
'''simple docstring'''



def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Any:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          =				int(_lowerCamelCase						)

   # Initialize Result
   _SCREAMING_SNAKE_CASE          =				[]

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

   return answer


# Driver Code
if __name__ == "__main__":
     UpperCamelCase__      :		Dict       							=  []
     UpperCamelCase__      :		Tuple       							=  "0"

     if (
         input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
         == "y"
     ):
          UpperCamelCase__      :		str       							=  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()))
          UpperCamelCase__      :		str       							=  input("Enter the change you want to make in Indian Currency: ").strip()
     else:
          # All denominations of Indian Currency if user does not enter
          UpperCamelCase__      :		Dict       							=  [1, 2, 5, 10, 20, 50, 100, 500, 2_000]
          UpperCamelCase__      :		Tuple       							=  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}: """)
          UpperCamelCase__      :		Dict       							=  find_minimum_change(denominations, value)
          # Print result
          for i in range(len(answer)):
               print(answer[i], end=" ")








'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase__      :		str       							=  {
    "configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig"],
    "tokenization_canine": ["CanineTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__      :		List[Any]       							=  [
         "CANINE_PRETRAINED_MODEL_ARCHIVE_LIST",
         "CanineForMultipleChoice",
         "CanineForQuestionAnswering",
         "CanineForSequenceClassification",
         "CanineForTokenClassification",
         "CanineLayer",
         "CanineModel",
         "CaninePreTrainedModel",
         "load_tf_weights_in_canine",
     ]


if TYPE_CHECKING:
     from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
     from .tokenization_canine import CanineTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_canine import (
              CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
              CanineForMultipleChoice,
              CanineForQuestionAnswering,
              CanineForSequenceClassification,
              CanineForTokenClassification,
              CanineLayer,
              CanineModel,
              CaninePreTrainedModel,
              load_tf_weights_in_canine,
          )


else:
     import sys

     UpperCamelCase__      :		str       							=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






