


"""simple docstring"""







import os
from collections import deque

import torch
from torch.utils.data import Dataset


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


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






         """simple docstring"""





         assert os.path.isdir(snake_case__						)

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

       def __len__(						self						):






         """simple docstring"""





         return len(self.documents						)







       def __getitem__(						self		,     snake_case__						):






         """simple docstring"""





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

def 					a__			(				SCREAMING_SNAKE_CASE    :    Union[str, Any]				):


  '''simple docstring'''







  lowerCAmelCase     :     Optional[int]								=							list(filter(lambda SCREAMING_SNAKE_CASE				: len(SCREAMING_SNAKE_CASE				) != 0				,      [line.strip() for line in raw_story.split("\n"				)]				)				)

  # for some unknown reason some lines miss a period, add it
  lowerCAmelCase     :     List[Any]								=							[_add_missing_period(SCREAMING_SNAKE_CASE				) for line in nonempty_lines]

  # gather article lines
  lowerCAmelCase     :     List[Any]								=							[]
  lowerCAmelCase     :     List[str]								=							deque(SCREAMING_SNAKE_CASE				)
  while True:
    try:
      lowerCAmelCase     :     List[str]								=							lines.popleft()
      if element.startswith("@highlight"				):
        break
      story_lines.append(SCREAMING_SNAKE_CASE				)
    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 SCREAMING_SNAKE_CASE				: not t.startswith("@highlight"				)				,      SCREAMING_SNAKE_CASE				)				)

  return story_lines, summary_lines
def 					a__			(				SCREAMING_SNAKE_CASE    :    Union[str, Any]				):


  '''simple docstring'''







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


  '''simple docstring'''







  if len(SCREAMING_SNAKE_CASE				) > block_size:
    return sequence[:block_size]
  else:
    sequence.extend([pad_token_id] * (block_size - len(SCREAMING_SNAKE_CASE				))				)
    return sequence
def 					a__			(				SCREAMING_SNAKE_CASE    :    List[Any]				,      SCREAMING_SNAKE_CASE    :    int				):


  '''simple docstring'''







  lowerCAmelCase     :     List[Any]								=							torch.ones_like(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     str								=							sequence == pad_token_id
  lowerCAmelCase     :     Optional[int]								=							0
  return mask
def 					a__			(				SCREAMING_SNAKE_CASE    :    str				,      SCREAMING_SNAKE_CASE    :    Tuple				,      SCREAMING_SNAKE_CASE    :    Any				):


  '''simple docstring'''







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

  return story_token_ids, summary_token_ids





def 					a__			(				SCREAMING_SNAKE_CASE    :    Dict				,      SCREAMING_SNAKE_CASE    :    Union[str, Any]				):


  '''simple docstring'''







  lowerCAmelCase     :     Optional[Any]								=							[]
  for sequence in batch:
    lowerCAmelCase     :     List[str]								=							-1
    lowerCAmelCase     :     str								=							[]
    for s in sequence:
      if s == separator_token_id:
        sentence_num += 1
      embeddings.append(sentence_num % 2				)
    batch_embeddings.append(SCREAMING_SNAKE_CASE				)
  return torch.tensor(SCREAMING_SNAKE_CASE				)









"""simple docstring"""







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


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   {
    '''facebook/dpr-ctx_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-ctx_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'''
    ),
}


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				int   ="dpr"

       def __init__(						self		,     snake_case__=30_522		,     snake_case__=768		,     snake_case__=12		,     snake_case__=12		,     snake_case__=3_072		,     snake_case__="gelu"		,     snake_case__=0.1		,     snake_case__=0.1		,     snake_case__=512		,     snake_case__=2		,     snake_case__=0.02		,     snake_case__=1e-12		,     snake_case__=0		,     snake_case__="absolute"		,     snake_case__ = 0		,     **snake_case__		,     ):






         """simple docstring"""





         super().__init__(pad_token_id=snake_case__		,     **snake_case__						)

         lowerCAmelCase     :     Union[str, Any]								=							vocab_size
         lowerCAmelCase     :     str								=							hidden_size
         lowerCAmelCase     :     Any								=							num_hidden_layers
         lowerCAmelCase     :     Optional[int]								=							num_attention_heads
         lowerCAmelCase     :     Union[str, Any]								=							hidden_act
         lowerCAmelCase     :     Dict								=							intermediate_size
         lowerCAmelCase     :     Union[str, Any]								=							hidden_dropout_prob
         lowerCAmelCase     :     Dict								=							attention_probs_dropout_prob
         lowerCAmelCase     :     Dict								=							max_position_embeddings
         lowerCAmelCase     :     Tuple								=							type_vocab_size
         lowerCAmelCase     :     Any								=							initializer_range
         lowerCAmelCase     :     Any								=							layer_norm_eps
         lowerCAmelCase     :     Dict								=							projection_dim
         lowerCAmelCase     :     Dict								=							position_embedding_type





