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  UpperCamelCase				( __lowercase   ,  __lowercase ):

     '''simple docstring'''

     A_										=  'pixel_values'
     A_										=  False
     A_										=  TimmBackboneConfig



     def __init__(					self				,				A_				,				**A_						) ->				Any:






           """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 		UpperCamelCase_						(					cls				,				A_				,				*A_				,				**A_						) ->				Dict:






           """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 		UpperCamelCase_						(					self				,				A_						) ->				Any:






           """simple docstring"""
           pass





     def 		UpperCamelCase_						(					self				,				A_				,				A_=None				,				A_=None				,				A_=None				,				**A_						) ->				Union[BackboneOutput, Tuple[Tensor, ...]]:






           """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_						)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    str:



      '''simple docstring'''

      _lowerCamelCase			    =				0
      while b > 0:
            if b & 1:
                  res += a

            a += a
            b >>= 1

      return res
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    Optional[Any]:



      '''simple docstring'''

      _lowerCamelCase			    =				0
      while b > 0:
            if b & 1:
                  _lowerCamelCase			    =				((res % c) + (a % c)) % c

            a += a
            b >>= 1

      return res