




'''simple docstring'''



import os
from collections import deque

import torch
from torch.utils.data import Dataset






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






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







             '''simple docstring'''







             assert os.path.isdir(A  )

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[]
             SCREAMING_SNAKE_CASE   :      str    					=						os.listdir(A  )
             for story_filename in story_filenames_list:
                   if "summary" in story_filename:
                         continue
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						os.path.join(A,							A  )
                   if not os.path.isfile(A  ):
                         continue
                   self.documents.append(A  )







       def __len__(      self  ):







             '''simple docstring'''







             return len(self.documents  )







       def __getitem__(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						self.documents[idx]
             SCREAMING_SNAKE_CASE   :      List[str]    					=						document_path.split('/'  )[-1]
             with open(A,							encoding='utf-8'  ) as source:
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						source.read()
                   SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      int    					=						process_story(A  )
             return document_name, story_lines, summary_lines




def   lowercase__(			__UpperCamelCase:					Optional[Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Any    					=						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
      SCREAMING_SNAKE_CASE   :      List[str]    					=						[_add_missing_period(__UpperCamelCase    ) for line in nonempty_lines]

      # gather article lines
      SCREAMING_SNAKE_CASE   :      Tuple    					=						[]
      SCREAMING_SNAKE_CASE   :      int    					=						deque(__UpperCamelCase    )
      while True:
            try:
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						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
      SCREAMING_SNAKE_CASE   :      int    					=						list(filter(lambda __UpperCamelCase    : not t.startswith('@highlight'    )      ,__UpperCamelCase    )    )

      return story_lines, summary_lines




def   lowercase__(			__UpperCamelCase:					Dict    ):

      """simple docstring"""






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




def   lowercase__(			__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					Any      ,__UpperCamelCase:					Optional[Any]    ):

      """simple docstring"""






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




def   lowercase__(			__UpperCamelCase:					Optional[int]      ,__UpperCamelCase:					Dict    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      List[Any]    					=						torch.ones_like(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						sequence == pad_token_id
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						0
      return mask




def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					Any      ,__UpperCamelCase:					Union[str, Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Dict    					=						[tokenizer.encode(__UpperCamelCase    ) for line in story_lines]
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[token for sentence in story_lines_token_ids for token in sentence]
      SCREAMING_SNAKE_CASE   :      str    					=						[tokenizer.encode(__UpperCamelCase    ) for line in summary_lines]
      SCREAMING_SNAKE_CASE   :      Dict    					=						[token for sentence in summary_lines_token_ids for token in sentence]

      return story_token_ids, summary_token_ids





def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					Union[str, Any]    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[]
      for sequence in batch:
            SCREAMING_SNAKE_CASE   :      Dict    					=						-1
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						[]
            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    )






from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							    =							{
    'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
    'studio-ousia/luke-large': 'https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json',
}


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      """luke"""


     def __init__(	self  ,       __A=50267  ,       __A=500000  ,       __A=768  ,       __A=256  ,       __A=12  ,       __A=12  ,       __A=3072  ,       __A="gelu"  ,       __A=0.1  ,       __A=0.1  ,       __A=512  ,       __A=2  ,       __A=0.02  ,       __A=1E-12  ,       __A=True  ,       __A=None  ,       __A=1  ,       __A=0  ,       __A=2  ,       **__A  ,       ):
        super().__init__(pad_token_id=__A  ,       bos_token_id=__A  ,       eos_token_id=__A  ,       **__A    )

        __a					=  vocab_size
        __a					=  entity_vocab_size
        __a					=  hidden_size
        __a					=  entity_emb_size
        __a					=  num_hidden_layers
        __a					=  num_attention_heads
        __a					=  hidden_act
        __a					=  intermediate_size
        __a					=  hidden_dropout_prob
        __a					=  attention_probs_dropout_prob
        __a					=  max_position_embeddings
        __a					=  type_vocab_size
        __a					=  initializer_range
        __a					=  layer_norm_eps
        __a					=  use_entity_aware_attention
        __a					=  classifier_dropout



