





'''simple docstring'''




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__SCREAMING_SNAKE_CASE     :List[Any]       =				logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     :Dict       =				{
    '''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : str           =							"""deta"""
 _lowerCamelCase  : List[str]           =							{
     """hidden_size""": """d_model""",
     """num_attention_heads""": """encoder_attention_heads""",
 }




 def __init__(					self       :    Optional[Any]	,			snake_case_       :    Dict=None	,			snake_case_       :    int=9_0_0	,			snake_case_       :    Tuple=2_0_4_8	,			snake_case_       :    Union[str, Any]=6	,			snake_case_       :    Tuple=2_0_4_8	,			snake_case_       :    int=8	,			snake_case_       :    str=6	,			snake_case_       :    int=1_0_2_4	,			snake_case_       :    str=8	,			snake_case_       :    Tuple=0.0	,			snake_case_       :    int=True	,			snake_case_       :    str="relu"	,			snake_case_       :    Optional[int]=2_5_6	,			snake_case_       :    int=0.1	,			snake_case_       :    int=0.0	,			snake_case_       :    Dict=0.0	,			snake_case_       :    List[Any]=0.0_2	,			snake_case_       :    Optional[int]=1.0	,			snake_case_       :    Tuple=True	,			snake_case_       :    Dict=False	,			snake_case_       :    Any="sine"	,			snake_case_       :    int=5	,			snake_case_       :    Union[str, Any]=4	,			snake_case_       :    List[Any]=4	,			snake_case_       :    Optional[int]=True	,			snake_case_       :    Optional[Any]=3_0_0	,			snake_case_       :    str=True	,			snake_case_       :    Optional[Any]=True	,			snake_case_       :    Dict=1	,			snake_case_       :    Optional[int]=5	,			snake_case_       :    str=2	,			snake_case_       :    int=1	,			snake_case_       :    Union[str, Any]=1	,			snake_case_       :    Dict=5	,			snake_case_       :    Union[str, Any]=2	,			snake_case_       :    Optional[Any]=0.1	,			snake_case_       :    Optional[int]=0.2_5	,			**snake_case_       :    Optional[Any]	,			):
      if backbone_config is None:
           logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."    )
           _UpperCAmelCase     							=   CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]    )
      else:
           if isinstance(snake_case_	,			snake_case_    ):
                _UpperCAmelCase     							=   backbone_config.pop("model_type"    )
                _UpperCAmelCase     							=   CONFIG_MAPPING[backbone_model_type]
                _UpperCAmelCase     							=   config_class.from_dict(snake_case_    )

      _UpperCAmelCase     							=   backbone_config
      _UpperCAmelCase     							=   num_queries
      _UpperCAmelCase     							=   max_position_embeddings
      _UpperCAmelCase     							=   d_model
      _UpperCAmelCase     							=   encoder_ffn_dim
      _UpperCAmelCase     							=   encoder_layers
      _UpperCAmelCase     							=   encoder_attention_heads
      _UpperCAmelCase     							=   decoder_ffn_dim
      _UpperCAmelCase     							=   decoder_layers
      _UpperCAmelCase     							=   decoder_attention_heads
      _UpperCAmelCase     							=   dropout
      _UpperCAmelCase     							=   attention_dropout
      _UpperCAmelCase     							=   activation_dropout
      _UpperCAmelCase     							=   activation_function
      _UpperCAmelCase     							=   init_std
      _UpperCAmelCase     							=   init_xavier_std
      _UpperCAmelCase     							=   encoder_layerdrop
      _UpperCAmelCase     							=   auxiliary_loss
      _UpperCAmelCase     							=   position_embedding_type
      # deformable attributes
      _UpperCAmelCase     							=   num_feature_levels
      _UpperCAmelCase     							=   encoder_n_points
      _UpperCAmelCase     							=   decoder_n_points
      _UpperCAmelCase     							=   two_stage
      _UpperCAmelCase     							=   two_stage_num_proposals
      _UpperCAmelCase     							=   with_box_refine
      _UpperCAmelCase     							=   assign_first_stage
      if two_stage is True and with_box_refine is False:
           raise ValueError("If two_stage is True, with_box_refine must be True."    )
      # Hungarian matcher
      _UpperCAmelCase     							=   class_cost
      _UpperCAmelCase     							=   bbox_cost
      _UpperCAmelCase     							=   giou_cost
      # Loss coefficients
      _UpperCAmelCase     							=   mask_loss_coefficient
      _UpperCAmelCase     							=   dice_loss_coefficient
      _UpperCAmelCase     							=   bbox_loss_coefficient
      _UpperCAmelCase     							=   giou_loss_coefficient
      _UpperCAmelCase     							=   eos_coefficient
      _UpperCAmelCase     							=   focal_alpha
      super().__init__(is_encoder_decoder=snake_case_	,			**snake_case_    )




 @property
 def      lowercase	(					self       :    List[Any]    ):
      return self.encoder_attention_heads




 @property
 def      lowercase	(					self       :    Dict    ):
      return self.d_model




 def      lowercase	(					self       :    List[Any]    ):
      _UpperCAmelCase     							=   copy.deepcopy(self.__dict__    )
      _UpperCAmelCase     							=   self.backbone_config.to_dict()
      _UpperCAmelCase     							=   self.__class__.model_type
      return output








from sklearn.metrics import mean_squared_error

import datasets


lowerCamelCase											=       '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'

lowerCamelCase											=       '\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'


lowerCamelCase											=       '\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        "raw_values" : Returns a full set of errors in case of multioutput input.\n\n        "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric("mse")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {\'mse\': 0.6123724356957945}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric("mse", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mse\': array([0.41666667, 1. ])}\n'




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,							_KWARGS_DESCRIPTION     )
class A  (   datasets.Metric     ):






      def        lowerCamelCase       (					self						:     List[Any] )					->						List[str]:
             """simple docstring"""




             return datasets.MetricInfo(
                 description=_DESCRIPTION			,   citation=_CITATION			,   inputs_description=_KWARGS_DESCRIPTION			,   features=datasets.Features(self._get_feature_types() )			,   reference_urls=[
                     'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'
                 ]			,   )






      def        lowerCamelCase       (					self						:     Tuple )					->						Optional[int]:
             """simple docstring"""




             if self.config_name == "multilist":
                    return {
                        "predictions": datasets.Sequence(datasets.Value('float' ) ),
                        "references": datasets.Sequence(datasets.Value('float' ) ),
                    }
             else:
                    return {
                        "predictions": datasets.Value('float' ),
                        "references": datasets.Value('float' ),
                    }






      def        lowerCamelCase       (					self						:     List[Any]			,   lowercase_						:     List[Any]			,   lowercase_						:     Tuple			,   lowercase_						:     List[str]=None			,   lowercase_						:     Union[str, Any]="uniform_average"			,   lowercase_						:     Tuple=True )					->						Any:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=mean_squared_error(
                 lowercase_			,   lowercase_			,   sample_weight=lowercase_			,   multioutput=lowercase_			,   squared=lowercase_ )

             return {"mse": mse}


