


'''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      a    (		_a	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			List[Any]							       =       "dandelin/vilt-b32-finetuned-vqa"
     SCREAMING_SNAKE_CASE	:			Union[str, Any]							       =       (
         "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."
     )
     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       "image_qa"
     SCREAMING_SNAKE_CASE	:			Union[str, Any]							       =       AutoProcessor
     SCREAMING_SNAKE_CASE	:			Optional[Any]							       =       AutoModelForVisualQuestionAnswering

     SCREAMING_SNAKE_CASE	:			List[Any]							       =       ["image", "text"]
     SCREAMING_SNAKE_CASE	:			Dict							       =       ["text"]

     def __init__(    self					:		Dict							,			*snake_case					:		Any							,			**snake_case					:		Union[str, Any]      )						->							Optional[Any]:
          requires_backends(self							,			['''vision''']      )
          super().__init__(*snake_case							,			**snake_case      )

     def 			lowerCamelCase__			(    self					:		List[str]							,			snake_case					:		"Image"							,			snake_case					:		str      )						->							str:
          return self.pre_processor(snake_case							,			snake_case							,			return_tensors='''pt'''      )

     def 			lowerCamelCase__			(    self					:		Union[str, Any]							,			snake_case					:		Any      )						->							Tuple:
          with torch.no_grad():
               return self.model(**snake_case      ).logits






     def 			lowerCamelCase__			(    self					:		Optional[Any]							,			snake_case					:		Any      )						->							int:
          __UpperCAmelCase     :			List[Any]  							=      outputs.argmax(-1      ).item()
          return self.model.config.idalabel[idx]



'''simple docstring'''



from argparse import ArgumentParser

from .env import EnvironmentCommand




def      _a     (						):





     '''simple docstring'''






     __UpperCAmelCase     :			Optional[Any]  							=      ArgumentParser('''Diffusers CLI tool'''      , usage='''diffusers-cli <command> [<args>]'''  )
     __UpperCAmelCase     :			Union[str, Any]  							=      parser.add_subparsers(help='''diffusers-cli command helpers'''  )

     # Register commands
     EnvironmentCommand.register_subcommand(_lowercase  )

     # Let's go
     __UpperCAmelCase     :			Union[str, Any]  							=      parser.parse_args()

     if not hasattr(_lowercase      , '''func'''  ):
          parser.print_help()
          exit(1  )

     # Run
     __UpperCAmelCase     :			Any  							=      args.func(_lowercase  )
     service.run()


if __name__ == "__main__":
      main()