





import argparse
import os
import re


snake_case								=							"""src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
snake_case								=							re.compile(R"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
snake_case								=							re.compile(R"""\s*\(\s*\"(\S[^\"]+)\"""")

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Any      ,				snake_case__	:bool = False  )   ->    int:
  with open(snake_case__      ,				'r'      ,				encoding='utf-8'  ) as f:
    _lowercase						    =			f.read()

  _lowercase						    =			content.split('\n'  )
  _lowercase						    =			[]
  _lowercase						    =			0
  while line_idx < len(snake_case__  ):
    if _re_intro_mapping.search(lines[line_idx]  ) is not None:
      _lowercase						    =			len(re.search(R'^(\s*)\S'      ,				lines[line_idx]  ).groups()[0]  ) + 8
      # Start of a new mapping!
      while not lines[line_idx].startswith(' ' * indent + '('  ):
        new_lines.append(lines[line_idx]  )
        line_idx += 1

      _lowercase						    =			[]
      while lines[line_idx].strip() != "]":
        # Blocks either fit in one line or not
        if lines[line_idx].strip() == "(":
          _lowercase						    =			line_idx
          while not lines[line_idx].startswith(' ' * indent + ')'  ):
            line_idx += 1
          blocks.append('\n'.join(lines[start_idx : line_idx + 1]  )  )
        else:
          blocks.append(lines[line_idx]  )
        line_idx += 1

      # Sort blocks by their identifiers
      _lowercase						    =			sorted(snake_case__      ,				key=lambda snake_case__  : _re_identifier.search(snake_case__  ).groups()[0]  )
      new_lines += blocks
    else:
      new_lines.append(lines[line_idx]  )
      line_idx += 1

  if overwrite:
    with open(snake_case__      ,				'w'      ,				encoding='utf-8'  ) as f:
      f.write('\n'.join(snake_case__  )  )
  elif "\n".join(snake_case__  ) != content:
    return True

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:bool = False  )   ->    Tuple:
  _lowercase						    =			[os.path.join(snake_case__      ,				snake_case__  ) for f in os.listdir(snake_case__  ) if f.endswith('.py'  )]
  _lowercase						    =			[sort_auto_mapping(snake_case__      ,				overwrite=snake_case__  ) for fname in fnames]

  if not overwrite and any(snake_case__  ):
    _lowercase						    =			[f for f, d in zip(snake_case__      ,				snake_case__  ) if d]
    raise ValueError(
        F"""The following files have auto mappings that need sorting: {", ".join(snake_case__  )}. Run `make style` to fix"""
        ' this.'  )


if __name__ == "__main__":
      snake_case								=							argparse.ArgumentParser()
      parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
      snake_case								=							parser.parse_args()

      sort_all_auto_mappings(not args.check_only)



'''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     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		int     				=    '''dandelin/vilt-b32-finetuned-vqa'''
 __lowercase					:		int     				=    (
     '''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					:		Union[str, Any]     				=    '''image_qa'''
 __lowercase					:		Optional[Any]     				=    AutoProcessor
 __lowercase					:		Tuple     				=    AutoModelForVisualQuestionAnswering

 __lowercase					:		Tuple     				=    ['''image''', '''text''']
 __lowercase					:		List[str]     				=    ['''text''']


 def __init__(   self   ,   *__lowercase   ,   **__lowercase    ):

  """simple docstring"""







  requires_backends(self   ,   ['vision']    )
  super().__init__(*__lowercase   ,   **__lowercase    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  return self.pre_processor(__lowercase   ,   __lowercase   ,   return_tensors='pt'    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  with torch.no_grad():
   return self.model(**__lowercase    ).logits







 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		List[str]	  =      outputs.argmax(-1    ).item()
  return self.model.config.idalabel[idx]






