
"""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 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





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

		__lowercase							:  List[str]						   =		['''image''', '''text''']
		__lowercase							:  Optional[Any]						   =		['''text''']




		def __init__(     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				requires_backends(self							,  ["""vision"""])
				super().__init__(*lowerCAmelCase__							,  **lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__):
				return self.pre_processor(lowerCAmelCase__							,  lowerCAmelCase__							,  return_tensors="""pt""")




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				with torch.no_grad():
						return self.model(**lowerCAmelCase__).logits




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = outputs.argmax(-1).item()
				return self.model.config.idalabel[idx]






"""simple docstring"""





from __future__ import annotations

from fractions import Fraction



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		return (
		    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
		)



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = []
		__SCREAMING_SNAKE_CASE            = 11
		__SCREAMING_SNAKE_CASE            = int("""1""" + """0""" * digit_len				)
		for num in range(UpperCamelCase_  ,				UpperCamelCase_				):
				while den <= 99:
						if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
								if is_digit_cancelling(UpperCamelCase_  ,				UpperCamelCase_				):
										solutions.append(f"{num}/{den}"				)
						den += 1
				num += 1
				__SCREAMING_SNAKE_CASE            = 10
		return solutions



def 						_lowerCAmelCase				(				UpperCamelCase_ = 2				):
		__SCREAMING_SNAKE_CASE            = 1.0
		for fraction in fraction_list(UpperCamelCase_				):
				__SCREAMING_SNAKE_CASE            = Fraction(UpperCamelCase_				)
				result *= frac.denominator / frac.numerator
		return int(UpperCamelCase_				)


if __name__ == "__main__":
							print(solution())




