






'''simple docstring'''







import os
from collections import deque

import torch
from torch.utils.data import Dataset

class _lowercase			(       _lowercase	):




							def __init__(							self:       List[Any]    ,							UpperCamelCase__:       Optional[int]=""    ,							UpperCamelCase__:       Tuple="train"  ):
														assert os.path.isdir(UpperCamelCase__  )

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




							def __len__(							self:       int  ):
														return len(self.documents  )




							def __getitem__(							self:       Dict    ,							UpperCamelCase__:       List[str]  ):
														lowerCamelCase__      :			Optional[int] 						= self.documents[idx]
														lowerCamelCase__      :			Optional[int] 						= document_path.split("""/"""  )[-1]
														with open(UpperCamelCase__    ,							encoding="""utf-8"""  ) as source:
																					lowerCamelCase__      :			List[str] 						= source.read()
																					lowerCamelCase__						,	lowerCamelCase__      :			List[str] 						= process_story(UpperCamelCase__  )
														return document_name, story_lines, summary_lines





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> str:
							lowerCamelCase__      :			List[str] 						= 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
							lowerCamelCase__      :			Union[str, Any] 						= [_add_missing_period(UpperCamelCase   ) for line in nonempty_lines]

							# gather article lines
							lowerCamelCase__      :			Dict 						= []
							lowerCamelCase__      :			List[str] 						= deque(UpperCamelCase   )
							while True:
														try:
																					lowerCamelCase__      :			str 						= 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
							lowerCamelCase__      :			Optional[Any] 						= list(filter(lambda UpperCamelCase   : not t.startswith("""@highlight"""   )       ,    UpperCamelCase   )   )

							return story_lines, summary_lines





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





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> int:
							if len(UpperCamelCase   ) > block_size:
														return sequence[:block_size]
							else:
														sequence.extend([pad_token_id] * (block_size - len(UpperCamelCase   ))   )
														return sequence





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> Dict:
							lowerCamelCase__      :			Dict 						= torch.ones_like(UpperCamelCase   )
							lowerCamelCase__      :			Any 						= sequence == pad_token_id
							lowerCamelCase__      :			List[str] 						= 0
							return mask





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> str:
							lowerCamelCase__      :			str 						= [tokenizer.encode(UpperCamelCase   ) for line in story_lines]
							lowerCamelCase__      :			Dict 						= [token for sentence in story_lines_token_ids for token in sentence]
							lowerCamelCase__      :			Optional[int] 						= [tokenizer.encode(UpperCamelCase   ) for line in summary_lines]
							lowerCamelCase__      :			str 						= [token for sentence in summary_lines_token_ids for token in sentence]

							return story_token_ids, summary_token_ids





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> Optional[Any]:
							lowerCamelCase__      :			Optional[int] 						= []
							for sequence in batch:
														lowerCamelCase__      :			Optional[Any] 						= -1
														lowerCamelCase__      :			Optional[int] 						= []
														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_speech_available,
    is_torch_available,
)


_A			:    Any					  ={
    '''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
    '''processing_trocr''': ['''TrOCRProcessor'''],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A			:    str					  =[
			    '''TROCR_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TrOCRForCausalLM''',
			    '''TrOCRPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
			from .processing_trocr import TrOCRProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
			import sys

			_A			:    Dict					  =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

