






'''simple docstring'''



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				):




      """simple docstring"""




      UpperCamelCase_								=			'''dandelin/vilt-b32-finetuned-vqa'''
      UpperCamelCase_								=			(
          '''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.'''
      )
      UpperCamelCase_								=			'''image_qa'''
      UpperCamelCase_								=			AutoProcessor
      UpperCamelCase_								=			AutoModelForVisualQuestionAnswering

      UpperCamelCase_								=			['''image''', '''text''']
      UpperCamelCase_								=			['''text''']






      def __init__( self			:		Optional[int]     ,      *UpperCAmelCase			:		Any     ,      **UpperCAmelCase			:		Dict		)  ->  Dict:


       '''simple docstring'''

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






      def 				A__      ( self			:		Tuple     ,      UpperCAmelCase			:		"Image"     ,      UpperCAmelCase			:		str		)  ->  Tuple:


       '''simple docstring'''

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






      def 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		List[str]		)  ->  Optional[int]:


       '''simple docstring'''

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






      def 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		Tuple		)  ->  List[str]:


       '''simple docstring'''

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











'''simple docstring'''



def 							lowercase_				(    __A :       int					,      __A :       int			)       ->      str:



 """simple docstring"""




 if number < 0 or shift_amount < 0:
  raise ValueError('''both inputs must be positive integers'''			)

 lowercase					:   List[Any]				  =str(bin(__A			)			)
 binary_number += "0" * shift_amount
 return binary_number


def 							lowercase_				(    __A :       int					,      __A :       int			)       ->      str:



 """simple docstring"""




 if number < 0 or shift_amount < 0:
  raise ValueError('''both inputs must be positive integers'''			)

 lowercase					:   Union[str, Any]				  =str(bin(__A			)			)[2:]
 if shift_amount >= len(__A			):
  return "0b0"
 lowercase					:   Any				  =binary_number[: len(__A			) - shift_amount]
 return "0b" + shifted_binary_number


def 							lowercase_				(    __A :       int					,      __A :       int			)       ->      str:



 """simple docstring"""




 if number >= 0:  # Get binary representation of positive number
  lowercase					:   str				  ='''0''' + str(bin(__A			)			).strip('''-'''			)[2:]
 else:  # Get binary (2's complement) representation of negative number
  lowercase					:   Dict				  =len(bin(__A			)[3:]			)  # Find 2's complement of number
  lowercase					:   Optional[Any]				  =bin(abs(__A			) - (1 << binary_number_length)			)[3:]
  lowercase					:   int				  =(
      '''1''' + '''0''' * (binary_number_length - len(__A			)) + binary_number
  )

 if shift_amount >= len(__A			):
  return "0b" + binary_number[0] * len(__A			)
 return (
     "0b"
     + binary_number[0] * shift_amount
     + binary_number[: len(__A			) - shift_amount]
 )


if __name__ == "__main__":
      import doctest

      doctest.testmod()



