




'''simple docstring'''


from typing import TYPE_CHECKING

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


lowerCAmelCase_      :  Tuple				     =  {
    'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
    'configuration_data2vec_text': [
        'DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecTextConfig',
        'Data2VecTextOnnxConfig',
    ],
    'configuration_data2vec_vision': [
        'DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecVisionConfig',
        'Data2VecVisionOnnxConfig',
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  Optional[Any]				     =  [
     'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
     'Data2VecAudioForAudioFrameClassification',
     'Data2VecAudioForCTC',
     'Data2VecAudioForSequenceClassification',
     'Data2VecAudioForXVector',
     'Data2VecAudioModel',
     'Data2VecAudioPreTrainedModel',
 ]
 lowerCAmelCase_      :  Union[str, Any]				     =  [
     'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'Data2VecTextForCausalLM',
     'Data2VecTextForMaskedLM',
     'Data2VecTextForMultipleChoice',
     'Data2VecTextForQuestionAnswering',
     'Data2VecTextForSequenceClassification',
     'Data2VecTextForTokenClassification',
     'Data2VecTextModel',
     'Data2VecTextPreTrainedModel',
 ]
 lowerCAmelCase_      :  Union[str, Any]				     =  [
     'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
     'Data2VecVisionForImageClassification',
     'Data2VecVisionForMaskedImageModeling',
     'Data2VecVisionForSemanticSegmentation',
     'Data2VecVisionModel',
     'Data2VecVisionPreTrainedModel',
 ]

if is_tf_available():
 lowerCAmelCase_      :  int				     =  [
     'TFData2VecVisionForImageClassification',
     'TFData2VecVisionForSemanticSegmentation',
     'TFData2VecVisionModel',
     'TFData2VecVisionPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
 from .configuration_dataavec_text import (
     DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DataaVecTextConfig,
     DataaVecTextOnnxConfig,
 )
 from .configuration_dataavec_vision import (
     DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DataaVecVisionConfig,
     DataaVecVisionOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_dataavec_audio import (
      DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecAudioForAudioFrameClassification,
      DataaVecAudioForCTC,
      DataaVecAudioForSequenceClassification,
      DataaVecAudioForXVector,
      DataaVecAudioModel,
      DataaVecAudioPreTrainedModel,
  )
  from .modeling_dataavec_text import (
      DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecTextForCausalLM,
      DataaVecTextForMaskedLM,
      DataaVecTextForMultipleChoice,
      DataaVecTextForQuestionAnswering,
      DataaVecTextForSequenceClassification,
      DataaVecTextForTokenClassification,
      DataaVecTextModel,
      DataaVecTextPreTrainedModel,
  )
  from .modeling_dataavec_vision import (
      DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
      DataaVecVisionForImageClassification,
      DataaVecVisionForMaskedImageModeling,
      DataaVecVisionForSemanticSegmentation,
      DataaVecVisionModel,
      DataaVecVisionPreTrainedModel,
  )
 if is_tf_available():
  from .modeling_tf_dataavec_vision import (
      TFDataaVecVisionForImageClassification,
      TFDataaVecVisionForSemanticSegmentation,
      TFDataaVecVisionModel,
      TFDataaVecVisionPreTrainedModel,
  )

else:
 import sys

 lowerCAmelCase_      :  int				     =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






'''simple docstring'''


def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					list[int] ,    UpperCamelCase__    :					str				):




     """simple docstring"""


     a_     :							Tuple     						=  int(UpperCamelCase__				)

     # Initialize Result
     a_     :							Tuple     						=  []

     # 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__":
 lowerCAmelCase_      :  List[str]				     =  []
 lowerCAmelCase_      :  Union[str, Any]				     =  '0'

 if (
     input('Do you want to enter your denominations ? (yY/n): ').strip().lower()
     == "y"
 ):
  lowerCAmelCase_      :  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()))
  lowerCAmelCase_      :  Union[str, Any]				     =  input('Enter the change you want to make in Indian Currency: ').strip()
 else:
  # All denominations of Indian Currency if user does not enter
  lowerCAmelCase_      :  List[Any]				     =  [1, 2, 5, 10, 20, 50, 100, 500, 2000]
  lowerCAmelCase_      :  str				     =  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_      :  Optional[int]				     =  find_minimum_change(denominations, value)
  # Print result
  for i in range(len(answer)):
   print(answer[i], end=' ')
