"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_a  :  Union[str, Any]=				{
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  int=				["RobertaTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  Dict=				[
	    "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "RobertaForCausalLM",
	    "RobertaForMaskedLM",
	    "RobertaForMultipleChoice",
	    "RobertaForQuestionAnswering",
	    "RobertaForSequenceClassification",
	    "RobertaForTokenClassification",
	    "RobertaModel",
	    "RobertaPreTrainedModel",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  str=				[
	    "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFRobertaForCausalLM",
	    "TFRobertaForMaskedLM",
	    "TFRobertaForMultipleChoice",
	    "TFRobertaForQuestionAnswering",
	    "TFRobertaForSequenceClassification",
	    "TFRobertaForTokenClassification",
	    "TFRobertaMainLayer",
	    "TFRobertaModel",
	    "TFRobertaPreTrainedModel",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  List[str]=				[
	    "FlaxRobertaForCausalLM",
	    "FlaxRobertaForMaskedLM",
	    "FlaxRobertaForMultipleChoice",
	    "FlaxRobertaForQuestionAnswering",
	    "FlaxRobertaForSequenceClassification",
	    "FlaxRobertaForTokenClassification",
	    "FlaxRobertaModel",
	    "FlaxRobertaPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
	from .tokenization_roberta import RobertaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_roberta_fast import RobertaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta import (
		    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaForCausalLM,
		    RobertaForMaskedLM,
		    RobertaForMultipleChoice,
		    RobertaForQuestionAnswering,
		    RobertaForSequenceClassification,
		    RobertaForTokenClassification,
		    RobertaModel,
		    RobertaPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta import (
		    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaForCausalLM,
		    TFRobertaForMaskedLM,
		    TFRobertaForMultipleChoice,
		    TFRobertaForQuestionAnswering,
		    TFRobertaForSequenceClassification,
		    TFRobertaForTokenClassification,
		    TFRobertaMainLayer,
		    TFRobertaModel,
		    TFRobertaPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta import (
		    FlaxRobertaForCausalLM,
		    FlaxRobertaForMaskedLM,
		    FlaxRobertaForMultipleChoice,
		    FlaxRobertaForQuestionAnswering,
		    FlaxRobertaForSequenceClassification,
		    FlaxRobertaForTokenClassification,
		    FlaxRobertaModel,
		    FlaxRobertaPreTrainedModel,
		)

else:
	import sys

	_a  :  int=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






"""simple docstring"""







def 						__UpperCAmelCase       (					UpperCAmelCase_					:       list[int]    ,							UpperCAmelCase_					:       str						)							->    list[int]:

	'''simple docstring'''





	__snake_case    :							Union[str, Any]   	=				int(UpperCAmelCase_						)

	# Initialize Result
	__snake_case    :							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__":
	_a  :  Optional[int]=				[]
	_a  :  Optional[int]=				"0"

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





