




'''simple docstring'''





import os
from collections import deque

import torch
from torch.utils.data import Dataset

class        _a    (						__lowerCAmelCase							):






     def __init__(			self ,_SCREAMING_SNAKE_CASE="" ,_SCREAMING_SNAKE_CASE="train"   )							->      int:
          assert os.path.isdir(_SCREAMING_SNAKE_CASE   )

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






     def __len__(			self   )							->      Dict:
          return len(self.documents   )






     def __getitem__(			self ,_SCREAMING_SNAKE_CASE   )							->      str:
          _snake_case							=				self.documents[idx]
          _snake_case							=				document_path.split("/"   )[-1]
          with open(_SCREAMING_SNAKE_CASE ,encoding="utf-8"   ) as source:
               _snake_case							=				source.read()
               _snake_case							, _snake_case							=				process_story(_SCREAMING_SNAKE_CASE   )
          return document_name, story_lines, summary_lines







def      __a     (       _UpperCamelCase:   Optional[int]   )						->			Any:

     """simple docstring"""




     _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
     _snake_case							=				[_add_missing_period(_UpperCamelCase   ) for line in nonempty_lines]

     # gather article lines
     _snake_case							=				[]
     _snake_case							=				deque(_UpperCamelCase   )
     while True:
          try:
               _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
     _snake_case							=				list(filter(lambda _UpperCamelCase   : not t.startswith("@highlight"   ) ,						_UpperCamelCase   )   )

     return story_lines, summary_lines





def      __a     (       _UpperCamelCase:   List[str]   )						->			Optional[int]:

     """simple docstring"""




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





def      __a     (       _UpperCamelCase:   str ,						_UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   int   )						->			Optional[int]:

     """simple docstring"""




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





def      __a     (       _UpperCamelCase:   Any ,						_UpperCamelCase:   List[Any]   )						->			str:

     """simple docstring"""




     _snake_case							=				torch.ones_like(_UpperCamelCase   )
     _snake_case							=				sequence == pad_token_id
     _snake_case							=				0
     return mask





def      __a     (       _UpperCamelCase:   Dict ,						_UpperCamelCase:   Tuple ,						_UpperCamelCase:   Optional[int]   )						->			Optional[Any]:

     """simple docstring"""




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

     return story_token_ids, summary_token_ids





def      __a     (       _UpperCamelCase:   Union[str, Any] ,						_UpperCamelCase:   List[str]   )						->			Optional[int]:

     """simple docstring"""




     _snake_case							=				[]
     for sequence in batch:
          _snake_case							=				-1
          _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   )






'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_						:      Union[str, Any]       =						{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_						:      Optional[int]       =						['''PLBartTokenizer''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_						:      int       =						[
       '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''PLBartForCausalLM''',
       '''PLBartForConditionalGeneration''',
       '''PLBartForSequenceClassification''',
       '''PLBartModel''',
       '''PLBartPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_plbart import PLBartTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_plbart import (
          PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
          PLBartForCausalLM,
          PLBartForConditionalGeneration,
          PLBartForSequenceClassification,
          PLBartModel,
          PLBartPreTrainedModel,
      )


else:
   import sys

   UpperCamelCase_						:      str       =						_LazyModule(__name__, globals()['''__file__'''], _import_structure)


