


'''simple docstring'''






import os
from collections import deque

import torch
from torch.utils.data import Dataset




class 						a__							(				a__    ):





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_=""   ,       lowerCamelCase_="train"  )			->				Optional[int]:
														assert os.path.isdir(lowerCamelCase_  )

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







							def __len__(						self  )			->				List[Any]:
														return len(self.documents  )







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


def    _snake_case  (	A			)		->       Optional[int]:
							lowerCAmelCase__			  =      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
							lowerCAmelCase__			  =      [_add_missing_period(A			) for line in nonempty_lines]

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

							return story_lines, summary_lines


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


def    _snake_case  (	A					,       A					,       A			)		->       str:
							if len(A			) > block_size:
														return sequence[:block_size]
							else:
														sequence.extend([pad_token_id] * (block_size - len(A			))			)
														return sequence


def    _snake_case  (	A					,       A			)		->       str:
							lowerCAmelCase__			  =      torch.ones_like(A			)
							lowerCAmelCase__			  =      sequence == pad_token_id
							lowerCAmelCase__			  =      0
							return mask


def    _snake_case  (	A					,       A					,       A			)		->       Dict:
							lowerCAmelCase__			  =      [tokenizer.encode(A			) for line in story_lines]
							lowerCAmelCase__			  =      [token for sentence in story_lines_token_ids for token in sentence]
							lowerCAmelCase__			  =      [tokenizer.encode(A			) for line in summary_lines]
							lowerCAmelCase__			  =      [token for sentence in summary_lines_token_ids for token in sentence]

							return story_token_ids, summary_token_ids






def    _snake_case  (	A					,       A			)		->       List[Any]:
							lowerCAmelCase__			  =      []
							for sequence in batch:
														lowerCAmelCase__			  =      -1
														lowerCAmelCase__			  =      []
														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 os
import sys

lowercase   =	os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowercase   =	[
    '''torch''',
    '''numpy''',
    '''tokenizers''',
    '''filelock''',
    '''requests''',
    '''tqdm''',
    '''regex''',
    '''sentencepiece''',
    '''sacremoses''',
    '''importlib_metadata''',
    '''huggingface_hub''',
]



@add_start_docstrings(AutoConfig.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[Any]      ,					**UpperCAmelCase__					:   List[str]							)	->     List[str]:
							return AutoConfig.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoTokenizer.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[str]      ,					**UpperCAmelCase__					:   List[Any]							)	->     Any:
							return AutoTokenizer.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModel.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   Union[str, Any]      ,					**UpperCAmelCase__					:   Dict							)	->     Union[str, Any]:
							return AutoModel.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForCausalLM.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[str]      ,					**UpperCAmelCase__					:   List[Any]							)	->     Optional[Any]:
							return AutoModelForCausalLM.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForMaskedLM.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   Union[str, Any]      ,					**UpperCAmelCase__					:   Dict							)	->     int:
							return AutoModelForMaskedLM.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForSequenceClassification.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[Any]      ,					**UpperCAmelCase__					:   Dict							)	->     Dict:
							return AutoModelForSequenceClassification.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForQuestionAnswering.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   Any      ,					**UpperCAmelCase__					:   List[str]							)	->     Union[str, Any]:
							return AutoModelForQuestionAnswering.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)





