
'''simple docstring'''
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 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						"""pixel_values"""
		lowerCamelCase     							=						False
		lowerCamelCase     							=						TimmBackboneConfig



		def __init__(      self				:			Dict  ,	UpperCamelCase__				:			Optional[Any]  ,	**UpperCamelCase__				:			Dict      )						->			List[Any]:






				"""simple docstring"""







				requires_backends(self  ,	'''timm'''      )
				super().__init__(UpperCamelCase__      )
				snake_case  :       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.'''      )

				snake_case  :       Tuple     			=						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.
				snake_case  :       List[Any]     			=						config.out_indices if getattr(UpperCamelCase__  ,	'''out_indices'''  ,	UpperCamelCase__      ) is not None else (-1,)

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



		@classmethod
		def       lowerCAmelCase				(      cls				:			Tuple  ,	UpperCamelCase__				:			Dict  ,	*UpperCamelCase__				:			Optional[Any]  ,	**UpperCamelCase__				:			Optional[Any]      )						->			List[str]:






				"""simple docstring"""







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

				snake_case  :       Any     			=						kwargs.pop('''config'''  ,	TimmBackboneConfig()      )

				snake_case  :       Union[str, Any]     			=						kwargs.pop('''use_timm_backbone'''  ,	UpperCamelCase__      )
				if not use_timm:
						raise ValueError('''use_timm_backbone must be True for timm backbones'''      )

				snake_case  :       int     			=						kwargs.pop('''num_channels'''  ,	config.num_channels      )
				snake_case  :       Tuple     			=						kwargs.pop('''features_only'''  ,	config.features_only      )
				snake_case  :       Any     			=						kwargs.pop('''use_pretrained_backbone'''  ,	config.use_pretrained_backbone      )
				snake_case  :       List[str]     			=						kwargs.pop('''out_indices'''  ,	config.out_indices      )
				snake_case  :       List[str]     			=						TimmBackboneConfig(
				    backbone=UpperCamelCase__  ,	num_channels=UpperCamelCase__  ,	features_only=UpperCamelCase__  ,	use_pretrained_backbone=UpperCamelCase__  ,	out_indices=UpperCamelCase__  ,	)
				return super()._from_config(UpperCamelCase__  ,	**UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			str  ,	UpperCamelCase__				:			str      )						->			Optional[Any]:






				"""simple docstring"""







				pass



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






				"""simple docstring"""







				snake_case  :       Dict     			=						return_dict if return_dict is not None else self.config.use_return_dict
				snake_case  :       str     			=						(
				    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
				)
				snake_case  :       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
						snake_case  :       List[Any]     			=						self._all_layers
						snake_case  :       Optional[Any]     			=						self._backbone(UpperCamelCase__  ,	**UpperCamelCase__      )
						snake_case  :       Union[str, Any]     			=						self._return_layers
						snake_case  :       Tuple     			=						tuple(hidden_states[i] for i in self.out_indices      )
				else:
						snake_case  :       Dict     			=						self._backbone(UpperCamelCase__  ,	**UpperCamelCase__      )
						snake_case  :       Union[str, Any]     			=						None

				snake_case  :       List[Any]     			=						tuple(UpperCamelCase__      )
				snake_case  :       Any     			=						tuple(UpperCamelCase__      ) if hidden_states is not None else None

				if not return_dict:
						snake_case  :       str     			=						(feature_maps,)
						if output_hidden_states:
								snake_case  :       Any     			=						output + (hidden_states,)
						return output

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




'''simple docstring'''
import warnings

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


lowercase__						       =      logging.get_logger(__name__)






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		def __init__(      self				:			Optional[Any]  ,	*UpperCamelCase__				:			Optional[Any]  ,	**UpperCamelCase__				:			str      )						->			None:






				"""simple docstring"""







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


