

"""simple docstring"""




def       lowercase__						(       snake_case_   :list[int]			,		snake_case_   :str    ):
	__UpperCAmelCase      		= int(snake_case_    )

	# Initialize Result
	__UpperCAmelCase      		= []

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

				if (
				    input('Do you want to enter your denominations ? (yY/n): ').strip().lower()
				    == "y"
				):
								_lowercase   :	List[Any]												=						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()))
								_lowercase   :	Optional[Any]												=						input('Enter the change you want to make in Indian Currency: ').strip()
				else:
								# All denominations of Indian Currency if user does not enter
								_lowercase   :	List[Any]												=						[1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
								_lowercase   :	List[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}: """)
								_lowercase   :	Optional[int]												=						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


A_     :						Optional[Any]    							={
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 A_     :						Dict    							=['''SqueezeBertTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 A_     :						Optional[int]    							=[
     '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''SqueezeBertForMaskedLM''',
     '''SqueezeBertForMultipleChoice''',
     '''SqueezeBertForQuestionAnswering''',
     '''SqueezeBertForSequenceClassification''',
     '''SqueezeBertForTokenClassification''',
     '''SqueezeBertModel''',
     '''SqueezeBertModule''',
     '''SqueezeBertPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_squeezebert import (
     SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     SqueezeBertConfig,
     SqueezeBertOnnxConfig,
 )
 from .tokenization_squeezebert import SqueezeBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_squeezebert import (
      SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      SqueezeBertForMaskedLM,
      SqueezeBertForMultipleChoice,
      SqueezeBertForQuestionAnswering,
      SqueezeBertForSequenceClassification,
      SqueezeBertForTokenClassification,
      SqueezeBertModel,
      SqueezeBertModule,
      SqueezeBertPreTrainedModel,
  )

else:
 import sys

 A_     :						List[str]    							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





