

'''simple docstring'''

import os
from collections import deque

import torch
from torch.utils.data import Dataset


class      lowerCAmelCase   (					__a      ):







       def __init__(							self	:       Optional[Any]  ,					__lowercase	:       str=""  ,					__lowercase	:       Union[str, Any]="train"					):







            """simple docstring"""







            assert os.path.isdir(lowerCAmelCase__					)

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







       def __len__(							self	:       int					):







            """simple docstring"""







            return len(self.documents					)







       def __getitem__(							self	:       Tuple  ,					__lowercase	:       List[Any]					):







            """simple docstring"""







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






def  __UpperCamelCase							(		lowercase__  :     Dict       ):






     '''simple docstring'''





     __lowercase							=list(filter(lambda lowercase__       : len(UpperCamelCase_       ) != 0,    [line.strip() for line in raw_story.split('\n'       )]       )       )

     # for some unknown reason some lines miss a period, add it
     __lowercase							=[_add_missing_period(UpperCamelCase_       ) for line in nonempty_lines]

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

     return story_lines, summary_lines

def  __UpperCamelCase							(		lowercase__  :     str       ):






     '''simple docstring'''





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

def  __UpperCamelCase							(		lowercase__  :     Tuple,    lowercase__  :     List[Any],    lowercase__  :     str       ):






     '''simple docstring'''





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

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






     '''simple docstring'''





     __lowercase							=torch.ones_like(UpperCamelCase_       )
     __lowercase							=sequence == pad_token_id
     __lowercase							=0
     return mask

def  __UpperCamelCase							(		lowercase__  :     Dict,    lowercase__  :     int,    lowercase__  :     List[Any]       ):






     '''simple docstring'''





     __lowercase							=[tokenizer.encode(UpperCamelCase_       ) for line in story_lines]
     __lowercase							=[token for sentence in story_lines_token_ids for token in sentence]
     __lowercase							=[tokenizer.encode(UpperCamelCase_       ) for line in summary_lines]
     __lowercase							=[token for sentence in summary_lines_token_ids for token in sentence]

     return story_token_ids, summary_token_ids







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






     '''simple docstring'''





     __lowercase							=[]
     for sequence in batch:
          __lowercase							=-1
          __lowercase							=[]
          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"""





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
							from PIL import Image
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  Tuple						   =		'''dandelin/vilt-b32-finetuned-vqa'''
		__lowercase							:  str						   =		(
		    '''This is a tool that answers a question about an image. It takes an input named `image` which should be the '''
		    '''image containing the information, as well as a `question` which should be the question in English. It '''
		    '''returns a text that is the answer to the question.'''
		)
		__lowercase							:  Optional[int]						   =		'''image_qa'''
		__lowercase							:  Union[str, Any]						   =		AutoProcessor
		__lowercase							:  Optional[Any]						   =		AutoModelForVisualQuestionAnswering

		__lowercase							:  List[str]						   =		['''image''', '''text''']
		__lowercase							:  Optional[Any]						   =		['''text''']




		def __init__(     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				requires_backends(self							,  ["""vision"""])
				super().__init__(*lowerCAmelCase__							,  **lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__):
				return self.pre_processor(lowerCAmelCase__							,  lowerCAmelCase__							,  return_tensors="""pt""")




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				with torch.no_grad():
						return self.model(**lowerCAmelCase__).logits




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = outputs.argmax(-1).item()
				return self.model.config.idalabel[idx]




