




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 						__lowercase		(_A							):




      """simple docstring"""





      _snake_case												=						"""dandelin/vilt-b32-finetuned-vqa"""
      _snake_case												=						(
          """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."""
      )
      _snake_case												=						"""image_qa"""
      _snake_case												=						AutoProcessor
      _snake_case												=						AutoModelForVisualQuestionAnswering

      _snake_case												=						["""image""", """text"""]
      _snake_case												=						["""text"""]

      def __init__(				self    ,				*A    ,				**A     )		->    Tuple:
         requires_backends(self    ,				['''vision''']     )
         super().__init__(*__SCREAMING_SNAKE_CASE    ,				**__SCREAMING_SNAKE_CASE     )

      def      UpperCAmelCase				(				self    ,				A    ,				A     )		->    Optional[int]:
         return self.pre_processor(__SCREAMING_SNAKE_CASE    ,				__SCREAMING_SNAKE_CASE    ,				return_tensors='''pt'''     )

      def      UpperCAmelCase				(				self    ,				A     )		->    List[str]:
         with torch.no_grad():
            return self.model(**__SCREAMING_SNAKE_CASE     ).logits

      def      UpperCAmelCase				(				self    ,				A     )		->    Dict:
         snake_case				:    Union[str, Any]           =	outputs.argmax(-1     ).item()
         return self.model.config.idalabel[idx]











from collections import defaultdict




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       bool:
   snake_case				:    List[str]           =	first_str.lower().strip()
   snake_case				:    List[str]           =	second_str.lower().strip()

   # Remove whitespace
   snake_case				:    Any           =	first_str.replace(""" """						,""""""					)
   snake_case				:    List[str]           =	second_str.replace(""" """						,""""""					)

   # Strings of different lengths are not anagrams
   if len(lowercase					) != len(lowercase					):
      return False

   # Default values for count should be 0
   snake_case				:    defaultdict[str, int]           =	defaultdict(lowercase					)

   # For each character in input strings,
   # increment count in the corresponding
   for i in range(len(lowercase					)					):
      count[first_str[i]] += 1
      count[second_str[i]] -= 1

   return all(_count == 0 for _count in count.values()					)


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       lowerCamelCase	:	List[Any]				=   input('Enter the first string ').strip()
       lowerCamelCase	:	Optional[int]				=   input('Enter the second string ').strip()

       lowerCamelCase	:	Optional[Any]				=   check_anagrams(input_a, input_b)
       print(f"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")





