


"""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   _UpperCamelCase  (   lowerCAmelCase__       ,lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    List[Any]				      ="""pixel_values"""
							__UpperCAmelCase						:    Union[str, Any]				      =False
							__UpperCAmelCase						:    Union[str, Any]				      =TimmBackboneConfig

							def __init__(     self    ,   __a    ,   **__a	):
										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 			snake_case				(     cls    ,   __a    ,   *__a    ,   **__a	):
										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 			snake_case				(     self    ,   __a	):
										pass



							def 			snake_case				(     self    ,   __a    ,   __a=None    ,   __a=None    ,   __a=None    ,   **__a	):
										__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	)







"""simple docstring"""





from ..utils import DummyObject, requires_backends







class 				SCREAMING_SNAKE_CASE					(		metaclass=a_       ):






					"""simple docstring"""



					lowercase__				 =       ["speech"]







					def __init__(       self			:				Tuple   ,*lowercase_			:				Tuple   ,**lowercase_			:				List[str]		):
						requires_backends(self   ,['''speech''']		)







class 				SCREAMING_SNAKE_CASE					(		metaclass=a_       ):






					"""simple docstring"""



					lowercase__				 =       ["speech"]







					def __init__(       self			:				Union[str, Any]   ,*lowercase_			:				List[str]   ,**lowercase_			:				Any		):
						requires_backends(self   ,['''speech''']		)




