






'''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 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      :		List[Any]  ,   *UpperCAmelCase      :		List[str]  ,   **UpperCAmelCase      :		Dict		)	->	Dict:


       '''simple docstring'''

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






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


       '''simple docstring'''

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






      def 					A__   (			self      :		int  ,   UpperCAmelCase      :		Optional[int]		)	->	Union[str, Any]:


       '''simple docstring'''

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






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


       '''simple docstring'''

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











'''simple docstring'''



from math import isqrt


def 				lowercase_					(     __A			:							int      )							->   list[int]:



 """simple docstring"""




 lowercase       : Dict						=[True] * max_number
 for i in range(2       ,   isqrt(max_number - 1      ) + 1      ):
  if is_prime[i]:
   for j in range(i**2       ,   __A       ,   __A      ):
    lowercase       : str						=False

 return [i for i in range(2       ,   __A      ) if is_prime[i]]


def 				lowercase_					(     __A			:							int = 1_0**8      )							->   int:



 """simple docstring"""




 lowercase       : Dict						=calculate_prime_numbers(max_number // 2      )

 lowercase       : str						=0
 lowercase       : Optional[Any]						=0
 lowercase       : Union[str, Any]						=len(__A      ) - 1
 while left <= right:
  while prime_numbers[left] * prime_numbers[right] >= max_number:
   right -= 1
  semiprimes_count += right - left + 1
  left += 1

 return semiprimes_count


if __name__ == "__main__":
      print(f"""{solution() = }""")



