



'''simple docstring'''






def  snake_case_     (					_lowerCAmelCase  :						list[int]   ,      _lowerCAmelCase  :						str  )      ->   list[int]:
							UpperCAmelCase :       Optional[Any] 							=		int(_lowerCAmelCase  )

							# Initialize Result
							UpperCAmelCase :       List[Any] 							=		[]

							# 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__":
						UpperCamelCase__:   int								=			[]
						UpperCamelCase__:   Optional[Any]								=			"0"

						if (
						    input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
						    == "y"
						):
												UpperCamelCase__:   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()))
												UpperCamelCase__:   int								=			input("Enter the change you want to make in Indian Currency: ").strip()
						else:
												# All denominations of Indian Currency if user does not enter
												UpperCamelCase__:   Any								=			[1, 2, 5, 10, 20, 50, 100, 500, 2000]
												UpperCamelCase__:   Any								=			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__:   Union[str, Any]								=			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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__:   str								=			{
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:   Union[str, Any]								=			[
						    "LxmertEncoder",
						    "LxmertForPreTraining",
						    "LxmertForQuestionAnswering",
						    "LxmertModel",
						    "LxmertPreTrainedModel",
						    "LxmertVisualFeatureEncoder",
						    "LxmertXLayer",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase__:   int								=			[
						    "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFLxmertForPreTraining",
						    "TFLxmertMainLayer",
						    "TFLxmertModel",
						    "TFLxmertPreTrainedModel",
						    "TFLxmertVisualFeatureEncoder",
						]


if TYPE_CHECKING:
						from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
						from .tokenization_lxmert import LxmertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_lxmert_fast import LxmertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_lxmert import (
												    LxmertEncoder,
												    LxmertForPreTraining,
												    LxmertForQuestionAnswering,
												    LxmertModel,
												    LxmertPreTrainedModel,
												    LxmertVisualFeatureEncoder,
												    LxmertXLayer,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_lxmert import (
												    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFLxmertForPreTraining,
												    TFLxmertMainLayer,
												    TFLxmertModel,
												    TFLxmertPreTrainedModel,
												    TFLxmertVisualFeatureEncoder,
												)

else:
						import sys

						UpperCamelCase__:   Tuple								=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





