import os
from collections import deque

import torch
from torch.utils.data import Dataset







class __snake_case				(						lowerCamelCase_		):




   def __init__(    self			:				List[Any]       ,	_lowercase			:				Optional[Any]=""       ,	_lowercase			:				Dict="train"      ):



     """simple docstring"""






     assert os.path.isdir(_lowercase      )

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




   def __len__(    self			:				Tuple      ):



     """simple docstring"""






     return len(self.documents      )




   def __getitem__(    self			:				Any       ,	_lowercase			:				Union[str, Any]      ):



     """simple docstring"""






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





def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Dict	)							->					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 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[Any]	)							->					Union[str, Any]:







  """simple docstring"""





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



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       Optional[int] ,     __UpperCamelCase    :       Optional[Any]	)							->					Any:







  """simple docstring"""





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



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Dict ,     __UpperCamelCase    :       int	)							->					Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      torch.ones_like(__UpperCamelCase	)
  SCREAMING_SNAKE_CASE__										=      sequence == pad_token_id
  SCREAMING_SNAKE_CASE__										=      0
  return mask



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Dict ,     __UpperCamelCase    :       List[Any] ,     __UpperCamelCase    :       List[str]	)							->					List[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 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Any ,     __UpperCamelCase    :       Tuple	)							->					Tuple:







  """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	)

from numpy import exp, pi, sqrt



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Any ,     __UpperCamelCase    :       float = 0.0 ,     __UpperCamelCase    :       float = 1.0	)							->					int:







  """simple docstring"""





  return 1 / sqrt(2 * pi * sigma**2	) * exp(-((x - mu) ** 2) / (2 * sigma**2)	)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
