
"""simple docstring"""




from sklearn.metrics import mean_squared_error

import datasets


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

A__      :	List[Any]          =    """\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


A__      :	Tuple          =    """
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        \"raw_values\" : Returns a full set of errors in case of multioutput input.

        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric(\"mse\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {'mse': 0.6123724356957945}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mse': array([0.41666667, 1. ])}
"""







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


     '''simple docstring'''





     def  lowerCAmelCase__       (      self  )->					str:
          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  )->					Union[str, Any]:
          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 ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase=None ,			__UpperCamelCase="uniform_average" ,			__UpperCamelCase=True  )->					int:
          UpperCAmelCase__							: List[str] 							=						mean_squared_error(
              __UpperCamelCase ,			__UpperCamelCase ,			sample_weight=__UpperCamelCase ,			multioutput=__UpperCamelCase ,			squared=__UpperCamelCase  )

          return {"mse": mse}





"""simple docstring"""




from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							''
     _A								=							'hf-legacy'  # "hf://"" is reserved for hffs



     def __init__(      self ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			**__UpperCamelCase ,			)->					str:
          super().__init__(self ,			**__UpperCamelCase  )
          UpperCAmelCase__							: int 							=						repo_info
          UpperCAmelCase__							: Optional[int] 							=						token
          UpperCAmelCase__							: Optional[Any] 							=						None



     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          if self.dir_cache is None:
               UpperCAmelCase__							: str 							=						{}
               for hf_file in self.repo_info.siblings:
                    # TODO(QL): add sizes
                    UpperCAmelCase__							: str 							=						{
                        "name": hf_file.rfilename,
                        "size": None,
                        "type": "file",
                    }
                    self.dir_cache.update(
                        {
                            str(__UpperCamelCase  ): {"name": str(__UpperCamelCase  ), "size": None, "type": "directory"}
                            for d in list(PurePosixPath(hf_file.rfilename  ).parents  )[:-1]
                        }  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = "rb" ,			**__UpperCamelCase ,			)->					List[Any]:
          if not isinstance(self.repo_info ,			__UpperCamelCase  ):
               raise NotImplementedError(F"Open is only implemented for dataset repositories, but got {self.repo_info}"  )
          UpperCAmelCase__							: Union[str, Any] 							=						hf_hub_url(self.repo_info.id ,			__UpperCamelCase ,			revision=self.repo_info.sha  )
          return fsspec.open(
              __UpperCamelCase ,			mode=__UpperCamelCase ,			headers=get_authentication_headers_for_url(__UpperCamelCase ,			use_auth_token=self.token  ) ,			client_kwargs={"trust_env": True} ,			).open()



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			**__UpperCamelCase  )->					List[str]:
          self._get_dirs()
          UpperCAmelCase__							: Union[str, Any] 							=						self._strip_protocol(__UpperCamelCase  )
          if path in self.dir_cache:
               return self.dir_cache[path]
          else:
               raise FileNotFoundError(__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase=False ,			**__UpperCamelCase  )->					str:
          self._get_dirs()
          UpperCAmelCase__							: str 							=						PurePosixPath(path.strip("/"  )  )
          UpperCAmelCase__							: Optional[Any] 							=						{}
          for p, f in self.dir_cache.items():
               UpperCAmelCase__							: Optional[int] 							=						PurePosixPath(p.strip("/"  )  )
               UpperCAmelCase__							: Dict 							=						p.parent
               if root == path:
                    UpperCAmelCase__							: Tuple 							=						f
          UpperCAmelCase__							: List[Any] 							=						list(paths.values()  )
          if detail:
               return out
          else:
               return sorted(f["name"] for f in out  )



