



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__  (						UpperCAmelCase_       ,    UpperCAmelCase_):
     """simple docstring"""



     _A       =			'pixel_values'
     _A       =			False
     _A       =			TimmBackboneConfig







     def __init__(self				,			__a				,			**__a    ):






           '''simple docstring'''



           requires_backends(self				,			"timm"    )
           super().__init__(__a    )
           lowerCamelCase								=		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."    )

           lowerCamelCase								=		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.
           lowerCamelCase								=		config.out_indices if getattr(__a				,			"out_indices"				,			__a    ) is not None else (-1,)

           lowerCamelCase								=		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.
           lowerCamelCase								=		self._backbone.return_layers
           lowerCamelCase								=		{layer["module"]: str(__a    ) for i, layer in enumerate(self._backbone.feature_info.info    )}
           super()._init_backbone(__a    )







     @classmethod
     def 						_a      (cls				,			__a				,			*__a				,			**__a    ):






           '''simple docstring'''



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

           lowerCamelCase								=		kwargs.pop("config"				,			TimmBackboneConfig()    )

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

           lowerCamelCase								=		kwargs.pop("num_channels"				,			config.num_channels    )
           lowerCamelCase								=		kwargs.pop("features_only"				,			config.features_only    )
           lowerCamelCase								=		kwargs.pop("use_pretrained_backbone"				,			config.use_pretrained_backbone    )
           lowerCamelCase								=		kwargs.pop("out_indices"				,			config.out_indices    )
           lowerCamelCase								=		TimmBackboneConfig(
               backbone=__a				,			num_channels=__a				,			features_only=__a				,			use_pretrained_backbone=__a				,			out_indices=__a				,			)
           return super()._from_config(__a				,			**__a    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           pass







     def 						_a      (self				,			__a				,			__a=None				,			__a=None				,			__a=None				,			**__a    ):






           '''simple docstring'''



           lowerCamelCase								=		return_dict if return_dict is not None else self.config.use_return_dict
           lowerCamelCase								=		(
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           lowerCamelCase								=		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
                 lowerCamelCase								=		self._all_layers
                 lowerCamelCase								=		self._backbone(__a				,			**__a    )
                 lowerCamelCase								=		self._return_layers
                 lowerCamelCase								=		tuple(hidden_states[i] for i in self.out_indices    )
           else:
                 lowerCamelCase								=		self._backbone(__a				,			**__a    )
                 lowerCamelCase								=		None

           lowerCamelCase								=		tuple(__a    )
           lowerCamelCase								=		tuple(__a    ) if hidden_states is not None else None

           if not return_dict:
                 lowerCamelCase								=		(feature_maps,)
                 if output_hidden_states:
                       lowerCamelCase								=		output + (hidden_states,)
                 return output

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




from math import pi

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
     print(arc_length(9_0, 1_0))