





from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
						import timm


if is_torch_available():
						from torch import Tensor
class SCREAMING_SNAKE_CASE_  (   snake_case_    ,			snake_case_   ):
						__magic_name__:     Union[str, Any]	 =    "pixel_values"
						__magic_name__:     Union[str, Any]	 =    False
						__magic_name__:     Optional[int]	 =    TimmBackboneConfig






						def __init__( self : Optional[Any]						,				_A : str						,				**_A : List[Any]		)							->      str:







							"""simple docstring"""


							requires_backends(self						,				'timm'		)
							super().__init__(_A		)
							snake_case_						:     Union[str, Any]     =		config

							if config.backbone is None:
								raise ValueError('backbone is not set in the config. Please set it to a timm model name.'		)

							if config.backbone not in timm.list_models():
								raise ValueError(F"""backbone {config.backbone} is not supported by timm."""		)

							if hasattr(_A						,				'out_features'		) and config.out_features is not None:
								raise ValueError('out_features is not supported by TimmBackbone. Please use out_indices instead.'		)

							snake_case_						:     Tuple     =		getattr(_A						,				'use_pretrained_backbone'						,				_A		)
							if pretrained is None:
								raise ValueError('use_pretrained_backbone is not set in the config. Please set it to True or False.'		)

							# We just take the final layer by default. This matches the default for the transformers models.
							snake_case_						:     Union[str, Any]     =		config.out_indices if getattr(_A						,				'out_indices'						,				_A		) is not None else (-1,)

							snake_case_						:     Optional[int]     =		timm.create_model(
							    config.backbone						,				pretrained=_A						,				features_only=config.features_only						,				in_chans=config.num_channels						,				out_indices=_A						,				**_A						,				)
							# These are used to control the output of the model when called. If output_hidden_states is True, then
							# return_layers is modified to include all layers.
							snake_case_						:     Optional[int]     =		self._backbone.return_layers
							snake_case_						:     Dict     =		{layer['module']: str(_A		) for i, layer in enumerate(self._backbone.feature_info.info		)}
							super()._init_backbone(_A		)






						@classmethod
						def UpperCAmelCase_			( cls : Union[str, Any]						,				_A : List[str]						,				*_A : Union[str, Any]						,				**_A : Optional[Any]		)							->      Union[str, Any]:







							"""simple docstring"""


							requires_backends(cls						,				['vision', 'timm']		)
							from ...models.timm_backbone import TimmBackboneConfig

							snake_case_						:     int     =		kwargs.pop('config'						,				TimmBackboneConfig()		)

							snake_case_						:     Optional[Any]     =		kwargs.pop('use_timm_backbone'						,				_A		)
							if not use_timm:
								raise ValueError('use_timm_backbone must be True for timm backbones'		)

							snake_case_						:     Tuple     =		kwargs.pop('num_channels'						,				config.num_channels		)
							snake_case_						:     int     =		kwargs.pop('features_only'						,				config.features_only		)
							snake_case_						:     str     =		kwargs.pop('use_pretrained_backbone'						,				config.use_pretrained_backbone		)
							snake_case_						:     Optional[int]     =		kwargs.pop('out_indices'						,				config.out_indices		)
							snake_case_						:     Optional[Any]     =		TimmBackboneConfig(
							    backbone=_A						,				num_channels=_A						,				features_only=_A						,				use_pretrained_backbone=_A						,				out_indices=_A						,				)
							return super()._from_config(_A						,				**_A		)






						def UpperCAmelCase_			( self : Optional[int]						,				_A : Union[str, Any]		)							->      Optional[Any]:







							"""simple docstring"""


							pass






						def UpperCAmelCase_			( self : Tuple						,				_A : int						,				_A : List[str]=None						,				_A : Union[str, Any]=None						,				_A : Union[str, Any]=None						,				**_A : Tuple		)							->      Union[BackboneOutput, Tuple[Tensor, ...]]:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		return_dict if return_dict is not None else self.config.use_return_dict
							snake_case_						:     Dict     =		(
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							snake_case_						:     Union[str, Any]     =		output_attentions if output_attentions is not None else self.config.output_attentions

							if output_attentions:
								raise ValueError('Cannot output attentions for timm backbones at the moment'		)

							if output_hidden_states:
								# We modify the return layers to include all the stages of the backbone
								snake_case_						:     Optional[int]     =		self._all_layers
								snake_case_						:     Any     =		self._backbone(_A						,				**_A		)
								snake_case_						:     List[str]     =		self._return_layers
								snake_case_						:     int     =		tuple(hidden_states[i] for i in self.out_indices		)
							else:
								snake_case_						:     int     =		self._backbone(_A						,				**_A		)
								snake_case_						:     Any     =		None

							snake_case_						:     Any     =		tuple(_A		)
							snake_case_						:     int     =		tuple(_A		) if hidden_states is not None else None

							if not return_dict:
								snake_case_						:     Optional[int]     =		(feature_maps,)
								if output_hidden_states:
									snake_case_						:     Union[str, Any]     =		output + (hidden_states,)
								return output

							return BackboneOutput(feature_maps=_A						,				hidden_states=_A						,				attentions=_A		)













def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				):
	snake_case_						:     Optional[int]     =		int(__a				)

	# Initialize Result
	snake_case_						:     Tuple     =		[]

	# Traverse through all denomination
	for denomination in reversed(__a				):
		# Find denominations
		while int(__a				) >= int(__a				):
			total_value -= int(__a				)
			answer.append(__a				)  # Append the "answers" array

	return answer


# Driver Code
if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE 		=						[]
						_SCREAMING_SNAKE_CASE 		=						"""0"""

						if (
						    input("""Do you want to enter your denominations ? (yY/n): """).strip().lower()
						    == "y"
						):
												_SCREAMING_SNAKE_CASE 		=						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()))
												_SCREAMING_SNAKE_CASE 		=						input("""Enter the change you want to make in Indian Currency: """).strip()
						else:
												# All denominations of Indian Currency if user does not enter
												_SCREAMING_SNAKE_CASE 		=						[1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
												_SCREAMING_SNAKE_CASE 		=						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}: ''')
												_SCREAMING_SNAKE_CASE 		=						find_minimum_change(denominations, value)
												# Print result
												for i in range(len(answer)):
																		print(answer[i], end=""" """)

