
# 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   UpperCAmelCase		(						A_ ):
   A__					:		List[Any]						     =     "dandelin/vilt-b32-finetuned-vqa"
   A__					:		List[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."
   )
   A__					:		str						     =     "image_qa"
   A__					:		List[str]						     =     AutoProcessor
   A__					:		Dict						     =     AutoModelForVisualQuestionAnswering

   A__					:		List[str]						     =     ["image", "text"]
   A__					:		Union[str, Any]						     =     ["text"]



   def __init__(self		:					int    ,						*snake_case__		:					int    ,						**snake_case__		:					str )		->	List[Any]:


          '''simple docstring'''


          requires_backends(self    ,						["vision"] )
          super().__init__(*snake_case__    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					"Image"    ,						snake_case__		:					str )		->	Tuple:


          '''simple docstring'''


          return self.pre_processor(snake_case__    ,						snake_case__    ,						return_tensors="pt" )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					int )		->	Union[str, Any]:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					List[Any] )		->	List[Any]:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      outputs.argmax(-1 ).item()
          return self.model.config.idalabel[idx]










_lowercase: Dict      			=		[
    (1000, "M"),
    (900, "CM"),
    (500, "D"),
    (400, "CD"),
    (100, "C"),
    (90, "XC"),
    (50, "L"),
    (40, "XL"),
    (10, "X"),
    (9, "IX"),
    (5, "V"),
    (4, "IV"),
    (1, "I"),
]






def     a(      A       :    str       )		->       int:
					"""simple docstring"""





					a					     =   {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
					a					     =   0
					a					     =   0
					while place < len(A       ):
										if (place + 1 < len(A       )) and (vals[roman[place]] < vals[roman[place + 1]]):
															total += vals[roman[place + 1]] - vals[roman[place]]
															place += 2
										else:
															total += vals[roman[place]]
															place += 1
					return total






def     a(      A       :    int       )		->       str:
					"""simple docstring"""





					a					     =   []
					for arabic, roman in ROMAN:
										((a)     ,				(a))					     =   divmod(A ,     A       )
										result.append(roman * factor       )
										if number == 0:
															break
					return "".join(A       )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
