1)), 0) & Eq(x.size()[0]*(Mod((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8, 1)), 0) & Eq(6*(Mod((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8, 1)), 0) & (x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + x.size()[0] >= 0) & (x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + x.size()[0] >= 0) & (192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192 >= 0) & (486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 486 >= 0) & (486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 486 >= 486) & (576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 576 >= 0) & (576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 576 >= 576) & (486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 486 > 486) & (1 < 192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192) & (1 < 486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 486) & (1 < 576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 576) & Ne(x.size()[0], x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + x.size()[0]) & Ne(x.size()[0], x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + x.size()[0]) & Ne(x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + x.size()[0], -1) & Ne(x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + x.size()[0], 0) & Ne(x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + x.size()[0], 1) & Ne(x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + x.size()[0], -1) & Ne(x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + x.size()[0], 0) & Ne(x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + x.size()[0], 1) & Ne(486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 486, 0) & Ne(576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 576, 0) & (64*(x.stride()[2] - 7 + 6)//2**2 + 2*64*(x.stride()[2] - 7 + 6)//2 + 64 >= (x.stride()[2] - 7 + 6)//2**2 + 2*(x.stride()[2] - 7 + 6)//2 + 1) & (64*(x.stride()[2] - 7 + 6)//2**2 + 2*64*(x.stride()[2] - 7 + 6)//2 + 64 > (x.stride()[2] - 7 + 6)//2**2 + 2*(x.stride()[2] - 7 + 6)//2 + 1) & Ne(x.size()[0]*(Mod(1, (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)), 0) & Ne(x.size()[0]*(Mod(1, (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1)), 0) & Ne(Mod((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1, ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2), 0) & Eq(x.size()[0]*(Mod(6*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 12*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8, 1)), 0) & (486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 486 >= 486/6) & (3*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2) >= ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)) & (ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 >= ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)) & (486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 486 > 486/6) & Ne(6*x.size()[0]*(Mod(1, (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1)), 0) & Ne(3*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2), ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)) & Ne(ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2, ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)) & (6*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 12*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 6*x.size()[0] >= 0) & (x.size()[1]**2*64 - 2*x.size()[1]*64*(x.stride()[2] - 7 + 6)//2 - 8*x.size()[1]*64 + 64*(x.stride()[2] - 7 + 6)//2**2 + 8*64*(x.stride()[2] - 7 + 6)//2 + 16*64 >= 0) & (192*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 384*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192*x.size()[0] > 0) & (192*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 384*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 192*x.size()[0] > 0) & (1728*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 3456*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1728*x.size()[0] > 0) & (x.size()[1]**2*64 - 2*x.size()[1]*64*(x.stride()[2] - 7 + 6)//2 - 8*x.size()[1]*64 + 64*(x.stride()[2] - 7 + 6)//2**2 + 8*64*(x.stride()[2] - 7 + 6)//2 + 16*64 > 1) & (1 < 1728*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 3456*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1728*x.size()[0]) & Ne(x.size()[0], 6*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 12*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 6*x.size()[0]) & Ne(x.size()[0]*(Mod(1, 6*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 12*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 6)), 0) & Ne(4*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 8*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 4*x.size()[0], 0) & Ne(192*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 384*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192*x.size()[0], 0) & Ne(192*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 576*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 384*x.size()[0], 0) & Ne(192*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 768*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 576*x.size()[0], 0) & Ne(192*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 960*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1152*x.size()[0], 0) & Ne(192*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 1152*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1728*x.size()[0], 0) & Ne(768*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 1536*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 768*x.size()[0], 0) & Ne(768*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 4608*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 6912*x.size()[0], 0) & Ne(6*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 12*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 6*x.size()[0], -1) & Ne(6*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 12*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 6*x.size()[0], 0) & Ne(6*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 12*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 6*x.size()[0], 1) & Ne(54*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 108*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 54*x.size()[0], 0) & Ne(192*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 384*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 192*x.size()[0], 0) & Ne(216*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 432*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 216*x.size()[0], 0) & Ne(768*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 1536*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 768*x.size()[0], 0) & Ne(1728*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 3456*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1728*x.size()[0], 0) & Ne(6912*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 13824*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 6912*x.size()[0], 0) & Ne(x.size()[1]**2*64 - 2*x.size()[1]*64*(x.stride()[2] - 7 + 6)//2 - 8*x.size()[1]*64 + 64*(x.stride()[2] - 7 + 6)//2**2 + 8*64*(x.stride()[2] - 7 + 6)//2 + 16*64, 0) & (4*x.size()[1]**2*64 - 4*x.size()[1]*64*(x.stride()[2] - 7 + 6)//2 - 28*x.size()[1]*64 + 64*(x.stride()[2] - 7 + 6)//2**2 + 14*64*(x.stride()[2] - 7 + 6)//2 + 49*64 >= 0) & (8*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2) <= 8*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)) & (24*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2) <= 24*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)) & (x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + x.size()[0]*486 > 0) & (x.size()[0]*576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*x.size()[0]*576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + x.size()[0]*576 > 0) & (4*x.size()[1]**2*64 - 4*x.size()[1]*64*(x.stride()[2] - 7 + 6)//2 - 28*x.size()[1]*64 + 64*(x.stride()[2] - 7 + 6)//2**2 + 14*64*(x.stride()[2] - 7 + 6)//2 + 49*64 > 1) & (1 < x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + x.size()[0]*486) & (9 < x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + x.size()[0]*486) & (81 < x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + x.size()[0]*486) & (486 < x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + x.size()[0]*486) & (ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2) < 3*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2) & Ne((Mod(1, ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)))*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2), 0) & Ne(x.size()[0]*192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*x.size()[0]*192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + x.size()[0]*192, 0) & Ne(x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + x.size()[0]*486, 0) & Ne(x.size()[0]*576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*x.size()[0]*576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + x.size()[0]*576, 0) & Ne(4*x.size()[1]**2*64 - 4*x.size()[1]*64*(x.stride()[2] - 7 + 6)//2 - 28*x.size()[1]*64 + 64*(x.stride()[2] - 7 + 6)//2**2 + 14*64*(x.stride()[2] - 7 + 6)//2 + 49*64, 0) & (x.size()[1]**2 - 2*x.size()[1]*(x.stride()[2] - 7 + 6)//2 - 8*x.size()[1] + (x.stride()[2] - 7 + 6)//2**2 + 8*(x.stride()[2] - 7 + 6)//2 + 16 >= -x.size()[1] + (x.stride()[2] - 7 + 6)//2 + 4) & (3*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 >= ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2) & (9*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 >= ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2) & (x.size()[1]**2 - 2*x.size()[1]*(x.stride()[2] - 7 + 6)//2 - 8*x.size()[1] + (x.stride()[2] - 7 + 6)//2**2 + 8*(x.stride()[2] - 7 + 6)//2 + 16 > -x.size()[1] + (x.stride()[2] - 7 + 6)//2 + 4) & (3*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2) < 3*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2) & (3*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2) < 9*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2) & (3*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2) < 1728*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2) & (ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 < 288*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2) & (ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 < 1728*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2) & Ne(4*x.size()[0]*192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 8*x.size()[0]*192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 4*x.size()[0]*192, 0) & Ne(4*x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 8*x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 4*x.size()[0]*486, 0) & Ne(4*x.size()[0]*576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 8*x.size()[0]*576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 4*x.size()[0]*576, 0) & (ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 < 1728*x.size()[0]*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2) & (4*x.size()[1]**2 - 4*x.size()[1]*(x.stride()[2] - 7 + 6)//2 - 28*x.size()[1] + (x.stride()[2] - 7 + 6)//2**2 + 14*(x.stride()[2] - 7 + 6)//2 + 49 >= -2*x.size()[1] + (x.stride()[2] - 7 + 6)//2 + 7) & (4*x.size()[1]**2 - 4*x.size()[1]*(x.stride()[2] - 7 + 6)//2 - 28*x.size()[1] + (x.stride()[2] - 7 + 6)//2**2 + 14*(x.stride()[2] - 7 + 6)//2 + 49 > -2*x.size()[1] + (x.stride()[2] - 7 + 6)//2 + 7) & (3*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 < 9*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2) & (9*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 < 288*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2) & (9*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 < 1728*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2) & (288*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 < 1728*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2) & (9*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 18*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 9*486//54 >= 0) & (54*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 108*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 54*486//54 >= 0) & (1 < 54*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 108*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 54*486//54) & (9*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 < 1728*x.size()[0]*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2) & (288*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 < 1728*x.size()[0]*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2) & (1728*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 < 1728*x.size()[0]*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2) & Ne(9*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 18*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 9*486//54, 0) & Ne(9*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 18*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 9*486//54, 1) & Ne(54*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 108*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 54*486//54, 0) & Ne(x.size()[0]*x.size()[1]**2*64 - 2*x.size()[0]*x.size()[1]*64*(x.stride()[2] - 7 + 6)//2 - 8*x.size()[0]*x.size()[1]*64 + x.size()[0]*64*(x.stride()[2] - 7 + 6)//2**2 + 8*x.size()[0]*64*(x.stride()[2] - 7 + 6)//2 + 16*x.size()[0]*64, 0) & (6912*x.size()[0]*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 <= 6912*x.size()[0]*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2) & Ne(4*x.size()[0]*x.size()[1]**2*64 - 4*x.size()[0]*x.size()[1]*64*(x.stride()[2] - 7 + 6)//2 - 28*x.size()[0]*x.size()[1]*64 + x.size()[0]*64*(x.stride()[2] - 7 + 6)//2**2 + 14*x.size()[0]*64*(x.stride()[2] - 7 + 6)//2 + 49*x.size()[0]*64, 0) & Eq((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1, 196) & Eq((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2, 197) & ((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1 >= 0) & ((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1 >= 1) & ((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1 >= 2) & ((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2 >= 0) & ((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2 >= 1) & ((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2 >= 2) & ((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2 <= 9223372036854775807) & ((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1 > 1) & ((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2 > 0) & ((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2 > 1) & (0 < (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) & Ne((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1, -1) & Ne((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1, 0) & Ne((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1, 1) & Ne((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2, -1) & Ne((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2, 0) & Ne((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2, 1) & Ne(4*x.size()[0]*x.size()[1]**2*64 - 8*x.size()[0]*x.size()[1]*64*(x.stride()[2] - 7 + 6)//2 - 32*x.size()[0]*x.size()[1]*64 + 4*x.size()[0]*64*(x.stride()[2] - 7 + 6)//2**2 + 32*x.size()[0]*64*(x.stride()[2] - 7 + 6)//2 + 64*x.size()[0]*64, 0) & Ne(16*x.size()[0]*x.size()[1]**2*64 - 16*x.size()[0]*x.size()[1]*64*(x.stride()[2] - 7 + 6)//2 - 112*x.size()[0]*x.size()[1]*64 + 4*x.size()[0]*64*(x.stride()[2] - 7 + 6)//2**2 + 56*x.size()[0]*64*(x.stride()[2] - 7 + 6)//2 + 196*x.size()[0]*64, 0) & (54*x.size()[0]*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 108*x.size()[0]*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 54*x.size()[0]*486//54 > 0) & Ne(54*x.size()[0]*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 108*x.size()[0]*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 54*x.size()[0]*486//54, 0) & Ne(216*x.size()[0]*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 432*x.size()[0]*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 216*x.size()[0]*486//54, 0) & (9*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 18*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 9*486//54 >= 9*486//54) & (54*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 108*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 54*486//54 >= 9*486//54) & (12*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 12 >= 0) & (12*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 24 >= 0) & (384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384 >= 0) & (384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384 >= 384) & (384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 768 >= 0) & (384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 768 >= 384) & (9*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 18*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 9*486//54 > 9*486//54) & (384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 768 > 384) & (0 < 12*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 12) & (1 < 12*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 24) & (1 < 384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384) & (1 < 384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 768) & (9*486//54 < 54*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 108*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 54*486//54) & (54*486//54 < 54*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 108*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 54*486//54) & Ne(1, 384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384) & Ne(12*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 24, 0) & Ne(384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384, 0) & Ne(384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 768, 0) & Ne(384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 768, 384) & Eq(x.size()[0]*(Mod((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2, 1)), 0) & (x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + x.size()[0] >= 0) & (1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152 >= 0) & (1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152 >= 1152) & (1000*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*1000*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1000 >= 0) & (1000*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*1000*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1000 >= 1000) & (384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384 >= 0) & (1000*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*1000*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1000 > 1000) & (1 < 1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152) & (1 < 384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384) & (1152 < 1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152) & Ne(0, 384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384) & Ne(x.size()[0], x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + x.size()[0]) & Ne(x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + x.size()[0], -1) & Ne(x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + x.size()[0], 0) & Ne(x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + x.size()[0], 1) & Ne(1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152, 0) & Ne(1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152, 1) & Ne(1000*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*1000*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1000, 0) & Ne(4*x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 8*x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 4*x.size()[0]*486 - 2*486/3 + 36*486//54, 0) & Eq(384*x.size()[0]*(Mod((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2, 1)), 0) & Ne(x.size()[0]*(Mod(1, (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1)), 0) & Ne(x.size()[0]*(Mod(1, (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2)), 0) & (x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2*x.size()[0] >= 0) & (1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152 >= 1152/3) & (768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2*768 >= 0) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1 >= (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 6*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 9 >= (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 3) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1 >= (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1 > (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 6*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 9 > (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 3) & (1 < 768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2*768) & (768 < 768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2*768) & (1152//36 < 1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152) & Ne(x.size()[0], x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2*x.size()[0]) & Ne((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1, (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1) & Ne((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1, (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1) & Ne(x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2*x.size()[0], -1) & Ne(x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2*x.size()[0], 0) & Ne(x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2*x.size()[0], 1) & Ne(768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2*768, 0) & Ne(768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2*768, 1) & Ne(4*14*384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 4*384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 4*384, 0) & (12*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 24*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 24*x.size()[0] > 0) & (384*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 768*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384*x.size()[0] > 0) & (384*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 768*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 768*x.size()[0] > 0) & Ne(4*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 4*x.size()[0], 0) & Ne(4*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 8*x.size()[0], 0) & Ne(12*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 24*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 12*x.size()[0], 0) & Ne(12*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 24*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 24*x.size()[0], 0) & Ne(48*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 96*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 48*x.size()[0], 0) & Ne(48*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 96*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 96*x.size()[0], 0) & Ne(384*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 768*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384*x.size()[0], 0) & Ne(384*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 768*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 768*x.size()[0], 0) & Ne(1536*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 3072*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1536*x.size()[0], 0) & Ne(1536*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 3072*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 3072*x.size()[0], 0) & (768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2*768 >= 768/2) & (192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 384*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192 >= (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1) & (x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + x.size()[0]*1152 > 0) & (192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 384*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192 > (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1) & (1 < x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + x.size()[0]*1152) & (32 < x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + x.size()[0]*1152) & (384 < x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + x.size()[0]*1152) & (1152 < x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + x.size()[0]*1152) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1 < 3*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 6*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 3) & (768//24 < 768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2*768) & Ne(x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + x.size()[0]*1152, 0) & Ne(x.size()[0]*1000*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*x.size()[0]*1000*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + x.size()[0]*1000, 0) & Ne(x.size()[0]*384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*x.size()[0]*384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + x.size()[0]*384, 0) & Ne(1536*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 3072*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 3072*x.size()[0] - 1536, 0) & (1 < x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2*x.size()[0]*768) & (32 < x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2*x.size()[0]*768) & (384 < x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2*x.size()[0]*768) & (768 < x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2*x.size()[0]*768) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1 < 192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192) & Ne(384*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 768*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384*x.size()[0], 384*x.size()[0]) & Ne(x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2*x.size()[0]*768, 0) & (1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152//36 >= 0) & (192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 384*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192 >= 192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192) & (192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 384*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192 > 192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192) & (1 < 1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152//36) & Ne(1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152//36, 0) & Ne(1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152//36, 1) & Ne(4*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 4*x.size()[0]*1152, 0) & Ne(4*x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 8*x.size()[0]*768, 0) & Ne(4*x.size()[0]*1000*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*1000*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 4*x.size()[0]*1000, 0) & Ne(4*x.size()[0]*384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 4*x.size()[0]*384, 0) & Eq((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1, ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2) & (486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 486 >= 486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 486) & (576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 576 >= 576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 576) & (ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 >= (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1) & (4*14*384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 4*384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 4*384 <= 4*14**2*384) & (486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 486 > 486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 486) & (576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 576 > 576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 576) & (1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152//36 >= 1152//36) & (768//24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*768//24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2*768//24 >= 0) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1 < 1728*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 3456*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1728*x.size()[0]) & Ne(1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152//36, 1152//36) & Ne(768//24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*768//24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2*768//24, 0) & Ne(768//24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*768//24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2*768//24, 1) & Eq(Mod((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1, ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2), 0) & (12*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 24*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 12*1152//36 >= 0) & (1 < 12*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 24*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 12*1152//36) & (1 < 12*768//24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 24*768//24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 24*768//24) & Ne(768*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 4608*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 6912*x.size()[0] - 8*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 - 32, 0) & Ne(768*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 4608*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 6912*x.size()[0] - 8*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 - 28, 0) & Ne(768*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 4608*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 6912*x.size()[0] - 8*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 - 24, 0) & Ne(768*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 4608*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 6912*x.size()[0] - 4*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 - 12, 0) & Eq(9*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2, 9*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 18*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 9) & Eq(288*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2, 288*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 288) & Eq(1728*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2, 1728*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 3456*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1728) & (1152//36 < 12*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 24*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 12*1152//36) & (12*x.size()[0]*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 24*x.size()[0]*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 12*x.size()[0]*1152//36 > 0) & (12*x.size()[0]*768//24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 24*x.size()[0]*768//24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 24*x.size()[0]*768//24 > 0) & Ne(12*x.size()[0]*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 24*x.size()[0]*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 12*x.size()[0]*1152//36, 0) & Ne(36*x.size()[0]*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 72*x.size()[0]*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 36*x.size()[0]*1152//36, 0) & Ne(48*x.size()[0]*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 96*x.size()[0]*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 48*x.size()[0]*1152//36, 0) & Ne(12*x.size()[0]*768//24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 24*x.size()[0]*768//24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 24*x.size()[0]*768//24, 0) & Ne(24*x.size()[0]*768//24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 48*x.size()[0]*768//24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 48*x.size()[0]*768//24, 0) & Ne(48*x.size()[0]*768//24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 96*x.size()[0]*768//24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 96*x.size()[0]*768//24, 0) & (12*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 24*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 12*1152//36 >= 12*1152//36) & Ne(4*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 4*x.size()[0]*1152 - 4*1152 + 48*1152//36, 0) & Ne(4*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 4*x.size()[0]*1152 - 4*1152/3 + 48*1152//36, 0) & Ne(4*x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 8*x.size()[0]*768 - 4*768 + 48*768//24, 0) & Ne(4*x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 8*x.size()[0]*768 - 2*768 + 48*768//24, 0) & Eq(1728*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 3456*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1728*x.size()[0], 1728*x.size()[0]*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2) & (486*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 < x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + x.size()[0]*486) & Eq(486*x.size()[0]*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2, x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + x.size()[0]*486) & ((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1 >= (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) & ((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1 > (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) & Ne((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1, (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1 >= (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1) & Ne((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 6*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 9, (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 4*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 3) & Ne((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 6*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 9, (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 5*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 6) & (384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384 >= (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) & (384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384 > (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) & (384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384 >= (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) & (192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 384*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192 >= (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1) & (192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 1152*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1728 >= (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 6*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 9) & (9*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 18*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 9 >= (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1) & (288*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 288 >= (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1) & (384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384 > (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) & (192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 384*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192 > (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1) & (192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 1152*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1728 > (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 6*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 9) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1 < 3*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 6*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 3) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1 < 9*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 18*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 9) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1 < 1728*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 3456*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1728) & (384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384 >= 384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384) & (x.size()[1]**2*64 - 2*x.size()[1]*64*(x.stride()[2] - 7 + 6)//2 - 8*x.size()[1]*64 + 64*(x.stride()[2] - 7 + 6)//2**2 + 8*64*(x.stride()[2] - 7 + 6)//2 + 16*64 >= x.size()[1]**2 - 2*x.size()[1]*(x.stride()[2] - 7 + 6)//2 - 8*x.size()[1] + (x.stride()[2] - 7 + 6)//2**2 + 8*(x.stride()[2] - 7 + 6)//2 + 16) & (384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384 > 384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384) & (x.size()[1]**2*64 - 2*x.size()[1]*64*(x.stride()[2] - 7 + 6)//2 - 8*x.size()[1]*64 + 64*(x.stride()[2] - 7 + 6)//2**2 + 8*64*(x.stride()[2] - 7 + 6)//2 + 16*64 > x.size()[1]**2 - 2*x.size()[1]*(x.stride()[2] - 7 + 6)//2 - 8*x.size()[1] + (x.stride()[2] - 7 + 6)//2**2 + 8*(x.stride()[2] - 7 + 6)//2 + 16) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1 < 192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 2*192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192) & (1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152 >= 1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152) & (192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 384*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192 >= 192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 384*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 192) & (192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 1152*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1728 >= 192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 1152*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1728) & (192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 384*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 192 >= 192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 384*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 192) & (1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152 > 1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152) & (3*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 6*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 3 < 9*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 18*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 9) & (9*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 18*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 9 < 54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 108*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 54) & (9*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 18*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 9 < 1728*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 3456*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1728) & (288*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 288 < 1728*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 3456*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1728) & (4*x.size()[1]**2*64 - 4*x.size()[1]*64*(x.stride()[2] - 7 + 6)//2 - 28*x.size()[1]*64 + 64*(x.stride()[2] - 7 + 6)//2**2 + 14*64*(x.stride()[2] - 7 + 6)//2 + 49*64 >= 4*x.size()[1]**2 - 4*x.size()[1]*(x.stride()[2] - 7 + 6)//2 - 28*x.size()[1] + (x.stride()[2] - 7 + 6)//2**2 + 14*(x.stride()[2] - 7 + 6)//2 + 49) & (4*x.size()[1]**2*64 - 4*x.size()[1]*64*(x.stride()[2] - 7 + 6)//2 - 28*x.size()[1]*64 + 64*(x.stride()[2] - 7 + 6)//2**2 + 14*64*(x.stride()[2] - 7 + 6)//2 + 49*64 > 4*x.size()[1]**2 - 4*x.size()[1]*(x.stride()[2] - 7 + 6)//2 - 28*x.size()[1] + (x.stride()[2] - 7 + 6)//2**2 + 14*(x.stride()[2] - 7 + 6)//2 + 49) & ((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1 < 1728*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 3456*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1728*x.size()[0]) & (3*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 6*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 3 < 1728*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 3456*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1728*x.size()[0]) & (9*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 18*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 9 < 1728*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 3456*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1728*x.size()[0]) & (1728*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 3456*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1728 < 1728*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 3456*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1728*x.size()[0]) & (4*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 8*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 4*x.size()[0] <= 4*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 8*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 4*x.size()[0]) & (768*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 1536*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 768*x.size()[0] <= 768*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 1536*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 768*x.size()[0]) & (768*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 4608*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 6912*x.size()[0] <= 768*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 4608*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 6912*x.size()[0]) & (216*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 432*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 216*x.size()[0] <= 216*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 432*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 216*x.size()[0]) & (768*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 1536*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 768*x.size()[0] <= 768*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 1536*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 768*x.size()[0]) & (6912*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 13824*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 6912*x.size()[0] <= 6912*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 13824*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 6912*x.size()[0]) & Eq(486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 2*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 486, 54*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 108*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 54*486//54) & Ne(6912*x.size()[0]*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 + 4*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 8*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 - 4*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 + 4, 0) & (4*x.size()[0]*192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 8*x.size()[0]*192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 4*x.size()[0]*192 <= 4*x.size()[0]*192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 8*x.size()[0]*192*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 4*x.size()[0]*192) & (4*x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 8*x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 4*x.size()[0]*486 <= 4*x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 8*x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 4*x.size()[0]*486) & (4*x.size()[0]*576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 8*x.size()[0]*576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 4*x.size()[0]*576 <= 4*x.size()[0]*576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 8*x.size()[0]*576*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 4*x.size()[0]*576) & (54*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 108*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 54*486//54 >= 9*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 18*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 9*486//54) & (54*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 108*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 54*486//54 > 9*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 18*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 9*486//54) & (4*x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 8*x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 4*x.size()[0]*486 - 2*486/3 + 36*486//54 <= 4*x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 8*x.size()[0]*486*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 4*x.size()[0]*486) & ((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1 >= (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) & ((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2 >= (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2) & ((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2 <= (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2) & (12*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 24 >= (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2) & (384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384 >= (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) & (216*x.size()[0]*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 432*x.size()[0]*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 216*x.size()[0]*486//54 <= 216*x.size()[0]*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 432*x.size()[0]*486//54*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 216*x.size()[0]*486//54) & (12*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 24 > (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2) & (384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384 > (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) & ((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1 < 12*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 12) & Ne(384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384, (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) & (384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384 >= (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) & ((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**4 + 4*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**3 + 6*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 4*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1 >= 0) & (384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384 > (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) & Ne((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**4 + 4*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**3 + 6*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 4*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1, 0) & Ne((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**4 + 4*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**3 + 6*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 4*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1, 1) & (12*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**4 + 48*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**3 + 72*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 48*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 12 >= 0) & (768*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 4608*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 6912*x.size()[0] - 4*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 - 16 <= 768*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 4608*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 6912*x.size()[0]) & (768*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 4608*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 6912*x.size()[0] - 4*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 - 12 <= 768*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2 + 4608*x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 6912*x.size()[0]) & (1 < 12*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**4 + 48*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**3 + 72*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 48*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 12) & Ne(12*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**4 + 48*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**3 + 72*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 48*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 12, 0) & Ne(1536*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 3072*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 3072*x.size()[0] - 1536*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 - 3072*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 - 1536, 0) & (1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2304*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152 < x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + x.size()[0]*1152) & (768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 1536*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1536 < x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2*x.size()[0]*768) & (12*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**4 + 48*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**3 + 72*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 48*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 12*x.size()[0] > 0) & Ne(12*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**4 + 48*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**3 + 72*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 48*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 12*x.size()[0], 0) & Ne(48*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**4 + 192*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**3 + 288*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 192*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 48*x.size()[0], 0) & (4*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 4*x.size()[0] <= 4*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 4*x.size()[0]) & (4*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 8*x.size()[0] <= 4*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 8*x.size()[0]) & (48*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 96*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 48*x.size()[0] <= 48*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 96*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 48*x.size()[0]) & (48*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 96*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 96*x.size()[0] <= 48*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 96*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 96*x.size()[0]) & (1536*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 3072*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1536*x.size()[0] <= 1536*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 3072*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1536*x.size()[0]) & (1536*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 3072*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 3072*x.size()[0] <= 1536*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 3072*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 3072*x.size()[0]) & Eq(1152*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2304*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152*x.size()[0], x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + x.size()[0]*1152) & Eq(768*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 1536*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1536*x.size()[0], x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2*x.size()[0]*768) & Eq(1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152, 36*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 72*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 36*1152//36) & (6912*x.size()[0]*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 + 4*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2 + 8*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 - 4*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2 + 4 <= 6912*x.size()[0]*ceiling((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/2 + 1/2)**2) & Eq(768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2*768, 24*768//24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 48*768//24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 48*768//24) & (4*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 4*x.size()[0]*1152 <= 4*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 4*x.size()[0]*1152) & (4*x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 8*x.size()[0]*768 <= 4*x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 8*x.size()[0]*768) & (4*x.size()[0]*1000*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*1000*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 4*x.size()[0]*1000 <= 4*x.size()[0]*1000*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*1000*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 4*x.size()[0]*1000) & (4*x.size()[0]*384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 4*x.size()[0]*384 <= 4*x.size()[0]*384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*384*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 4*x.size()[0]*384) & Eq(12*x.size()[0]*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 24*x.size()[0]*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 12*x.size()[0]*1152//36, 384*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 768*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 384*x.size()[0]) & (12*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 24*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 12*1152//36 >= 1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152//36) & (1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1152//36 < 12*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 24*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 12*1152//36) & (12*768//24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 24*768//24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 24*768//24 >= 768//24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*768//24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 2*768//24) & (4*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 4*x.size()[0]*1152 - 4*1152 + 48*1152//36 <= 4*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 4*x.size()[0]*1152) & (4*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 4*x.size()[0]*1152 - 8*1152/3 + 48*1152//36 <= 4*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 4*x.size()[0]*1152) & (4*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 4*x.size()[0]*1152 - 4*1152/3 + 48*1152//36 <= 4*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*1152*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 4*x.size()[0]*1152) & (4*x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 8*x.size()[0]*768 - 4*768 + 48*768//24 <= 4*x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 8*x.size()[0]*768) & (4*x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 8*x.size()[0]*768 - 2*768 + 48*768//24 <= 4*x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 8*x.size()[0]*768*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 8*x.size()[0]*768) & (48*x.size()[0]*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 96*x.size()[0]*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 48*x.size()[0]*1152//36 <= 48*x.size()[0]*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 96*x.size()[0]*1152//36*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 48*x.size()[0]*1152//36) & (48*x.size()[0]*768//24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 96*x.size()[0]*768//24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 96*x.size()[0]*768//24 <= 48*x.size()[0]*768//24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 96*x.size()[0]*768//24*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 96*x.size()[0]*768//24) & ((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**4 + 4*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**3 + 6*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 4*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1 >= (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) & ((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**4 + 4*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**3 + 6*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 4*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1 > (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) & (1536*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 3072*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 3072*x.size()[0] - 1536*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 - 3072*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 - 1536 <= 1536*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 3072*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 3072*x.size()[0]) & (12*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**4 + 48*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**3 + 72*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 48*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 12 >= (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**4 + 4*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**3 + 6*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 4*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) & (12*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**4 + 48*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**3 + 72*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 48*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 12 > (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**4 + 4*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**3 + 6*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 4*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) & Eq((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4*(Mod((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2/((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1) + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1) + 1/((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1), 1)) + Mod((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2/((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1) + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1) + 1/((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1), 1), 0) & Eq((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8*(Mod((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2/((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1) + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8/((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1) + 1/((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1), 1)) + Mod((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2/((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1) + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8/((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1) + 1/((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1), 1), 0) & (48*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**4 + 192*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**3 + 288*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 192*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 48*x.size()[0] <= 48*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**4 + 192*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**3 + 288*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2 + 192*x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 48*x.size()[0]) & Ne(x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4*(Mod(1, (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2/((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1) + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1) + 1/((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1))) + x.size()[0]*(Mod(1, (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4**2/((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1) + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4/((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1) + 1/((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1))), 0) & Ne(x.size()[0]*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8*(Mod(1, (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2/((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1) + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8/((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1) + 1/((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1))) + x.size()[0]*(Mod(1, (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8**2/((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1) + 2*(-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8/((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1) + 1/((-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//8 + 1))), 0) & Eq((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2*(Mod((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2/((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2/((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) + 1/((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1), 1)) + Mod((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2/((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2/((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) + 1/((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1), 1), 0) & Ne((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2*(Mod(1, (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2/((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2/((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) + 1/((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1))) + Mod(1, (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2/((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2/((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) + 1/((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1)), 0) & Ne(x.size()[0]*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2*(Mod(1, (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2/((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2/((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) + 1/((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1))) + x.size()[0]*(Mod(1, (-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2**2/((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) + 2*(-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2/((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1) + 1/((-x.size()[0] + (-2*x.size()[1] - 4 + (x.stride()[2] - 7 + 6)//2 + 7)//4 + 1)//2 + 1))), 0) and x.stride()[2] == x.size()[3] == x.size()[2]) and
___check_tensors(x)NULL ERROR: /data/users/ezyang/pytorch-tmp/torch/csrc/dynamo/eval_frame.c:239
cuda train volo_d1_224 FAIL
/data/users/ezyang/vision/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension:
warn(f"Failed to load image Python extension: {e}")
Running xcit_large_24_p8_224...
WARNING:common:fp64 golden ref were not generated for xcit_large_24_p8_224
cuda train xcit_large_24_p8_224 PASS
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