
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			,							UpperCamelCase__			: List[str]			,							**UpperCamelCase__			: Any    )			->	str:







    """simple docstring"""






    requires_backends(self			,							"""timm"""    )
    super().__init__(UpperCamelCase__    )
    __magic_name__				     =       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."""    )

    __magic_name__				     =       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.
    __magic_name__				     =       config.out_indices if getattr(UpperCamelCase__			,							"""out_indices"""			,							UpperCamelCase__    ) is not None else (-1,)

    __magic_name__				     =       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.
    __magic_name__				     =       self._backbone.return_layers
    __magic_name__				     =       {layer["""module"""]: str(UpperCamelCase__    ) for i, layer in enumerate(self._backbone.feature_info.info    )}
    super()._init_backbone(UpperCamelCase__    )
  @classmethod
  def 							_lowercase							( cls			: Optional[Any]			,							UpperCamelCase__			: Dict			,							*UpperCamelCase__			: Optional[Any]			,							**UpperCamelCase__			: List[Any]    )			->	Optional[Any]:







    """simple docstring"""






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

    __magic_name__				     =       kwargs.pop("""config"""			,							TimmBackboneConfig()    )

    __magic_name__				     =       kwargs.pop("""use_timm_backbone"""			,							UpperCamelCase__    )
    if not use_timm:
      raise ValueError("""use_timm_backbone must be True for timm backbones"""    )

    __magic_name__				     =       kwargs.pop("""num_channels"""			,							config.num_channels    )
    __magic_name__				     =       kwargs.pop("""features_only"""			,							config.features_only    )
    __magic_name__				     =       kwargs.pop("""use_pretrained_backbone"""			,							config.use_pretrained_backbone    )
    __magic_name__				     =       kwargs.pop("""out_indices"""			,							config.out_indices    )
    __magic_name__				     =       TimmBackboneConfig(
        backbone=UpperCamelCase__			,							num_channels=UpperCamelCase__			,							features_only=UpperCamelCase__			,							use_pretrained_backbone=UpperCamelCase__			,							out_indices=UpperCamelCase__			,							)
    return super()._from_config(UpperCamelCase__			,							**UpperCamelCase__    )
  def 							_lowercase							( self			: Tuple			,							UpperCamelCase__			: Optional[Any]    )			->	int:







    """simple docstring"""






    pass





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







    """simple docstring"""






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

    __magic_name__				     =       tuple(UpperCamelCase__    )
    __magic_name__				     =       tuple(UpperCamelCase__    ) if hidden_states is not None else None

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

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



import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu




@require_cpu
class        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  def 							_lowercase							( self			: List[Any]    )			->	Union[str, Any]:







    """simple docstring"""






    __magic_name__				     =       torch.nn.Linear(10			,							10    )
    __magic_name__				     =       torch.optim.SGD(model.parameters()			,							0.1    )
    __magic_name__				     =       Accelerator()
    __magic_name__				     =       accelerator.prepare(UpperCamelCase__    )
    try:
      pickle.loads(pickle.dumps(UpperCamelCase__    )    )
    except Exception as e:
      self.fail(F'''Accelerated optimizer pickling failed with {e}'''    )
    AcceleratorState._reset_state()

