Elad Nachmias eladn

## subsets_batch_sampler.py
__author__ = "Elad Nachmias"
__email__ = "eladnah@gmail.com"
__date__ = "2023-10-18"

import dataclasses
import enum
import itertools
import warnings
from collections import defaultdict, Counter
from typing import Optional, Dict, List, Sequence, Iterable, Union, Set, Tuple

## deterministic_structured_hash.py
__author__ = "Elad Nachmias"
__email__ = "eladnah@gmail.com"
__date__ = "2023-10-18"

import base64
import functools
import hashlib
from typing import Any, Set, Optional, NamedTuple, Dict
import dataclasses

## timeout.py
__author__ = "Elad Nachmias"
__email__ = "eladnah@gmail.com"
__date__ = "2023-10-18"

import ctypes
import math
import signal
import threading
import time
import traceback

## find_permutations.cpp
#include <iostream>
#include <sstream>

using namespace std;

void permutations(string str) {
    // factorials[i] = i! for i in [0..|str|]
    uint64_t factorials[str.length() + 1];
    factorials[0] = 1;
    for(int i = 1; i <= str.length(); i++) {

## pretify_time.py
import math

__all__ = ['format_time', 'find_pretty_time_fmt', 'prettify_time']

possible_time_fmts = ('secs', 'mins', 'hours', 'days', 'months', 'years', 'thousand years', 'million years')
time_fmts_divisors = {
    'secs': 1, 'mins': 60, 'hours': 60*60, 'days': 60*60*24, 'months': 60*60*24*30,
    'years': 60*60*24*365, 'thousand years': 60*60*24*365*1_000,
    'million years': 60*60*24*365*1_000_000}
assert set(time_fmts_divisors.keys()) == set(possible_time_fmts)

## scatter_attention.py
import torch
import torch.nn as nn
from typing import Optional, Tuple
from torch_scatter import scatter_sum, scatter_softmax


__all__ = ['ScatterAttention']


class ScatterAttention(nn.Module):

## weave_tensors.py
import torch
from typing import List, Tuple, Union


__all__ = ['weave_tensors', 'unweave_tensor']


def weave_tensors(
        tensors: Union[List[torch.Tensor], Tuple[torch.Tensor, ...]], dim: int = 0):
    assert len(tensors) > 0

## async_notify_run.py
import multiprocessing as mp


__all__ = ['AsyncNotifyChannel']


class CloseNotifyProcessAction:
    pass


## pytorch_chunked_random_access_dataset.py
import os
import io
import torch
import dbm
import itertools
import numpy as np
from warnings import warn
from typing import Optional, Mapping, ByteString
from torch.utils.data.dataset import Dataset

## trie.py
import functools
import dataclasses
from typing import TypeVar, Generic, List, Tuple, Dict, Iterator


__all__ = ['TrieNode']


SequenceElementType = TypeVar('SequenceElementType')
SequenceType = Tuple[SequenceElementType, ...]
	__author__ = "Elad Nachmias"
	__email__ = "eladnah@gmail.com"
	__date__ = "2023-10-18"

	import dataclasses
	import enum
	import itertools
	import warnings
	from collections import defaultdict, Counter
	from typing import Optional, Dict, List, Sequence, Iterable, Union, Set, Tuple
	#include <iostream>
	#include <sstream>

	using namespace std;

	void permutations(string str) {
	// factorials[i] = i! for i in [0..\|str\|]
	uint64_t factorials[str.length() + 1];
	factorials[0] = 1;
	for(int i = 1; i <= str.length(); i++) {
	import math

	__all__ = ['format_time', 'find_pretty_time_fmt', 'prettify_time']

	possible_time_fmts = ('secs', 'mins', 'hours', 'days', 'months', 'years', 'thousand years', 'million years')
	time_fmts_divisors = {
	'secs': 1, 'mins': 60, 'hours': 6060, 'days': 606024, 'months': 60602430,
	'years': 606024365, 'thousand years': 606024365*1_000,
	'million years': 6060243651_000_000}
	assert set(time_fmts_divisors.keys()) == set(possible_time_fmts)
	import torch
	import torch.nn as nn
	from typing import Optional, Tuple
	from torch_scatter import scatter_sum, scatter_softmax


	__all__ = ['ScatterAttention']


	class ScatterAttention(nn.Module):
	import torch
	from typing import List, Tuple, Union


	__all__ = ['weave_tensors', 'unweave_tensor']


	def weave_tensors(
	tensors: Union[List[torch.Tensor], Tuple[torch.Tensor, ...]], dim: int = 0):
	assert len(tensors) > 0
	import multiprocessing as mp


	__all__ = ['AsyncNotifyChannel']


	class CloseNotifyProcessAction:
	pass
	import os
	import io
	import torch
	import dbm
	import itertools
	import numpy as np
	from warnings import warn
	from typing import Optional, Mapping, ByteString
	from torch.utils.data.dataset import Dataset
	import functools
	import dataclasses
	from typing import TypeVar, Generic, List, Tuple, Dict, Iterator


	__all__ = ['TrieNode']


	SequenceElementType = TypeVar('SequenceElementType')
	SequenceType = Tuple[SequenceElementType, ...]