

import os
from collections import deque

import torch
from torch.utils.data import Dataset



class        lowercase		(			snake_case__   ):



 '''simple docstring'''


 def __init__(self			,			__a=""			,			__a="train"					)					->   Optional[int]:






  """simple docstring"""





  assert os.path.isdir(_UpperCamelCase					)

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




 def __len__(self					)					->   List[str]:






  """simple docstring"""





  return len(self.documents					)




 def __getitem__(self			,			__a					)					->   Tuple:






  """simple docstring"""





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







def 		UpperCamelCase_(     snake_case__:    List[Any]  )			->					int:
 UpperCAmelCase__     							=							list(filter(lambda snake_case__  : len(__A  ) != 0    ,       [line.strip() for line in raw_story.split('\n'  )]  )  )

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

 # gather article lines
 UpperCAmelCase__     							=							[]
 UpperCAmelCase__     							=							deque(__A  )
 while True:
  try:
   UpperCAmelCase__     							=							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
 UpperCAmelCase__     							=							list(filter(lambda snake_case__  : not t.startswith('@highlight'  )    ,       __A  )  )

 return story_lines, summary_lines
def 		UpperCamelCase_(     snake_case__:    Any  )			->					Any:
 UpperCAmelCase__     							=							['.', '!', '?', '...', '\'', '`', '\"', '\u2019', '\u2019', ')']
 if line.startswith('@highlight'  ):
  return line
 if line[-1] in END_TOKENS:
  return line
 return line + "."
def 		UpperCamelCase_(     snake_case__:    List[str]    ,       snake_case__:    Optional[int]    ,       snake_case__:    List[str]  )			->					Optional[int]:




 if len(__A  ) > block_size:
  return sequence[:block_size]
 else:
  sequence.extend([pad_token_id] * (block_size - len(__A  ))  )
  return sequence
def 		UpperCamelCase_(     snake_case__:    str    ,       snake_case__:    Any  )			->					List[str]:
 UpperCAmelCase__     							=							torch.ones_like(__A  )
 UpperCAmelCase__     							=							sequence == pad_token_id
 UpperCAmelCase__     							=							0
 return mask
def 		UpperCamelCase_(     snake_case__:    List[str]    ,       snake_case__:    Tuple    ,       snake_case__:    List[Any]  )			->					Tuple:
 UpperCAmelCase__     							=							[tokenizer.encode(__A  ) for line in story_lines]
 UpperCAmelCase__     							=							[token for sentence in story_lines_token_ids for token in sentence]
 UpperCAmelCase__     							=							[tokenizer.encode(__A  ) for line in summary_lines]
 UpperCAmelCase__     							=							[token for sentence in summary_lines_token_ids for token in sentence]

 return story_token_ids, summary_token_ids







def 		UpperCamelCase_(     snake_case__:    Tuple    ,       snake_case__:    int  )			->					List[Any]:
 UpperCAmelCase__     							=							[]
 for sequence in batch:
  UpperCAmelCase__     							=							-1
  UpperCAmelCase__     							=							[]
  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  )






import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


UpperCamelCase__		 =     get_logger(__name__)

class     __snake_case    (				enum.Enum  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							"all_checks"
       __SCREAMING_SNAKE_CASE				    =							"basic_checks"
       __SCREAMING_SNAKE_CASE				    =							"no_checks"






class     __snake_case    (				snake_case__  ):
       """simple docstring"""













class     __snake_case    (				snake_case__  ):
       """simple docstring"""













class     __snake_case    (				snake_case__  ):
       """simple docstring"""













class     __snake_case    (				snake_case__  ):
       """simple docstring"""














def 						lowerCamelCase__      ( __A    :Optional[dict]	,__A    :dict	,__A    :Any=None		):


       """simple docstring"""




       if expected_checksums is None:
              logger.info("""Unable to verify checksums."""		)
              return
       if len(set(__A		) - set(__A		)		) > 0:
              raise ExpectedMoreDownloadedFiles(str(set(__A		) - set(__A		)		)		)
       if len(set(__A		) - set(__A		)		) > 0:
              raise UnexpectedDownloadedFile(str(set(__A		) - set(__A		)		)		)
       __snake_case       	=							[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
       __snake_case       	=							""" for """ + verification_name if verification_name is not None else """"""
       if len(__A		) > 0:
              raise NonMatchingChecksumError(
                  F'Checksums didn\'t match{for_verification_name}:\n'
                  F'{bad_urls}\n'
                  """Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"""		)
       logger.info("""All the checksums matched successfully""" + for_verification_name		)

class     __snake_case    (				snake_case__  ):
       """simple docstring"""













class     __snake_case    (				snake_case__  ):
       """simple docstring"""













class     __snake_case    (				snake_case__  ):
       """simple docstring"""













class     __snake_case    (				snake_case__  ):
       """simple docstring"""














def 						lowerCamelCase__      ( __A    :Optional[dict]	,__A    :dict		):


       """simple docstring"""




       if expected_splits is None:
              logger.info("""Unable to verify splits sizes."""		)
              return
       if len(set(__A		) - set(__A		)		) > 0:
              raise ExpectedMoreSplits(str(set(__A		) - set(__A		)		)		)
       if len(set(__A		) - set(__A		)		) > 0:
              raise UnexpectedSplits(str(set(__A		) - set(__A		)		)		)
       __snake_case       	=							[
           {"""expected""": expected_splits[name], """recorded""": recorded_splits[name]}
           for name in expected_splits
           if expected_splits[name].num_examples != recorded_splits[name].num_examples
       ]
       if len(__A		) > 0:
              raise NonMatchingSplitsSizesError(str(__A		)		)
       logger.info("""All the splits matched successfully."""		)







def 						lowerCamelCase__      ( __A    :str	,__A    :bool = True		):


       """simple docstring"""




       if record_checksum:
              __snake_case       	=							shaaaa()
              with open(__A	,"""rb"""		) as f:
                     for chunk in iter(lambda: f.read(1 << 2_0		)	,b""""""		):
                            m.update(__A		)
                     __snake_case       	=							m.hexdigest()
       else:
              __snake_case       	=							None
       return {"num_bytes": os.path.getsize(__A		), "checksum": checksum}







def 						lowerCamelCase__      ( __A    :Optional[int]		):


       """simple docstring"""




       if dataset_size and config.IN_MEMORY_MAX_SIZE:
              return dataset_size < config.IN_MEMORY_MAX_SIZE
       else:
              return False

