isVoid

cmake_minimum_required(VERSION 3.10)
project(ClangToolingExample)

# libastcanopy shared lib targget
find_package(Clang REQUIRED)

# Add include directories
include_directories(${CLANG_INCLUDE_DIRS})

# Add the source files for your Clang tool

isVoid

import os
import pkgutil
import importlib
from inspect import getmembers, isfunction
import cuml
import json

import pprint

pp = pprint.PrettyPrinter(indent=4)

isVoid

isVoid

from dataclasses import dataclass
import subprocess

benchmark_bin = "/home/coder/cuspatial/cpp/build/release/benchmarks/LINESTRING_DISTANCES_BENCH"

attr_names = ["NumPairs",
              "NumLineStringsPerMultiLineString",
              "NumSegmentsPerLineString"]

@dataclass

isVoid

isVoid

isVoid

isVoid

import cupy as cp
import pandas as pd
import cudf

def make_non_monotonic_non_unique_multiindex(N):
    half_n = int(N / 2)
    l0 = cp.concatenate([cp.arange(0, half_n), cp.full(half_n, 42)])
    l1 = cp.concatenate([cp.arange(half_n, N), cp.full(half_n, 42)])
    random_order = cp.arange(0, N)
    cp.random.shuffle(random_order)

isVoid

import cupy as cp
import pandas as pd
import cudf
import time

def make_non_monotonic_non_unique_multiindex(N):
    half_n = int(N / 2)
    l0 = cp.concatenate([cp.arange(0, half_n), cp.full(half_n, 42)])
    l1 = cp.concatenate([cp.arange(half_n, N), cp.full(half_n, 42)])
    random_order = cp.arange(0, N)

isVoid

Namespace(activation_fn='gelu', adam_betas='(0.9, 0.999)', adam_eps=1e-06, adaptive_softmax_cutoff=None, adaptive_softmax_dropout=0, arch='bart_large', attention_dropout=0.1, best_checkpoint_metric='loss', bilm_add_bos=False, bilm_attention_dropout=0.0, bilm_mask_last_state=False, bilm_model_dropout=0.1, bilm_relu_dropout=0.0, bpe='gpt2', bucket_cap_mb=25, clip_norm=0.1, cpu=False, criterion='cross_entropy', cross_self_attention=False, curriculum=0, data='[PATH TO PRETRAINED WEIGHTS]', dataset_impl='mmap', ddp_backend='c10d', decoder_attention_heads=16, decoder_embed_dim=1024, decoder_embed_path=None, decoder_embed_scale=1.0, decoder_ffn_embed_dim=4096, decoder_input_dim=1024, decoder_layerdrop=0, decoder_layers=12, decoder_layers_to_keep=None, decoder_learned_pos=True, decoder_normalize_before=False, decoder_output_dim=1024, decoder_to_encoder_attention_layers=12, device_id=0, disable_validation=False, distributed_backend='nccl', distributed_init_method='tcp://learnfair1688:55498', distributed_no_spawn=True,