

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     A_      (					lowerCAmelCase__       ,   lowerCAmelCase__  ):


    '''simple docstring'''






    a__   							=				"""pixel_values"""
    a__   							=				False
    a__   							=				TimmBackboneConfig


    def __init__(self   ,					lowercase__   ,					**lowercase__     )   ->					List[Any]:
        requires_backends(self   ,					'''timm'''     )
        super().__init__(UpperCamelCase__     )
        __UpperCAmelCase     =	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.'''     )

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

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


    @classmethod
    def    lowerCAmelCase_  (cls   ,					lowercase__   ,					*lowercase__   ,					**lowercase__     )   ->					List[Any]:
        requires_backends(cls   ,					['''vision''', '''timm''']     )
        from ...models.timm_backbone import TimmBackboneConfig

        __UpperCAmelCase     =	kwargs.pop('''config'''   ,					TimmBackboneConfig()     )

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

        __UpperCAmelCase     =	kwargs.pop('''num_channels'''   ,					config.num_channels     )
        __UpperCAmelCase     =	kwargs.pop('''features_only'''   ,					config.features_only     )
        __UpperCAmelCase     =	kwargs.pop('''use_pretrained_backbone'''   ,					config.use_pretrained_backbone     )
        __UpperCAmelCase     =	kwargs.pop('''out_indices'''   ,					config.out_indices     )
        __UpperCAmelCase     =	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   ,					lowercase__     )   ->					Union[str, Any]:
        pass






    def    lowerCAmelCase_  (self   ,					lowercase__   ,					lowercase__=None   ,					lowercase__=None   ,					lowercase__=None   ,					**lowercase__     )   ->					Union[BackboneOutput, Tuple[Tensor, ...]]:
        __UpperCAmelCase     =	return_dict if return_dict is not None else self.config.use_return_dict
        __UpperCAmelCase     =	(
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        __UpperCAmelCase     =	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
            __UpperCAmelCase     =	self._all_layers
            __UpperCAmelCase     =	self._backbone(UpperCamelCase__   ,					**UpperCamelCase__     )
            __UpperCAmelCase     =	self._return_layers
            __UpperCAmelCase     =	tuple(hidden_states[i] for i in self.out_indices     )
        else:
            __UpperCAmelCase     =	self._backbone(UpperCamelCase__   ,					**UpperCamelCase__     )
            __UpperCAmelCase     =	None

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

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

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










import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor

def      A      (      _SCREAMING_SNAKE_CASE    )	-> tuple:
       return (data["data"], data["target"])

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> np.ndarray:
       lowerCamelCase   :      List[str]			 =					XGBRegressor(verbosity=0 ,random_state=42    )
       xgb.fit(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       # Predict target for test data
       lowerCamelCase   :      List[Any]			 =					xgb.predict(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Tuple			 =					predictions.reshape(len(_SCREAMING_SNAKE_CASE    ) ,1    )
       return predictions

def      A      (      )	-> None:
       lowerCamelCase   :      Dict			 =					fetch_california_housing()
       lowerCamelCase ,      lowerCamelCase   :      Tuple			 =					data_handling(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Optional[Any]			 =					train_test_split(
           _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,test_size=0.25 ,random_state=1    )
       lowerCamelCase   :      Any			 =					xgboost(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       # Error printing
       print(f'''Mean Absolute Error : {mean_absolute_error(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )}'''    )
       print(f'''Mean Square Error  : {mean_squared_error(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )}'''    )


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)
       main()






