


"""simple docstring"""

from sklearn.metrics import mean_squared_error

import datasets


UpperCAmelCase__  							="\\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"

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


UpperCAmelCase__  							="\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    lowerCamelCase__						(		datasets.Metric    ):
      def SCREAMING_SNAKE_CASE_	(		self	:	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 SCREAMING_SNAKE_CASE_	(		self	:	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 SCREAMING_SNAKE_CASE_	(		self	:	List[Any]      ,       A_	:	Optional[int]      ,       A_	:	Dict      ,       A_	:	Union[str, Any]=None      ,       A_	:	Optional[Any]="uniform_average"      ,       A_	:	Optional[int]=True							):






       '''simple docstring'''


       __lowercase									=	mean_squared_error(
           A_      ,       A_      ,       sample_weight=A_      ,       multioutput=A_      ,       squared=A_							)

       return {"mse": mse}




"""simple docstring"""

from typing import TYPE_CHECKING

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


UpperCAmelCase__  							={"configuration_van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"]}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase__  							=[
           "VAN_PRETRAINED_MODEL_ARCHIVE_LIST",
           "VanForImageClassification",
           "VanModel",
           "VanPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_van import (
                  VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VanForImageClassification,
                  VanModel,
                  VanPreTrainedModel,
              )

else:
       import sys

       UpperCAmelCase__  							=_LazyModule(__name__, globals()["__file__"], _import_structure)
