



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 	lowerCamelCase_    ( _A						,_A						):






						'''simple docstring'''
						a__   =  "pixel_values"
						a__   =  False
						a__   =  TimmBackboneConfig







						def __init__(	self      :			Optional[Any]	,	__lowerCamelCase      :			Dict	,	**__lowerCamelCase      :			Any )      ->				List[Any]:
									requires_backends(self	,	"timm" )
									super().__init__(UpperCAmelCase__ )
									A							:					Optional[int]              =						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(UpperCAmelCase__	,	"out_features" ) and config.out_features is not None:
												raise ValueError("out_features is not supported by TimmBackbone. Please use out_indices instead." )

									A							:					List[Any]              =						getattr(UpperCAmelCase__	,	"use_pretrained_backbone"	,	UpperCAmelCase__ )
									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.
									A							:					Dict              =						config.out_indices if getattr(UpperCAmelCase__	,	"out_indices"	,	UpperCAmelCase__ ) is not None else (-1,)

									A							:					Optional[int]              =						timm.create_model(
									    config.backbone	,	pretrained=UpperCAmelCase__	,	features_only=config.features_only	,	in_chans=config.num_channels	,	out_indices=UpperCAmelCase__	,	**UpperCAmelCase__	,	)
									# 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.
									A							:					List[Any]              =						self._backbone.return_layers
									A							:					Optional[int]              =						{layer["module"]: str(UpperCAmelCase__ ) for i, layer in enumerate(self._backbone.feature_info.info )}
									super()._init_backbone(UpperCAmelCase__ )







						@classmethod
						def 			SCREAMING_SNAKE_CASE__		(	cls      :			List[str]	,	__lowerCamelCase      :			Optional[int]	,	*__lowerCamelCase      :			Union[str, Any]	,	**__lowerCamelCase      :			List[Any] )      ->				Union[str, Any]:
									requires_backends(cls	,	["vision", "timm"] )
									from ...models.timm_backbone import TimmBackboneConfig

									A							:					Dict              =						kwargs.pop("config"	,	TimmBackboneConfig() )

									A							:					Any              =						kwargs.pop("use_timm_backbone"	,	UpperCAmelCase__ )
									if not use_timm:
												raise ValueError("use_timm_backbone must be True for timm backbones" )

									A							:					List[str]              =						kwargs.pop("num_channels"	,	config.num_channels )
									A							:					str              =						kwargs.pop("features_only"	,	config.features_only )
									A							:					Optional[int]              =						kwargs.pop("use_pretrained_backbone"	,	config.use_pretrained_backbone )
									A							:					Optional[Any]              =						kwargs.pop("out_indices"	,	config.out_indices )
									A							:					List[Any]              =						TimmBackboneConfig(
									    backbone=UpperCAmelCase__	,	num_channels=UpperCAmelCase__	,	features_only=UpperCAmelCase__	,	use_pretrained_backbone=UpperCAmelCase__	,	out_indices=UpperCAmelCase__	,	)
									return super()._from_config(UpperCAmelCase__	,	**UpperCAmelCase__ )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str	,	__lowerCamelCase      :			int )      ->				List[str]:
									pass







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any]	,	__lowerCamelCase      :			str	,	__lowerCamelCase      :			int=None	,	__lowerCamelCase      :			Union[str, Any]=None	,	__lowerCamelCase      :			Any=None	,	**__lowerCamelCase      :			List[str] )      ->				Union[BackboneOutput, Tuple[Tensor, ...]]:
									A							:					int              =						return_dict if return_dict is not None else self.config.use_return_dict
									A							:					Optional[int]              =						(
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									A							:					Optional[int]              =						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
												A							:					Any              =						self._all_layers
												A							:					str              =						self._backbone(UpperCAmelCase__	,	**UpperCAmelCase__ )
												A							:					Union[str, Any]              =						self._return_layers
												A							:					Dict              =						tuple(hidden_states[i] for i in self.out_indices )
									else:
												A							:					Dict              =						self._backbone(UpperCAmelCase__	,	**UpperCAmelCase__ )
												A							:					Dict              =						None

									A							:					List[str]              =						tuple(UpperCAmelCase__ )
									A							:					Optional[Any]              =						tuple(UpperCAmelCase__ ) if hidden_states is not None else None

									if not return_dict:
												A							:					Dict              =						(feature_maps,)
												if output_hidden_states:
															A							:					Any              =						output + (hidden_states,)
												return output

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




from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
__SCREAMING_SNAKE_CASE           =  1.0_5_4_5_7_1_8_1_7e-3_4  # unit of ℏ : J * s

__SCREAMING_SNAKE_CASE           =  3e8  # unit of c : m * s^-1




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase    ):

			if (force, area, distance).count(0    ) != 1:
						raise ValueError("One and only one argument must be 0"    )
			if force < 0:
						raise ValueError("Magnitude of force can not be negative"    )
			if distance < 0:
						raise ValueError("Distance can not be negative"    )
			if area < 0:
						raise ValueError("Area can not be negative"    )
			if force == 0:
						A							:					int              =						(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
						    240 * (distance) ** 4
						)
						return {"force": force}
			elif area == 0:
						A							:					Tuple              =						(240 * force * (distance) ** 4) / (
						    REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
						)
						return {"area": area}
			elif distance == 0:
						A							:					Dict              =						(
						    (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
						) ** (1 / 4)
						return {"distance": distance}
			raise ValueError("One and only one argument must be 0"    )


# Run doctest
if __name__ == "__main__":
							import doctest

							doctest.testmod()