




'''simple docstring'''



def   lowercase__(			__UpperCamelCase:					list[int]      ,__UpperCamelCase:					str    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      List[str]    					=						int(__UpperCamelCase    )

      # Initialize Result
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[]

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

      return answer


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

      if (
          input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
          == "y"
      ):
            UpperCamelCase_    	=				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_    	=				input("Enter the change you want to make in Indian Currency: ").strip()
      else:
            # All denominations of Indian Currency if user does not enter
            UpperCamelCase_    	=				[1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 5_0_0, 2_0_0_0]
            UpperCamelCase_    	=				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_    	=				find_minimum_change(denominations, value)
            # Print result
            for i in range(len(answer)):
                  print(answer[i], end=" ")




# Copyright 2023 The HuggingFace 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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_snake_case      = {
    '''configuration_efficientnet''': [
        '''EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EfficientNetConfig''',
        '''EfficientNetOnnxConfig''',
    ]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case      = ['''EfficientNetImageProcessor''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case      = [
     '''EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''EfficientNetForImageClassification''',
     '''EfficientNetModel''',
     '''EfficientNetPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_efficientnet import (
     EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
     EfficientNetConfig,
     EfficientNetOnnxConfig,
 )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_efficientnet import EfficientNetImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_efficientnet import (
      EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      EfficientNetForImageClassification,
      EfficientNetModel,
      EfficientNetPreTrainedModel,
  )

else:
 import sys

 _snake_case      = _LazyModule(__name__, globals()['''__file__'''], _import_structure)


