




import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







def 		lowercase__   (		__A:	Features				):





					'''simple docstring'''






					__magic_name__							:	Any    							=							np.inf

					def set_batch_size(__A:	FeatureType				) -> None:
										nonlocal batch_size
										if isinstance(__A							,__A				):
															__magic_name__							:	Optional[int]    							=							min(__A							,config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS				)
										elif isinstance(__A							,__A				):
															__magic_name__							:	List[str]    							=							min(__A							,config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS				)
										elif isinstance(__A							,__A				) and feature.dtype == "binary":
															__magic_name__							:	str    							=							min(__A							,config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS				)

					_visit(__A							,__A				)

					return None if batch_size is np.inf else batch_size


class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def __init__( self			:   List[str]       ,						lowerCamelCase_			:   NestedDataStructureLike[PathLike]       ,						lowerCamelCase_			:   Optional[NamedSplit] = None       ,						lowerCamelCase_			:   Optional[Features] = None       ,						lowerCamelCase_			:   str = None       ,						lowerCamelCase_			:   bool = False       ,						lowerCamelCase_			:   bool = False       ,						lowerCamelCase_			:   Optional[int] = None       ,						**lowerCamelCase_			:   Optional[int]       ,						)						->   Dict:
								super().__init__(
								    lowerCamelCase_       ,						split=lowerCamelCase_       ,						features=lowerCamelCase_       ,						cache_dir=lowerCamelCase_       ,						keep_in_memory=lowerCamelCase_       ,						streaming=lowerCamelCase_       ,						num_proc=lowerCamelCase_       ,						**lowerCamelCase_       ,						)
								__magic_name__							:	List[Any]    							=							path_or_paths if isinstance(lowerCamelCase_       ,						lowerCamelCase_  ) else {self.split: path_or_paths}
								__magic_name__							:	List[Any]    							=							_PACKAGED_DATASETS_MODULES['''parquet'''][1]
								__magic_name__							:	List[str]    							=							Parquet(
								    cache_dir=lowerCamelCase_       ,						data_files=lowerCamelCase_       ,						features=lowerCamelCase_       ,						hash=lowerCamelCase_       ,						**lowerCamelCase_       ,						)


			def 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   List[Any]:
								# Build iterable dataset
								if self.streaming:
													__magic_name__							:	Dict    							=							self.builder.as_streaming_dataset(split=self.split  )
								# Build regular (map-style) dataset
								else:
													__magic_name__							:	Dict    							=							None
													__magic_name__							:	Union[str, Any]    							=							None
													__magic_name__							:	Optional[Any]    							=							None
													__magic_name__							:	Optional[int]    							=							None

													self.builder.download_and_prepare(
													    download_config=lowerCamelCase_       ,						download_mode=lowerCamelCase_       ,						verification_mode=lowerCamelCase_       ,						base_path=lowerCamelCase_       ,						num_proc=self.num_proc       ,						)
													__magic_name__							:	int    							=							self.builder.as_dataset(
													    split=self.split       ,						verification_mode=lowerCamelCase_       ,						in_memory=self.keep_in_memory  )
								return dataset






class    lowerCamelCase    :






			'''simple docstring'''





			def __init__( self			:   Union[str, Any]       ,						lowerCamelCase_			:   Dataset       ,						lowerCamelCase_			:   Union[PathLike, BinaryIO]       ,						lowerCamelCase_			:   Optional[int] = None       ,						**lowerCamelCase_			:   List[str]       ,						)						->   Dict:
								__magic_name__							:	Union[str, Any]    							=							dataset
								__magic_name__							:	List[str]    							=							path_or_buf
								__magic_name__							:	Tuple    							=							batch_size or get_writer_batch_size(dataset.features  )
								__magic_name__							:	Optional[int]    							=							parquet_writer_kwargs

			def 							UpperCAmelCase__       ( self			:   Dict  )						->   int:
								__magic_name__							:	str    							=							self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

								if isinstance(self.path_or_buf       ,						(str, bytes, os.PathLike)  ):
													with open(self.path_or_buf       ,						'''wb+'''  ) as buffer:
																		__magic_name__							:	List[str]    							=							self._write(file_obj=lowerCamelCase_       ,						batch_size=lowerCamelCase_       ,						**self.parquet_writer_kwargs  )
								else:
													__magic_name__							:	Tuple    							=							self._write(file_obj=self.path_or_buf       ,						batch_size=lowerCamelCase_       ,						**self.parquet_writer_kwargs  )
								return written


			def 							UpperCAmelCase__       ( self			:   List[str]       ,						lowerCamelCase_			:   BinaryIO       ,						lowerCamelCase_			:   int       ,						**lowerCamelCase_			:   Dict  )						->   int:
								__magic_name__							:	Dict    							=							0
								__magic_name__							:	int    							=							parquet_writer_kwargs.pop('''path_or_buf'''       ,						lowerCamelCase_  )
								__magic_name__							:	List[str]    							=							self.dataset.features.arrow_schema

								__magic_name__							:	Dict    							=							pq.ParquetWriter(lowerCamelCase_       ,						schema=lowerCamelCase_       ,						**lowerCamelCase_  )

								for offset in logging.tqdm(
								    range(0       ,						len(self.dataset  )       ,						lowerCamelCase_  )       ,						unit='''ba'''       ,						disable=not logging.is_progress_bar_enabled()       ,						desc='''Creating parquet from Arrow format'''       ,						):
													__magic_name__							:	List[Any]    							=							query_table(
													    table=self.dataset._data       ,						key=slice(lowerCamelCase_       ,						offset + batch_size  )       ,						indices=self.dataset._indices if self.dataset._indices is not None else None       ,						)
													writer.write_table(lowerCamelCase_  )
													written += batch.nbytes
								writer.close()
								return written









from sklearn.metrics import mean_squared_error

import datasets


__lowerCamelCase   :						List[str]				=			'''\
@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}
}
'''

__lowerCamelCase   :						Optional[int]				=			'''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''


__lowerCamelCase   :						str				=			'''
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    lowerCamelCase    (		datasets.Metric						):






			'''simple docstring'''





			def 							UpperCAmelCase__       ( self			:   Dict  )						->   Tuple:
								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 							UpperCAmelCase__       ( self			:   List[Any]  )						->   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 							UpperCAmelCase__       ( self			:   Union[str, Any]       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   int=None       ,						lowerCamelCase_			:   List[Any]="uniform_average"       ,						lowerCamelCase_			:   List[str]=True  )						->   List[str]:
								__magic_name__							:	Tuple    							=							mean_squared_error(
								    lowerCamelCase_       ,						lowerCamelCase_       ,						sample_weight=lowerCamelCase_       ,						multioutput=lowerCamelCase_       ,						squared=lowerCamelCase_  )

								return {"mse": mse}



