





import os
from collections import deque

import torch
from torch.utils.data import Dataset







class     __lowerCAmelCase	(   UpperCamelCase__):







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



        '''simple docstring'''




        assert os.path.isdir(__a							)

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







      def __len__(   self							):



        '''simple docstring'''




        return len(self.documents							)







      def __getitem__(   self		,				lowerCAmelCase__							):



        '''simple docstring'''




        a__     : str  =self.documents[idx]
        a__     : List[str]  =document_path.split("/"							)[-1]
        with open(__a		,				encoding="utf-8"							) as source:
          a__     : Dict  =source.read()
          a__ ,							a__     : Tuple  =process_story(__a							)
        return document_name, story_lines, summary_lines
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[int]						):





  """simple docstring"""



  a__     : str  =list(filter(lambda SCREAMING_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
  a__     : int  =[_add_missing_period(__snake_case						) for line in nonempty_lines]

  # gather article lines
  a__     : int  =[]
  a__     : Dict  =deque(__snake_case						)
  while True:
    try:
      a__     : List[str]  =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
  a__     : Union[str, Any]  =list(filter(lambda SCREAMING_SNAKE_CASE						: not t.startswith("@highlight"						)	,	__snake_case						)						)

  return story_lines, summary_lines
def 					_A	(		SCREAMING_SNAKE_CASE					:       List[Any]						):





  """simple docstring"""



  a__     : str  =[".", "!", "?", "...", "\'", "`", "\"", "\u2019", "\u2019", ")"]
  if line.startswith("@highlight"						):
    return line
  if line[-1] in END_TOKENS:
    return line
  return line + "."
def 					_A	(		SCREAMING_SNAKE_CASE					:       List[Any]	,	SCREAMING_SNAKE_CASE					:       Dict	,	SCREAMING_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 					_A	(		SCREAMING_SNAKE_CASE					:       Dict	,	SCREAMING_SNAKE_CASE					:       Union[str, Any]						):





  """simple docstring"""



  a__     : str  =torch.ones_like(__snake_case						)
  a__     : Any  =sequence == pad_token_id
  a__     : Optional[int]  =0
  return mask
def 					_A	(		SCREAMING_SNAKE_CASE					:       str	,	SCREAMING_SNAKE_CASE					:       Any	,	SCREAMING_SNAKE_CASE					:       Union[str, Any]						):





  """simple docstring"""



  a__     : List[str]  =[tokenizer.encode(__snake_case						) for line in story_lines]
  a__     : Dict  =[token for sentence in story_lines_token_ids for token in sentence]
  a__     : int  =[tokenizer.encode(__snake_case						) for line in summary_lines]
  a__     : List[str]  =[token for sentence in summary_lines_token_ids for token in sentence]

  return story_token_ids, summary_token_ids





def 					_A	(		SCREAMING_SNAKE_CASE					:       Any	,	SCREAMING_SNAKE_CASE					:       Dict						):





  """simple docstring"""



  a__     : int  =[]
  for sequence in batch:
    a__     : Dict  =-1
    a__     : Optional[Any]  =[]
    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						)













from maths.prime_check import is_prime
def 					_A	(		SCREAMING_SNAKE_CASE					:       int						):





  """simple docstring"""



  if not isinstance(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						):
    a__     : Dict  =f'''Input value of [number={number}] must be an integer'''
    raise TypeError(SCREAMING_SNAKE_CASE						)
  if is_prime(SCREAMING_SNAKE_CASE						) and is_prime(number + 2						):
    return number + 2
  else:
    return -1


if __name__ == "__main__":
     import doctest

     doctest.testmod()






