
def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   str					=				int(snake_case__		)

      # Initialize Result
      UpperCamelCase__    :   str					=				[]

      # Traverse through all denomination
      for denomination in reversed(snake_case__		):
            # Find denominations
            while int(snake_case__		) >= int(snake_case__		):
                  total_value -= int(snake_case__		)
                  answer.append(snake_case__		)  # Append the "answers" array

      return answer


# Driver Code
if __name__ == "__main__":
    lowerCamelCase      =[]
    lowerCamelCase      ="0"

    if (
        input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
        == "y"
    ):
        lowerCamelCase      =int(input("Enter the number of denominations you want to add: ").strip())

        for i in range(0, n):
            denominations.append(int(input(F'''Denomination {i}: ''').strip()))
        lowerCamelCase      =input("Enter the change you want to make in Indian Currency: ").strip()
    else:
        # All denominations of Indian Currency if user does not enter
        lowerCamelCase      =[1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 5_0_0, 2_0_0_0]
        lowerCamelCase      =input("Enter the change you want to make: ").strip()

    if int(value) == 0 or int(value) < 0:
        print("The total value cannot be zero or negative.")

    else:
        print(F'''Following is minimal change for {value}: ''')
        lowerCamelCase      =find_minimum_change(denominations, value)
        # Print result
        for i in range(len(answer)):
            print(answer[i], end=" ")




import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  '''M-CLIP'''






     def __init__(							self						,						__SCREAMING_SNAKE_CASE=1_0_2_4						,						__SCREAMING_SNAKE_CASE=7_6_8						,						**__SCREAMING_SNAKE_CASE  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				transformerDimSize
           UpperCamelCase__    :   Any					=				imageDimSize
           super().__init__(**__SCREAMING_SNAKE_CASE  )




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  MCLIPConfig






     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )  ->   Optional[int]:




           """simple docstring"""







           super().__init__(__SCREAMING_SNAKE_CASE						,						*__SCREAMING_SNAKE_CASE						,						**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Union[str, Any]					=				XLMRobertaModel(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Tuple					=				torch.nn.Linear(
               in_features=config.transformerDimensions						,						out_features=config.numDims  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				self.transformer(input_ids=__SCREAMING_SNAKE_CASE						,						attention_mask=__SCREAMING_SNAKE_CASE  )[0]
           UpperCamelCase__    :   Tuple					=				(embs * attention_mask.unsqueeze(2  )).sum(dim=1  ) / attention_mask.sum(dim=1  )[:, None]
           return self.LinearTransformation(__SCREAMING_SNAKE_CASE  ), embs


