




import os
from collections import deque

import torch
from torch.utils.data import Dataset

class     __a(	_a  ):






    """simple docstring"""





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

         UpperCAmelCase_     :				Dict  =     []
         UpperCAmelCase_     :				Union[str, Any]  =     os.listdir(_SCREAMING_SNAKE_CASE    )
         for story_filename in story_filenames_list:
              if "summary" in story_filename:
                   continue
              UpperCAmelCase_     :				int  =     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    )					->						Tuple:
         return len(self.documents    )





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







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				str  =     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
     UpperCAmelCase_     :				Union[str, Any]  =     [_add_missing_period(_lowercase ) for line in nonempty_lines]

     # gather article lines
     UpperCAmelCase_     :				Union[str, Any]  =     []
     UpperCAmelCase_     :				Any  =     deque(_lowercase )
     while True:
          try:
               UpperCAmelCase_     :				Union[str, Any]  =     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
     UpperCAmelCase_     :				int  =     list(filter(lambda _lowercase : not t.startswith('''@highlight''' )					,		_lowercase ) )

     return story_lines, summary_lines







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


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







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


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







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Optional[Any]  =     torch.ones_like(_lowercase )
     UpperCAmelCase_     :				Tuple  =     sequence == pad_token_id
     UpperCAmelCase_     :				Union[str, Any]  =     0
     return mask







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				List[Any]  =     [tokenizer.encode(_lowercase ) for line in story_lines]
     UpperCAmelCase_     :				Tuple  =     [token for sentence in story_lines_token_ids for token in sentence]
     UpperCAmelCase_     :				int  =     [tokenizer.encode(_lowercase ) for line in summary_lines]
     UpperCAmelCase_     :				List[str]  =     [token for sentence in summary_lines_token_ids for token in sentence]

     return story_token_ids, summary_token_ids







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				List[Any]  =     []
     for sequence in batch:
          UpperCAmelCase_     :				str  =     -1
          UpperCAmelCase_     :				Optional[Any]  =     []
          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 )





# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

__a							     =					'2.13.1'

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse('3.7'):
     raise ImportWarning(
         'To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'
     )

if version.parse(pyarrow.__version__).major < 8:
     raise ImportWarning(
         'To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'
         'If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'
     )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

__a							     =					concatenate_datasets
__a							     =					DownloadConfig
__a							     =					DownloadManager
__a							     =					DownloadMode
__a							     =					DownloadConfig
__a							     =					DownloadMode
__a							     =					DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager