

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__		(	A__						,					A__	):






  """simple docstring"""


  a									= "pixel_values"
  a									= False
  a									= TimmBackboneConfig






  def __init__(							self						:		str     ,       __lowerCamelCase						:		List[Any]     ,       **__lowerCamelCase						:		Tuple )		->							Union[str, Any]:
       requires_backends(self     ,       '''timm''' )
       super().__init__(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   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(__lowerCamelCase     ,       '''out_features''' ) and config.out_features is not None:
            raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.''' )

       SCREAMING_SNAKE_CASE__   =   getattr(__lowerCamelCase     ,       '''use_pretrained_backbone'''     ,       __lowerCamelCase )
       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.
       SCREAMING_SNAKE_CASE__   =   config.out_indices if getattr(__lowerCamelCase     ,       '''out_indices'''     ,       __lowerCamelCase ) is not None else (-1,)

       SCREAMING_SNAKE_CASE__   =   timm.create_model(
           config.backbone     ,       pretrained=__lowerCamelCase     ,       features_only=config.features_only     ,       in_chans=config.num_channels     ,       out_indices=__lowerCamelCase     ,       **__lowerCamelCase     ,       )
       # 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.
       SCREAMING_SNAKE_CASE__   =   self._backbone.return_layers
       SCREAMING_SNAKE_CASE__   =   {layer['''module''']: str(__lowerCamelCase ) for i, layer in enumerate(self._backbone.feature_info.info )}
       super()._init_backbone(__lowerCamelCase )






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

       SCREAMING_SNAKE_CASE__   =   kwargs.pop('''config'''     ,       TimmBackboneConfig() )

       SCREAMING_SNAKE_CASE__   =   kwargs.pop('''use_timm_backbone'''     ,       __lowerCamelCase )
       if not use_timm:
            raise ValueError('''use_timm_backbone must be True for timm backbones''' )

       SCREAMING_SNAKE_CASE__   =   kwargs.pop('''num_channels'''     ,       config.num_channels )
       SCREAMING_SNAKE_CASE__   =   kwargs.pop('''features_only'''     ,       config.features_only )
       SCREAMING_SNAKE_CASE__   =   kwargs.pop('''use_pretrained_backbone'''     ,       config.use_pretrained_backbone )
       SCREAMING_SNAKE_CASE__   =   kwargs.pop('''out_indices'''     ,       config.out_indices )
       SCREAMING_SNAKE_CASE__   =   TimmBackboneConfig(
           backbone=__lowerCamelCase     ,       num_channels=__lowerCamelCase     ,       features_only=__lowerCamelCase     ,       use_pretrained_backbone=__lowerCamelCase     ,       out_indices=__lowerCamelCase     ,       )
       return super()._from_config(__lowerCamelCase     ,       **__lowerCamelCase )






  def 				lowercase_		(							self						:		str     ,       __lowerCamelCase						:		Dict )		->							List[Any]:
       pass







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

       SCREAMING_SNAKE_CASE__   =   tuple(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   tuple(__lowerCamelCase ) if hidden_states is not None else None

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

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



import warnings

from .generation import TFGenerationMixin






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  warnings.warn(
      "Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
      "be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead."						,					A__						,					)


