

import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch


def 			_UpperCAmelCase							(		):

			'''simple docstring'''



			a_		:		Tuple				    =				Dataset.from_dict({"""i""": range(1_7)})
			a_		:		Optional[Any]				    =				len(__UpperCamelCase)
			a_		:		str				    =				3
			a_		:		str				    =				[
			    split_dataset_by_node(__UpperCamelCase       ,					rank=__UpperCamelCase       ,					world_size=__UpperCamelCase) for rank in range(__UpperCamelCase)
			]
			assert sum(len(__UpperCamelCase) for ds in datasets_per_rank) == full_size
			assert len({tuple(x.values()) for ds in datasets_per_rank for x in ds}) == full_size


def 			_UpperCAmelCase							(		):

			'''simple docstring'''



			def gen():
						return ({"i": i} for i in range(1_7))

			a_		:		List[Any]				    =				3
			a_		:		List[Any]				    =				IterableDataset.from_generator(__UpperCamelCase)
			a_		:		str				    =				len(list(__UpperCamelCase))
			a_		:		int				    =				[
			    split_dataset_by_node(__UpperCamelCase       ,					rank=__UpperCamelCase       ,					world_size=__UpperCamelCase) for rank in range(__UpperCamelCase)
			]
			assert sum(len(list(__UpperCamelCase)) for ds in datasets_per_rank) == full_size
			assert len({tuple(x.values()) for ds in datasets_per_rank for x in ds}) == full_size


@pytest.mark.parametrize("""shards_per_node"""       ,					[1, 2, 3])
def 			_UpperCAmelCase							(		a__):

			'''simple docstring'''



			def gen(a__):
						for shard in shards:
									yield from ({"i": i, "shard": shard} for i in range(1_7))

			a_		:		str				    =				3
			a_		:		List[Any]				    =				shards_per_node * world_size
			a_		:		List[str]				    =				{"""shards""": [f'''shard_{shard_idx}.txt''' for shard_idx in range(__UpperCamelCase)]}
			a_		:		Union[str, Any]				    =				IterableDataset.from_generator(__UpperCamelCase       ,					gen_kwargs=__UpperCamelCase)
			a_		:		Tuple				    =				len(list(__UpperCamelCase))
			assert full_ds.n_shards == world_size * shards_per_node
			a_		:		Union[str, Any]				    =				[
			    split_dataset_by_node(__UpperCamelCase       ,					rank=__UpperCamelCase       ,					world_size=__UpperCamelCase) for rank in range(__UpperCamelCase)
			]
			assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
			assert sum(len(list(__UpperCamelCase)) for ds in datasets_per_rank) == full_size
			assert len({tuple(x.values()) for ds in datasets_per_rank for x in ds}) == full_size


def 			_UpperCAmelCase							(		):

			'''simple docstring'''



			def gen():
						return ({"i": i} for i in range(1_7))

			a_		:		List[Any]				    =				2
			a_		:		List[Any]				    =				IterableDataset.from_generator(__UpperCamelCase)
			a_		:		Union[str, Any]				    =				len(list(__UpperCamelCase))

			a_		:		Optional[Any]				    =				split_dataset_by_node(__UpperCamelCase       ,					rank=0       ,					world_size=__UpperCamelCase).shuffle(seed=4_2)
			assert len(list(__UpperCamelCase)) == 1 + full_size // world_size
			with pytest.raises(__UpperCamelCase):
						split_dataset_by_node(__UpperCamelCase       ,					rank=0       ,					world_size=__UpperCamelCase).shuffle()

			a_		:		str				    =				split_dataset_by_node(full_ds.shuffle(seed=4_2)       ,					rank=0       ,					world_size=__UpperCamelCase)
			assert len(list(__UpperCamelCase)) == 1 + full_size // world_size
			with pytest.raises(__UpperCamelCase):
						split_dataset_by_node(full_ds.shuffle()       ,					rank=0       ,					world_size=__UpperCamelCase)


@pytest.mark.parametrize("""streaming"""       ,					[False, True])
@require_torch
@pytest.mark.skipif(os.name == """nt"""       ,					reason="""execute_subprocess_async doesn\'t support windows""")
@pytest.mark.integration
def 			_UpperCAmelCase							(		a__):

			'''simple docstring'''



			a_		:		List[str]				    =				2
			a_		:		Optional[int]				    =				get_torch_dist_unique_port()
			a_		:		Dict				    =				Path(__UpperCamelCase).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
			a_		:		Optional[Any]				    =				f'''\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '''.split()
			a_		:		List[str]				    =				f'''\n        --streaming={streaming}\n    '''.split()
			a_		:		Optional[Any]				    =				[sys.executable] + distributed_args + args
			execute_subprocess_async(__UpperCamelCase       ,					env=os.environ.copy())



@pytest.mark.parametrize(
    """nproc_per_node, num_workers"""       ,					[
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]       ,					)
@require_torch
@pytest.mark.skipif(os.name == """nt"""       ,					reason="""execute_subprocess_async doesn\'t support windows""")
@pytest.mark.integration
def 			_UpperCAmelCase							(		a__       ,					a__):

			'''simple docstring'''



			a_		:		Dict				    =				True
			a_		:		Optional[Any]				    =				get_torch_dist_unique_port()
			a_		:		Union[str, Any]				    =				Path(__UpperCamelCase).resolve().parent / """distributed_scripts""" / """launch_torch_distributed.py"""
			a_		:		Union[str, Any]				    =				f'''\n        -m torch.distributed.launch\n        --nproc_per_node={nproc_per_node}\n        --master_port={master_port}\n        {test_script}\n    '''.split()
			a_		:		str				    =				f'''\n        --streaming={streaming}\n        --num_workers={num_workers}\n    '''.split()
			a_		:		Optional[int]				    =				[sys.executable] + distributed_args + args
			execute_subprocess_async(__UpperCamelCase       ,					env=os.environ.copy())








from sklearn.metrics import mean_absolute_error

import datasets


__snake_case							:     List[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}
}
"""

__snake_case							:     List[Any] 						=	"""\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


__snake_case							:     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.

Returns:
    mae : mean absolute error.
        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric(\"mae\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.5}

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

    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results)
    {'mae': array([0.5, 1. ])}
"""



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,       _KWARGS_DESCRIPTION    )
class    A__(datasets.Metric    ):
						"""simple docstring"""






						def 	UpperCamelCase__			(  self  )      ->				List[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_absolute_error.html"""
									    ]       ,     )





						def 	UpperCamelCase__			(  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 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase=None       ,     _lowercase="uniform_average"  )      ->				Union[str, Any]:
									a_		:		Any				    =				mean_absolute_error(_lowercase       ,     _lowercase       ,     sample_weight=_lowercase       ,     multioutput=_lowercase  )

									return {"mae": mae_score}


