


'''simple docstring'''
import os
from collections import deque

import torch
from torch.utils.data import Dataset
class        lowercase     (							A__ ):

		"""simple docstring"""

		def __init__(		self   ,       UpperCamelCase_=""   ,       UpperCamelCase_="train"							):

							'''simple docstring'''


							assert os.path.isdir(UpperCamelCase_							)

							UpperCamelCase__       :List[Any]							 =			[]
							UpperCamelCase__       :int							 =			os.listdir(UpperCamelCase_							)
							for story_filename in story_filenames_list:
												if "summary" in story_filename:
																	continue
												UpperCamelCase__       :str							 =			os.path.join(UpperCamelCase_   ,       UpperCamelCase_							)
												if not os.path.isfile(UpperCamelCase_							):
																	continue
												self.documents.append(UpperCamelCase_							)




		def __len__(		self							):

							'''simple docstring'''


							return len(self.documents							)




		def __getitem__(		self   ,       UpperCamelCase_							):

							'''simple docstring'''


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



def       a				(      __a       )  ->     Optional[Any]:



					'''simple docstring'''






					UpperCamelCase__       :Optional[Any]							 =			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
					UpperCamelCase__       :str							 =			[_add_missing_period(__a       ) for line in nonempty_lines]

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

					return story_lines, summary_lines


def       a				(      __a       )  ->     Tuple:



					'''simple docstring'''






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


def       a				(      __a			, __a			, __a       )  ->     Any:



					'''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       )  ->     Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__       :int							 =			torch.ones_like(__a       )
					UpperCamelCase__       :Tuple							 =			sequence == pad_token_id
					UpperCamelCase__       :Any							 =			0
					return mask


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



					'''simple docstring'''






					UpperCamelCase__       :Union[str, Any]							 =			[tokenizer.encode(__a       ) for line in story_lines]
					UpperCamelCase__       :List[Any]							 =			[token for sentence in story_lines_token_ids for token in sentence]
					UpperCamelCase__       :Union[str, Any]							 =			[tokenizer.encode(__a       ) for line in summary_lines]
					UpperCamelCase__       :Union[str, Any]							 =			[token for sentence in summary_lines_token_ids for token in sentence]

					return story_token_ids, summary_token_ids







def       a				(      __a			, __a       )  ->     Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__       :Union[str, Any]							 =			[]
					for sequence in batch:
										UpperCamelCase__       :Optional[int]							 =			-1
										UpperCamelCase__       :Optional[int]							 =			[]
										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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case											=					{'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case											=					[
		    '''VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ViTMSNModel''',
		    '''ViTMSNForImageClassification''',
		    '''ViTMSNPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vit_msn import (
				    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViTMSNForImageClassification,
				    ViTMSNModel,
				    ViTMSNPreTrainedModel,
				)

else:
		import sys

		__snake_case											=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)