


from typing import List

import numpy as np




def 	a_      (    SCREAMING_SNAKE_CASE__					:      dict     ):
       '''simple docstring'''





       _lowerCamelCase       :		Dict		={key: len(SCREAMING_SNAKE_CASE__     ) for key, value in gen_kwargs.items() if isinstance(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )}
       if len(set(lists_lengths.values()     )     ) > 1:
              raise RuntimeError(
                  (
                      'Sharding is ambiguous for this dataset: '
                      + 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
                      + '\n'.join(F'''\t- key {key} has length {length}''' for key, length in lists_lengths.items()     )
                      + '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
                      + 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
                  )     )
       _lowerCamelCase       :		Tuple		=max(lists_lengths.values()		,			default=0     )
       return max(1		,			SCREAMING_SNAKE_CASE__     )




def 	a_      (    SCREAMING_SNAKE_CASE__					:      int		,			SCREAMING_SNAKE_CASE__					:      int     ):
       '''simple docstring'''





       _lowerCamelCase       :		Tuple		=[]
       for group_idx in range(SCREAMING_SNAKE_CASE__     ):
              _lowerCamelCase       :		List[str]		=num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
              if num_shards_to_add == 0:
                     break
              _lowerCamelCase       :		List[Any]		=shards_indices_per_group[-1].stop if shards_indices_per_group else 0
              _lowerCamelCase       :		Any		=range(SCREAMING_SNAKE_CASE__		,			start + num_shards_to_add     )
              shards_indices_per_group.append(SCREAMING_SNAKE_CASE__     )
       return shards_indices_per_group




def 	a_      (    SCREAMING_SNAKE_CASE__					:      dict		,			SCREAMING_SNAKE_CASE__					:      int     ):
       '''simple docstring'''





       _lowerCamelCase       :		List[str]		=_number_of_shards_in_gen_kwargs(SCREAMING_SNAKE_CASE__     )
       if num_shards == 1:
              return [dict(SCREAMING_SNAKE_CASE__     )]
       else:
              _lowerCamelCase       :		str		=_distribute_shards(num_shards=SCREAMING_SNAKE_CASE__		,			max_num_jobs=SCREAMING_SNAKE_CASE__     )
              return [
                  {
                      key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                      if isinstance(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )
                      else value
                      for key, value in gen_kwargs.items()
                  }
                  for group_idx in range(len(SCREAMING_SNAKE_CASE__     )     )
              ]




def 	a_      (    SCREAMING_SNAKE_CASE__					:      List[dict]     ):
       '''simple docstring'''





       return {
           key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
           if isinstance(gen_kwargs_list[0][key]		,			SCREAMING_SNAKE_CASE__     )
           else gen_kwargs_list[0][key]
           for key in gen_kwargs_list[0]
       }




def 	a_      (    SCREAMING_SNAKE_CASE__					:      np.random.Generator		,			SCREAMING_SNAKE_CASE__					:      dict     ):
       '''simple docstring'''





       _lowerCamelCase       :		Optional[Any]		={len(SCREAMING_SNAKE_CASE__     ) for value in gen_kwargs.values() if isinstance(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     )}
       _lowerCamelCase       :		Tuple		={}
       for size in list_sizes:
              _lowerCamelCase       :		Optional[int]		=list(range(SCREAMING_SNAKE_CASE__     )     )
              rng.shuffle(indices_per_size[size]     )
       # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
       _lowerCamelCase       :		int		=dict(SCREAMING_SNAKE_CASE__     )
       for key, value in shuffled_kwargs.items():
              if isinstance(SCREAMING_SNAKE_CASE__		,			SCREAMING_SNAKE_CASE__     ):
                     _lowerCamelCase       :		Union[str, Any]		=[value[i] for i in indices_per_size[len(SCREAMING_SNAKE_CASE__     )]]
       return shuffled_kwargs






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}


