"""simple docstring"""






def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							int(lowerCamelCase       )

				# Initialize Result
				UpperCAmelCase__             =							[]

				# 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__":
						lowerCAmelCase__  :  List[str]						     =	[]
						lowerCAmelCase__  :  Optional[int]						     =	'''0'''

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










from typing import TYPE_CHECKING

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


UpperCAmelCase						:   Optional[int]			  =     {
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   Union[str, Any]			  =     [
				    '''XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''XLMForMultipleChoice''',
				    '''XLMForQuestionAnswering''',
				    '''XLMForQuestionAnsweringSimple''',
				    '''XLMForSequenceClassification''',
				    '''XLMForTokenClassification''',
				    '''XLMModel''',
				    '''XLMPreTrainedModel''',
				    '''XLMWithLMHeadModel''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   Optional[Any]			  =     [
				    '''TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFXLMForMultipleChoice''',
				    '''TFXLMForQuestionAnsweringSimple''',
				    '''TFXLMForSequenceClassification''',
				    '''TFXLMForTokenClassification''',
				    '''TFXLMMainLayer''',
				    '''TFXLMModel''',
				    '''TFXLMPreTrainedModel''',
				    '''TFXLMWithLMHeadModel''',
				]


if TYPE_CHECKING:
				from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
				from .tokenization_xlm import XLMTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xlm import (
								    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    XLMForMultipleChoice,
								    XLMForQuestionAnswering,
								    XLMForQuestionAnsweringSimple,
								    XLMForSequenceClassification,
								    XLMForTokenClassification,
								    XLMModel,
								    XLMPreTrainedModel,
								    XLMWithLMHeadModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_xlm import (
								    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFXLMForMultipleChoice,
								    TFXLMForQuestionAnsweringSimple,
								    TFXLMForSequenceClassification,
								    TFXLMForTokenClassification,
								    TFXLMMainLayer,
								    TFXLMModel,
								    TFXLMPreTrainedModel,
								    TFXLMWithLMHeadModel,
								)

else:
				import sys

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




