

import os
from collections import deque

import torch
from torch.utils.data import Dataset






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  def __init__(   self     :  Union[str, Any]      ,lowercase__     :  Dict=""      ,lowercase__     :  Optional[int]="train"    ):
   assert os.path.isdir(lowercase__    )

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






  def __len__(   self     :  Optional[int]    ):
   return len(self.documents    )






  def __getitem__(   self     :  Optional[Any]      ,lowercase__     :  Tuple    ):
   __lowercase													=				self.documents[idx]
   __lowercase													=				document_path.split('''/'''    )[-1]
   with open(lowercase__      ,encoding='''utf-8'''    ) as source:
    __lowercase													=				source.read()
    __lowercase						,			__lowercase													=				process_story(lowercase__    )
   return document_name, story_lines, summary_lines


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				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
 __lowercase													=				[_add_missing_period(A__   ) for line in nonempty_lines]

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

 return story_lines, summary_lines


def 	_A  (					A__   ):






 """simple docstring"""



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


def 	_A  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



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


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				torch.ones_like(A__   )
 __lowercase													=				sequence == pad_token_id
 __lowercase													=				0
 return mask


def 	_A  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



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

 return story_token_ids, summary_token_ids






def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				[]
 for sequence in batch:
  __lowercase													=				-1
  __lowercase													=				[]
  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 collections.abc import Callable

import numpy as np


def 	_A  (					A__  ,       A__  ,       A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				int(np.ceil((x_end - xa) / step_size   )   )
 __lowercase													=				np.zeros((n + 1,)   )
 __lowercase													=				ya
 __lowercase													=				xa

 for k in range(A__   ):
  __lowercase													=				y[k] + step_size * ode_func(A__  ,       y[k]   )
  x += step_size

 return y


if __name__ == "__main__":
 import doctest

 doctest.testmod()

