



'''simple docstring'''




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    __lowerCamelCase						(      _lowercase	)   ->    Any:
							UpperCAmelCase  :	Union[str, Any]							  =	np.inf

							def set_batch_size(_lowercase	) -> None:
														nonlocal batch_size
														if isinstance(_lowercase			,       _lowercase	):
																					UpperCAmelCase  :	int							  =	min(_lowercase			,       config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS	)
														elif isinstance(_lowercase			,       _lowercase	):
																					UpperCAmelCase  :	Tuple							  =	min(_lowercase			,       config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS	)
														elif isinstance(_lowercase			,       _lowercase	) and feature.dtype == "binary":
																					UpperCAmelCase  :	Optional[Any]							  =	min(_lowercase			,       config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS	)

							_visit(_lowercase			,       _lowercase	)

							return None if batch_size is np.inf else batch_size

class     UpperCamelCase_				(							UpperCamelCase_    ):





	def __init__(						self	,	A	,	A = None	,	A = None	,	A = None	,	A = False	,	A = False	,	A = None	,	**A	,	)    ->    int:
								super().__init__(
								    UpperCamelCase__	,	split=UpperCamelCase__	,	features=UpperCamelCase__	,	cache_dir=UpperCamelCase__	,	keep_in_memory=UpperCamelCase__	,	streaming=UpperCamelCase__	,	num_proc=UpperCamelCase__	,	**UpperCamelCase__	,	)
								UpperCAmelCase  :	Optional[int]							  =	path_or_paths if isinstance(UpperCamelCase__	,	UpperCamelCase__					) else {self.split: path_or_paths}
								UpperCAmelCase  :	Union[str, Any]							  =	_PACKAGED_DATASETS_MODULES['''parquet'''][1]
								UpperCAmelCase  :	Optional[Any]							  =	Parquet(
								    cache_dir=UpperCamelCase__	,	data_files=UpperCamelCase__	,	features=UpperCamelCase__	,	hash=UpperCamelCase__	,	**UpperCamelCase__	,	)






	def 				_lowercase(						self					)    ->    Dict:





								if self.streaming:
															UpperCAmelCase  :	int							  =	self.builder.as_streaming_dataset(split=self.split					)
								# Build regular (map-style) dataset
								else:
															UpperCAmelCase  :	Tuple							  =	None
															UpperCAmelCase  :	Any							  =	None
															UpperCAmelCase  :	str							  =	None
															UpperCAmelCase  :	Optional[Any]							  =	None

															self.builder.download_and_prepare(
															    download_config=UpperCamelCase__	,	download_mode=UpperCamelCase__	,	verification_mode=UpperCamelCase__	,	base_path=UpperCamelCase__	,	num_proc=self.num_proc	,	)
															UpperCAmelCase  :	str							  =	self.builder.as_dataset(
															    split=self.split	,	verification_mode=UpperCamelCase__	,	in_memory=self.keep_in_memory					)
								return dataset




class     UpperCamelCase_				:





	def __init__(						self	,	A	,	A	,	A = None	,	**A	,	)    ->    str:
								UpperCAmelCase  :	Optional[Any]							  =	dataset
								UpperCAmelCase  :	Any							  =	path_or_buf
								UpperCAmelCase  :	Any							  =	batch_size or get_writer_batch_size(dataset.features					)
								UpperCAmelCase  :	Tuple							  =	parquet_writer_kwargs





	def 				_lowercase(						self					)    ->    List[Any]:
								UpperCAmelCase  :	Optional[int]							  =	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:
																						UpperCAmelCase  :	Optional[int]							  =	self._write(file_obj=UpperCamelCase__	,	batch_size=UpperCamelCase__	,	**self.parquet_writer_kwargs					)
								else:
															UpperCAmelCase  :	List[Any]							  =	self._write(file_obj=self.path_or_buf	,	batch_size=UpperCamelCase__	,	**self.parquet_writer_kwargs					)
								return written






	def 				_lowercase(						self	,	A	,	A	,	**A					)    ->    str:
								UpperCAmelCase  :	Optional[int]							  =	0
								UpperCAmelCase  :	str							  =	parquet_writer_kwargs.pop("""path_or_buf"""	,	UpperCamelCase__					)
								UpperCAmelCase  :	List[Any]							  =	self.dataset.features.arrow_schema

								UpperCAmelCase  :	List[Any]							  =	pq.ParquetWriter(UpperCamelCase__	,	schema=UpperCamelCase__	,	**UpperCamelCase__					)

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






'''simple docstring'''




from scipy.stats import pearsonr

import datasets


a     :    str      						=					"""
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
"""


a     :    Dict      						=					"""
Args:
    predictions (`list` of `int`): Predicted class labels, as returned by a model.
    references (`list` of `int`): Ground truth labels.
    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.

Returns:
    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.

Examples:

    Example 1-A simple example using only predictions and references.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
        >>> print(round(results['pearsonr'], 2))
        -0.74

    Example 2-The same as Example 1, but that also returns the `p-value`.
        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
        >>> print(sorted(list(results.keys())))
        ['p-value', 'pearsonr']
        >>> print(round(results['pearsonr'], 2))
        -0.74
        >>> print(round(results['p-value'], 2))
        0.15
"""


a     :    Optional[int]      						=					"""
@article{2020SciPy-NMeth,
author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
      Haberland, Matt and Reddy, Tyler and Cournapeau, David and
      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
      Kern, Robert and Larson, Eric and Carey, C J and
      Polat, Ilhan and Feng, Yu and Moore, Eric W. and
      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
      Harris, Charles R. and Archibald, Anne M. and
      Ribeiro, Antonio H. and Pedregosa, Fabian and
      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
      Computing in Python}},
journal = {Nature Methods},
year    = {2020},
volume  = {17},
pages   = {261--272},
adsurl  = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
"""

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





	def 				_lowercase(						self					)    ->    Optional[int]:
								return datasets.MetricInfo(
								    description=_DESCRIPTION	,	citation=_CITATION	,	inputs_description=_KWARGS_DESCRIPTION	,	features=datasets.Features(
								        {
								            """predictions""": datasets.Value("""float"""					),
								            """references""": datasets.Value("""float"""					),
								        }					)	,	reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html"""]	,	)






	def 				_lowercase(						self	,	A	,	A	,	A=False					)    ->    int:
								if return_pvalue:
															UpperCAmelCase  :	int							  =	pearsonr(A	,	A					)
															return {"pearsonr": results[0], "p-value": results[1]}
								else:
															return {"pearsonr": float(pearsonr(A	,	A					)[0]					)}

