






'''simple docstring'''



import os
from collections import deque

import torch
from torch.utils.data import Dataset
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):




      def __init__(  self ,   lowerCamelCase="" ,   lowerCamelCase="train"      )							->Optional[int]:



         '''simple docstring'''
         assert os.path.isdir(lowerCamelCase      )

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




      def __len__(  self      )							->Dict:



         '''simple docstring'''
         return len(self.documents      )






      def __getitem__(  self ,   lowerCamelCase      )							->Any:



         '''simple docstring'''
         __a				     =    self.documents[idx]
         __a				     =    document_path.split('/'      )[-1]
         with open(lowerCamelCase ,   encoding='utf-8'      ) as source:
            __a				     =    source.read()
            __a     ,			__a				     =    process_story(lowerCamelCase      )
         return document_name, story_lines, summary_lines







def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Any    )  ->			Union[str, Any]:

   """simple docstring"""
   __a				     =    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
   __a				     =    [_add_missing_period(SCREAMING_SNAKE_CASE__    ) for line in nonempty_lines]

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

   return story_lines, summary_lines



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	List[Any]    )  ->			Any:

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



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Optional[int],					SCREAMING_SNAKE_CASE__:	Dict,					SCREAMING_SNAKE_CASE__:	Optional[Any]    )  ->			Optional[int]:

   """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    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int,					SCREAMING_SNAKE_CASE__:	Union[str, Any]    )  ->			Optional[Any]:

   """simple docstring"""
   __a				     =    torch.ones_like(SCREAMING_SNAKE_CASE__    )
   __a				     =    sequence == pad_token_id
   __a				     =    0
   return mask



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Dict,					SCREAMING_SNAKE_CASE__:	Union[str, Any],					SCREAMING_SNAKE_CASE__:	Any    )  ->			List[Any]:

   """simple docstring"""
   __a				     =    [tokenizer.encode(SCREAMING_SNAKE_CASE__    ) for line in story_lines]
   __a				     =    [token for sentence in story_lines_token_ids for token in sentence]
   __a				     =    [tokenizer.encode(SCREAMING_SNAKE_CASE__    ) for line in summary_lines]
   __a				     =    [token for sentence in summary_lines_token_ids for token in sentence]

   return story_token_ids, summary_token_ids



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Optional[int],					SCREAMING_SNAKE_CASE__:	List[str]    )  ->			Union[str, Any]:

   """simple docstring"""
   __a				     =    []
   for sequence in batch:
      __a				     =    -1
      __a				     =    []
      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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase      :       Tuple      			=      {
    """configuration_blip_2""": [
        """BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Blip2Config""",
        """Blip2QFormerConfig""",
        """Blip2VisionConfig""",
    ],
    """processing_blip_2""": ["""Blip2Processor"""],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Optional[int]      			=      [
     """BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """Blip2Model""",
     """Blip2QFormerModel""",
     """Blip2PreTrainedModel""",
     """Blip2ForConditionalGeneration""",
     """Blip2VisionModel""",
 ]

if TYPE_CHECKING:
 from .configuration_blip_a import (
     BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BlipaConfig,
     BlipaQFormerConfig,
     BlipaVisionConfig,
 )
 from .processing_blip_a import BlipaProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_blip_a import (
      BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      BlipaForConditionalGeneration,
      BlipaModel,
      BlipaPreTrainedModel,
      BlipaQFormerModel,
      BlipaVisionModel,
  )

else:
 import sys

 __UpperCamelCase      :       Dict      			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)