




"""simple docstring"""




import os
from collections import deque

import torch
from torch.utils.data import Dataset


class     UpperCamelCase_   (			__lowerCAmelCase					):

    '''simple docstring'''






    def __init__(      self				:     Optional[Any] ,			UpperCAmelCase__				:     Union[str, Any]="" ,			UpperCAmelCase__				:     Optional[Any]="train")						->Optional[Any]:





           '''simple docstring'''

           assert os.path.isdir(lowerCamelCase__)

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


    def __len__(      self				:     Union[str, Any])						->Any:





           '''simple docstring'''

           return len(self.documents)





    def __getitem__(      self				:     Optional[int] ,			UpperCAmelCase__				:     Dict)						->List[Any]:





           '''simple docstring'''

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







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	Dict:



       """simple docstring"""

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

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

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

       return story_lines, summary_lines







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	Tuple:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	Optional[Any]:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	Optional[Any]:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	Optional[Any]:



       """simple docstring"""

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

       return story_token_ids, summary_token_ids







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	Tuple:



       """simple docstring"""

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








from __future__ import annotations

from collections import Counter
from random import random


class     UpperCamelCase_   :

    '''simple docstring'''






    def __init__(      self				:     Any)						->Optional[Any]:





           '''simple docstring'''

           A__							=   {}


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			UpperCAmelCase__				:     str)						->None:





           '''simple docstring'''

           A__							=   {}


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     float)						->None:





           '''simple docstring'''

           if nodea not in self.connections:
                  self.add_node(UpperCAmelCase__)
           if nodea not in self.connections:
                  self.add_node(UpperCAmelCase__)
           A__							=   probability


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->list[str]:





           '''simple docstring'''

           return list(self.connections)





    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     str)						->str:





           '''simple docstring'''

           A__							=   0
           A__							=   random()

           for dest in self.connections[node]:
                  current_probability += self.connections[node][dest]
                  if current_probability > random_value:
                         return dest
           return ""







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	dict[str, int]:



       """simple docstring"""

       A__							=   MarkovChainGraphUndirectedUnweighted()

       for nodea, nodea, probability in transitions:
              graph.add_transition_probability(lowercase_			,    lowercase_			,    lowercase_						)

       A__							=   Counter(graph.get_nodes()						)
       A__							=   start

       for _ in range(lowercase_						):
              A__							=   graph.transition(lowercase_						)
              visited[node] += 1

       return visited


if __name__ == "__main__":
  import doctest

  doctest.testmod()


