


"""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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A     :   Optional[int]			    =			{
    "configuration_altclip": [
        "ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "AltCLIPConfig",
        "AltCLIPTextConfig",
        "AltCLIPVisionConfig",
    ],
    "processing_altclip": ["AltCLIPProcessor"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   Tuple			    =			[
	    "ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "AltCLIPPreTrainedModel",
	    "AltCLIPModel",
	    "AltCLIPTextModel",
	    "AltCLIPVisionModel",
	]


if TYPE_CHECKING:
	from .configuration_altclip import (
	    ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    AltCLIPConfig,
	    AltCLIPTextConfig,
	    AltCLIPVisionConfig,
	)
	from .processing_altclip import AltCLIPProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_altclip import (
		    ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AltCLIPModel,
		    AltCLIPPreTrainedModel,
		    AltCLIPTextModel,
		    AltCLIPVisionModel,
		)


else:
	import sys

	A     :   int			    =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




