



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__(				lowerCamelCase  ,				lowerCamelCase						):

   '''simple docstring'''




   A     :	Dict												=     "pixel_values"
   A     :	Optional[Any]												=     False
   A     :	Any												=     TimmBackboneConfig


   def __init__(    self	:      List[Any]      ,					lowerCAmelCase	:      Tuple      ,					**lowerCAmelCase	:      str)						->					Tuple:




         """simple docstring"""

         requires_backends(self      ,					'timm')
         super().__init__(lowerCAmelCase)
         lowercase__  =	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.')

         lowercase__  =	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.
         lowercase__  =	config.out_indices if getattr(lowerCAmelCase      ,					'out_indices'      ,					lowerCAmelCase) is not None else (-1,)

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


   @classmethod
   def       UpperCAmelCase		(    cls	:      List[str]      ,					lowerCAmelCase	:      Union[str, Any]      ,					*lowerCAmelCase	:      int      ,					**lowerCAmelCase	:      Optional[int])						->					Any:




         """simple docstring"""

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

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

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

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


   def       UpperCAmelCase		(    self	:      List[str]      ,					lowerCAmelCase	:      List[Any])						->					List[Any]:




         """simple docstring"""

         pass



   def       UpperCAmelCase		(    self	:      Optional[Any]      ,					lowerCAmelCase	:      Any      ,					lowerCAmelCase	:      Any=None      ,					lowerCAmelCase	:      Union[str, Any]=None      ,					lowerCAmelCase	:      Optional[int]=None      ,					**lowerCAmelCase	:      Union[str, Any])						->					Union[BackboneOutput, Tuple[Tensor, ...]]:




         """simple docstring"""

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

         lowercase__  =	tuple(lowerCAmelCase)
         lowercase__  =	tuple(lowerCAmelCase) if hidden_states is not None else None

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

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





import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


a__   :					Optional[Any] 						=      logging.get_logger(__name__)



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   def __init__(    self	:      Optional[Any]      ,					*lowerCAmelCase	:      Optional[Any]      ,					**lowerCAmelCase	:      List[Any])						->					None:




         """simple docstring"""

         warnings.warn(
             'The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
             ' use GLPNImageProcessor instead.'      ,					lowerCAmelCase      ,					)
         super().__init__(*lowerCAmelCase      ,					**lowerCAmelCase)
