






'''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      __SCREAMING_SNAKE_CASE      (			lowerCamelCase	,				lowerCamelCase				):
 snake_case_  							=							"""pixel_values"""
 snake_case_  							=							False
 snake_case_  							=							TimmBackboneConfig




 def __init__(      self	:							Union[str, Any]					,      __lowercase	:							Optional[Any]					,      **__lowercase	:							Optional[int]      )	->						Any:
       requires_backends(self					,      '''timm'''      )
       super().__init__(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=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(__lowercase					,      '''out_features'''      ) and config.out_features is not None:
             raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.'''      )

       SCREAMING_SNAKE_CASE__     :				Any									=getattr(__lowercase					,      '''use_pretrained_backbone'''					,      __lowercase      )
       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.
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=config.out_indices if getattr(__lowercase					,      '''out_indices'''					,      __lowercase      ) is not None else (-1,)

       SCREAMING_SNAKE_CASE__     :				str									=timm.create_model(
           config.backbone					,      pretrained=__lowercase					,      features_only=config.features_only					,      in_chans=config.num_channels					,      out_indices=__lowercase					,      **__lowercase					,      )
       # 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.
       SCREAMING_SNAKE_CASE__     :				List[str]									=self._backbone.return_layers
       SCREAMING_SNAKE_CASE__     :				List[Any]									={layer['''module''']: str(__lowercase      ) for i, layer in enumerate(self._backbone.feature_info.info      )}
       super()._init_backbone(__lowercase      )




 @classmethod
 def      __magic_name__	(      cls	:							str					,      __lowercase	:							Optional[Any]					,      *__lowercase	:							str					,      **__lowercase	:							Optional[Any]      )	->						Dict:
       requires_backends(cls					,      ['''vision''', '''timm''']      )
       from ...models.timm_backbone import TimmBackboneConfig

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=kwargs.pop('''config'''					,      TimmBackboneConfig()      )

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

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




 def      __magic_name__	(      self	:							Optional[int]					,      __lowercase	:							str      )	->						str:
       pass




 def      __magic_name__	(      self	:							Tuple					,      __lowercase	:							Tuple					,      __lowercase	:							int=None					,      __lowercase	:							Optional[int]=None					,      __lowercase	:							List[str]=None					,      **__lowercase	:							Dict      )	->						Union[BackboneOutput, Tuple[Tensor, ...]]:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=return_dict if return_dict is not None else self.config.use_return_dict
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       SCREAMING_SNAKE_CASE__     :				List[Any]									=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
             SCREAMING_SNAKE_CASE__     :				Optional[int]									=self._all_layers
             SCREAMING_SNAKE_CASE__     :				str									=self._backbone(__lowercase					,      **__lowercase      )
             SCREAMING_SNAKE_CASE__     :				List[str]									=self._return_layers
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=tuple(hidden_states[i] for i in self.out_indices      )
       else:
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=self._backbone(__lowercase					,      **__lowercase      )
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=None

       SCREAMING_SNAKE_CASE__     :				Tuple									=tuple(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									=tuple(__lowercase      ) if hidden_states is not None else None

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

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







'''simple docstring'''
def  _a(  UpperCamelCase__				:						str,	UpperCamelCase__				:						str							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Optional[int]									=len(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=len(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Tuple									=[[False for _ in range(m + 1							)] for _ in range(n + 1							)]
      SCREAMING_SNAKE_CASE__     :				List[Any]									=True
      for i in range(UpperCamelCase__							):
            for j in range(m + 1							):
                  if dp[i][j]:
                        if j < m and a[i].upper() == b[j]:
                              SCREAMING_SNAKE_CASE__     :				Optional[int]									=True
                        if a[i].islower():
                              SCREAMING_SNAKE_CASE__     :				List[Any]									=True
      return dp[n][m]


if __name__ == "__main__":
  import doctest

  doctest.testmod()