






"""simple docstring"""



import os
from collections import deque

import torch
from torch.utils.data import Dataset

class        a     (   a_	):







    def __init__(			self							,		_lowerCamelCase=""							,		_lowerCamelCase="train"       ):
      assert os.path.isdir(_lowerCamelCase       )

      lowercase							    =       []
      lowercase							    =       os.listdir(_lowerCamelCase       )
      for story_filename in story_filenames_list:
        if "summary" in story_filename:
          continue
        lowercase							    =       os.path.join(_lowerCamelCase							,		_lowerCamelCase       )
        if not os.path.isfile(_lowerCamelCase       ):
          continue
        self.documents.append(_lowerCamelCase       )







    def __len__(			self       ):
      return len(self.documents       )







    def __getitem__(			self							,		_lowerCamelCase       ):
      lowercase							    =       self.documents[idx]
      lowercase							    =       document_path.split('/'       )[-1]
      with open(_lowerCamelCase							,		encoding='utf-8'       ) as source:
        lowercase							    =       source.read()
        lowercase    ,     lowercase							    =       process_story(_lowerCamelCase       )
      return document_name, story_lines, summary_lines


def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Any    ):





  '''simple docstring'''





  lowercase							    =       list(filter(lambda __snake_case    : len(__snake_case    ) != 0		,					[line.strip() for line in raw_story.split('\n'    )]    )    )

  # for some unknown reason some lines miss a period, add it
  lowercase							    =       [_add_missing_period(__snake_case    ) for line in nonempty_lines]

  # gather article lines
  lowercase							    =       []
  lowercase							    =       deque(__snake_case    )
  while True:
    try:
      lowercase							    =       lines.popleft()
      if element.startswith('@highlight'    ):
        break
      story_lines.append(__snake_case    )
    except IndexError:
      # if "@highlight" is absent from the file we pop
      # all elements until there is None, raising an exception.
      return story_lines, []

    # gather summary lines
  lowercase							    =       list(filter(lambda __snake_case    : not t.startswith('@highlight'    )		,					__snake_case    )    )

  return story_lines, summary_lines
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Optional[int]    ):





  '''simple docstring'''





  lowercase							    =       ['.', '!', '?', '...', '\'', '`', '"', '\u2019', '\u2019', ')']
  if line.startswith('@highlight'    ):
    return line
  if line[-1] in END_TOKENS:
    return line
  return line + "."
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Optional[int]		,					__snake_case			:  Optional[Any]		,					__snake_case			:  Union[str, Any]    ):





  '''simple docstring'''





  if len(__snake_case    ) > block_size:
    return sequence[:block_size]
  else:
    sequence.extend([pad_token_id] * (block_size - len(__snake_case    ))    )
    return sequence
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]		,					__snake_case			:  Optional[int]    ):





  '''simple docstring'''





  lowercase							    =       torch.ones_like(__snake_case    )
  lowercase							    =       sequence == pad_token_id
  lowercase							    =       0
  return mask
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Tuple		,					__snake_case			:  Dict		,					__snake_case			:  Optional[Any]    ):





  '''simple docstring'''





  lowercase							    =       [tokenizer.encode(__snake_case    ) for line in story_lines]
  lowercase							    =       [token for sentence in story_lines_token_ids for token in sentence]
  lowercase							    =       [tokenizer.encode(__snake_case    ) for line in summary_lines]
  lowercase							    =       [token for sentence in summary_lines_token_ids for token in sentence]

  return story_token_ids, summary_token_ids



def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int		,					__snake_case			:  Dict    ):





  '''simple docstring'''





  lowercase							    =       []
  for sequence in batch:
    lowercase							    =       -1
    lowercase							    =       []
    for s in sequence:
      if s == separator_token_id:
        sentence_num += 1
      embeddings.append(sentence_num % 2    )
    batch_embeddings.append(__snake_case    )
  return torch.tensor(__snake_case    )










"""simple docstring"""



import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


_UpperCamelCase		:  List[Any]						=		4
_UpperCamelCase		:  Optional[Any]						=		3

class        a     (   a_	):
    pass


def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]    ):





  '''simple docstring'''





  for shard in shards:
    for i in range(__snake_case    ):
      yield {"i": i, "shard": shard}
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       int(os.environ['RANK']    )
  lowercase							    =       int(os.environ['WORLD_SIZE']    )

  lowercase							    =       ArgumentParser()
  parser.add_argument('--streaming'		,					type=__snake_case    )
  parser.add_argument('--local_rank'		,					type=__snake_case    )
  parser.add_argument('--num_workers'		,					type=__snake_case		,					default=0    )
  lowercase							    =       parser.parse_args()
  lowercase							    =       args.streaming
  lowercase							    =       args.num_workers

  lowercase							    =       {'shards': [f'shard_{shard_idx}' for shard_idx in range(__snake_case    )]}
  lowercase							    =       IterableDataset.from_generator(__snake_case		,					gen_kwargs=__snake_case    )
  if not streaming:
    lowercase							    =       Dataset.from_list(list(__snake_case    )    )

  lowercase							    =       split_dataset_by_node(__snake_case		,					rank=__snake_case		,					world_size=__snake_case    )
  lowercase							    =       torch.utils.data.DataLoader(__snake_case		,					num_workers=__snake_case    )

  lowercase							    =       NUM_SHARDS * NUM_ITEMS_PER_SHARD
  lowercase							    =       full_size // world_size
  expected_local_size += int(rank < (full_size % world_size)    )

  lowercase							    =       sum(1 for _ in dataloader    )
  if local_size != expected_local_size:
    raise FailedTestError(f'local_size {local_size} != expected_local_size {expected_local_size}'    )


if __name__ == "__main__":
    main()


