
'''simple docstring'''

import os
from collections import deque

import torch
from torch.utils.data import Dataset


class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     def __init__(      self							,		lowerCAmelCase_=""							,		lowerCAmelCase_="train" ):

       '''simple docstring'''

       assert os.path.isdir(lowerCAmelCase_ )

       a_      :	int      =							[]
       a_      :	Optional[Any]      =							os.listdir(lowerCAmelCase_ )
       for story_filename in story_filenames_list:
         if "summary" in story_filename:
           continue
         a_      :	Dict      =							os.path.join(lowerCAmelCase_							,		lowerCAmelCase_ )
         if not os.path.isfile(lowerCAmelCase_ ):
           continue
         self.documents.append(lowerCAmelCase_ )


     def __len__(      self ):

       '''simple docstring'''

       return len(self.documents )






     def __getitem__(      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Optional[int]      =							self.documents[idx]
       a_      :	Optional[Any]      =							document_path.split("""/""" )[-1]
       with open(lowerCAmelCase_							,		encoding="""utf-8""" ) as source:
         a_      :	Optional[Any]      =							source.read()
         a_      , a_      :	List[Any]      =							process_story(lowerCAmelCase_ )
       return document_name, story_lines, summary_lines







def 	_snake_case						(      A_       :    Union[str, Any]   ):
  """simple docstring"""







  a_      :	List[Any]      =							list(filter(lambda A_   : len(A_   ) != 0     ,		[line.strip() for line in raw_story.split("""\n"""   )]   )   )

  # for some unknown reason some lines miss a period, add it
  a_      :	int      =							[_add_missing_period(A_   ) for line in nonempty_lines]

  # gather article lines
  a_      :	Optional[Any]      =							[]
  a_      :	List[Any]      =							deque(A_   )
  while True:
    try:
      a_      :	List[Any]      =							lines.popleft()
      if element.startswith("""@highlight"""   ):
        break
      story_lines.append(A_   )
    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
  a_      :	Union[str, Any]      =							list(filter(lambda A_   : not t.startswith("""@highlight"""   )     ,		A_   )   )

  return story_lines, summary_lines






def 	_snake_case						(      A_       :    str   ):
  """simple docstring"""







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






def 	_snake_case						(      A_       :    Any     ,		A_       :    List[Any]     ,		A_       :    List[str]   ):
  """simple docstring"""







  if len(A_   ) > block_size:
    return sequence[:block_size]
  else:
    sequence.extend([pad_token_id] * (block_size - len(A_   ))   )
    return sequence






def 	_snake_case						(      A_       :    Union[str, Any]     ,		A_       :    Optional[int]   ):
  """simple docstring"""







  a_      :	Optional[int]      =							torch.ones_like(A_   )
  a_      :	Optional[int]      =							sequence == pad_token_id
  a_      :	List[Any]      =							0
  return mask






def 	_snake_case						(      A_       :    Union[str, Any]     ,		A_       :    Optional[Any]     ,		A_       :    Tuple   ):
  """simple docstring"""







  a_      :	Optional[Any]      =							[tokenizer.encode(A_   ) for line in story_lines]
  a_      :	Union[str, Any]      =							[token for sentence in story_lines_token_ids for token in sentence]
  a_      :	Any      =							[tokenizer.encode(A_   ) for line in summary_lines]
  a_      :	Optional[Any]      =							[token for sentence in summary_lines_token_ids for token in sentence]

  return story_token_ids, summary_token_ids






def 	_snake_case						(      A_       :    int     ,		A_       :    Tuple   ):
  """simple docstring"""







  a_      :	Dict      =							[]
  for sequence in batch:
    a_      :	List[str]      =							-1
    a_      :	Any      =							[]
    for s in sequence:
      if s == separator_token_id:
        sentence_num += 1
      embeddings.append(sentence_num % 2   )
    batch_embeddings.append(A_   )
  return torch.tensor(A_   )




'''simple docstring'''

from __future__ import annotations






def 	_snake_case						(      A_       :    int   ):
  """simple docstring"""







  a_      :	Optional[Any]      =							2
  a_      :	int      =							[]
  while i * i <= n:
    if n % i:
      i += 1
    else:
      n //= i
      factors.append(A_   )
  if n > 1:
    factors.append(A_   )
  return factors


if __name__ == "__main__":
    import doctest

    doctest.testmod()


