

import os
from collections import deque

import torch
from torch.utils.data import Dataset




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):

    def __init__(			self :      Tuple							,    snake_case__ :      Any=""							,    snake_case__ :      Tuple="train"    ):

     """simple docstring"""






     assert os.path.isdir(snake_case__    )

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

    def __len__(			self :      Optional[Any]    ):

     """simple docstring"""






     return len(self.documents    )




    def __getitem__(			self :      int							,    snake_case__ :      Any    ):

     """simple docstring"""






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


def        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[Any]	)		->							Any:
 '''simple docstring'''


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

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

 # gather article lines
 SCREAMING_SNAKE_CASE								=			[]
 SCREAMING_SNAKE_CASE								=			deque(_UpperCamelCase	)
 while True:
  try:
   SCREAMING_SNAKE_CASE								=			lines.popleft()
   if element.startswith('@highlight'	):
    break
   story_lines.append(_UpperCamelCase	)
  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
 SCREAMING_SNAKE_CASE								=			list(filter(lambda _UpperCamelCase	: not t.startswith('@highlight'	)		,							_UpperCamelCase	)	)

 return story_lines, summary_lines


def        __lowerCAmelCase    (       _UpperCamelCase		:  Dict	)		->							Dict:
 '''simple docstring'''


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


def        __lowerCAmelCase    (       _UpperCamelCase		:  str		,							_UpperCamelCase		:  List[str]		,							_UpperCamelCase		:  str	)		->							Tuple:
 '''simple docstring'''


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


def        __lowerCAmelCase    (       _UpperCamelCase		:  Union[str, Any]		,							_UpperCamelCase		:  Dict	)		->							Tuple:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			torch.ones_like(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			sequence == pad_token_id
 SCREAMING_SNAKE_CASE								=			0
 return mask


def        __lowerCAmelCase    (       _UpperCamelCase		:  Optional[int]		,							_UpperCamelCase		:  int		,							_UpperCamelCase		:  Union[str, Any]	)		->							Optional[Any]:
 '''simple docstring'''


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

 return story_token_ids, summary_token_ids




def        __lowerCAmelCase    (       _UpperCamelCase		:  List[Any]		,							_UpperCamelCase		:  str	)		->							Any:
 '''simple docstring'''


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




import numpy as np


def        __lowerCAmelCase    (       _UpperCamelCase		:  np.ndarray	)		->							np.ndarray:
 '''simple docstring'''


 return 1 / (1 + np.exp(-vector	))


def        __lowerCAmelCase    (       _UpperCamelCase		:  np.ndarray	)		->							np.ndarray:
 '''simple docstring'''


 return vector * sigmoid(_UpperCamelCase	)


if __name__ == "__main__":
      import doctest

      doctest.testmod()

