
'''simple docstring'''




import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch

def 	__UpperCAmelCase       ( __magic_name__						)->    Dict:  # picklable for multiprocessing



			"""simple docstring"""



			return x.sum()

def 	__UpperCAmelCase       ( __magic_name__						)->    List[Any]:  # picklable for multiprocessing



			"""simple docstring"""



			return i + 1







@dataclass
class    A_	:
							"""simple docstring"""




							a__   		=	42
							a__   		=	42







class    A_	(a_							):
							"""simple docstring"""




							def 							_A	(		self			:Dict				)				->  Any:


										'''simple docstring'''




										snake_case_				:					List[str]   =       {}
										snake_case_				:					Optional[Any]   =       []
										snake_case_				:					str   =       1
										snake_case_				:					Optional[int]   =       [1, 2]
										snake_case_				:					Optional[int]   =       {"a": 1, "b": 2}
										snake_case_				:					List[str]   =       {"a": [1, 2], "b": [3, 4]}
										snake_case_				:					Dict   =       {"a": {"1": 1}, "b": 2}
										snake_case_				:					Any   =       {"a": 1, "b": 2, "c": 3, "d": 4}
										snake_case_				:					Optional[Any]   =       {}
										snake_case_				:					Any   =       []
										snake_case_				:					List[Any]   =       2
										snake_case_				:					Tuple   =       [2, 3]
										snake_case_				:					Any   =       {"a": 2, "b": 3}
										snake_case_				:					str   =       {"a": [2, 3], "b": [4, 5]}
										snake_case_				:					Optional[int]   =       {"a": {"1": 2}, "b": 3}
										snake_case_				:					int   =       {"a": 2, "b": 3, "c": 4, "d": 5}
										self.assertEqual(map_nested(lowerCAmelCase__       ,							lowerCAmelCase__				)       ,							lowerCAmelCase__				)
										self.assertEqual(map_nested(lowerCAmelCase__       ,							lowerCAmelCase__				)       ,							lowerCAmelCase__				)
										self.assertEqual(map_nested(lowerCAmelCase__       ,							lowerCAmelCase__				)       ,							lowerCAmelCase__				)
										self.assertEqual(map_nested(lowerCAmelCase__       ,							lowerCAmelCase__				)       ,							lowerCAmelCase__				)
										self.assertEqual(map_nested(lowerCAmelCase__       ,							lowerCAmelCase__				)       ,							lowerCAmelCase__				)
										self.assertEqual(map_nested(lowerCAmelCase__       ,							lowerCAmelCase__				)       ,							lowerCAmelCase__				)
										self.assertEqual(map_nested(lowerCAmelCase__       ,							lowerCAmelCase__				)       ,							lowerCAmelCase__				)
										self.assertEqual(map_nested(lowerCAmelCase__       ,							lowerCAmelCase__				)       ,							lowerCAmelCase__				)

										snake_case_				:					Optional[int]   =       2
										self.assertEqual(map_nested(lowerCAmelCase__       ,							lowerCAmelCase__       ,							num_proc=lowerCAmelCase__				)       ,							lowerCAmelCase__				)
										self.assertEqual(map_nested(lowerCAmelCase__       ,							lowerCAmelCase__       ,							num_proc=lowerCAmelCase__				)       ,							lowerCAmelCase__				)
										self.assertEqual(map_nested(lowerCAmelCase__       ,							lowerCAmelCase__       ,							num_proc=lowerCAmelCase__				)       ,							lowerCAmelCase__				)
										self.assertEqual(map_nested(lowerCAmelCase__       ,							lowerCAmelCase__       ,							num_proc=lowerCAmelCase__				)       ,							lowerCAmelCase__				)
										self.assertEqual(map_nested(lowerCAmelCase__       ,							lowerCAmelCase__       ,							num_proc=lowerCAmelCase__				)       ,							lowerCAmelCase__				)
										self.assertEqual(map_nested(lowerCAmelCase__       ,							lowerCAmelCase__       ,							num_proc=lowerCAmelCase__				)       ,							lowerCAmelCase__				)
										self.assertEqual(map_nested(lowerCAmelCase__       ,							lowerCAmelCase__       ,							num_proc=lowerCAmelCase__				)       ,							lowerCAmelCase__				)
										self.assertEqual(map_nested(lowerCAmelCase__       ,							lowerCAmelCase__       ,							num_proc=lowerCAmelCase__				)       ,							lowerCAmelCase__				)

										snake_case_				:					List[Any]   =       {"a": np.eye(2				), "b": np.zeros(3				), "c": np.ones(2				)}
										snake_case_				:					Union[str, Any]   =       {"a": 2, "b": 0, "c": 2}
										snake_case_				:					Dict   =       {
										    "a": np.eye(2				).astype(lowerCAmelCase__				),
										    "b": np.zeros(3				).astype(lowerCAmelCase__				),
										    "c": np.ones(2				).astype(lowerCAmelCase__				),
										}
										self.assertEqual(map_nested(lowerCAmelCase__       ,							lowerCAmelCase__       ,							map_numpy=lowerCAmelCase__				)       ,							lowerCAmelCase__				)
										self.assertEqual(
										    {k: v.tolist() for k, v in map_nested(lowerCAmelCase__       ,							lowerCAmelCase__       ,							map_numpy=lowerCAmelCase__				).items()}       ,							{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}       ,							)
										self.assertEqual(map_nested(lowerCAmelCase__       ,							lowerCAmelCase__       ,							map_numpy=lowerCAmelCase__       ,							num_proc=lowerCAmelCase__				)       ,							lowerCAmelCase__				)
										self.assertEqual(
										    {k: v.tolist() for k, v in map_nested(lowerCAmelCase__       ,							lowerCAmelCase__       ,							map_numpy=lowerCAmelCase__       ,							num_proc=lowerCAmelCase__				).items()}       ,							{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}       ,							)
										with self.assertRaises(lowerCAmelCase__				):  # can't pickle a local lambda
													map_nested(lambda lowerCAmelCase__				: x + 1       ,							lowerCAmelCase__       ,							num_proc=lowerCAmelCase__				)

							def 							_A	(		self			:Union[str, Any]				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       {"a": 1, "b": 2}
										snake_case_				:					List[Any]   =       {"a": 3, "b": 4}
										snake_case_				:					Optional[int]   =       {"a": 5, "b": 6}
										snake_case_				:					Optional[Any]   =       sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))]				)
										self.assertEqual(sorted(zip_dict(lowerCAmelCase__       ,							lowerCAmelCase__       ,							lowerCAmelCase__				)				)       ,							lowerCAmelCase__				)






							def 							_A	(		self			:Tuple				)				->  str:


										'''simple docstring'''




										class    A_	:
																	"""simple docstring"""




																	a__   		=	'''bar'''
										snake_case_				:					int   =       Foo()
										self.assertEqual(foo.my_attr       ,							"bar"				)
										with temporary_assignment(lowerCAmelCase__       ,							"my_attr"       ,							"BAR"				):
													self.assertEqual(foo.my_attr       ,							"BAR"				)
										self.assertEqual(foo.my_attr       ,							"bar"				)

@pytest.mark.parametrize(
    "iterable_length, num_proc, expected_num_proc"     ,[
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ]     ,)
def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    int:



			"""simple docstring"""



			with patch("datasets.utils.py_utils._single_map_nested"						) as mock_single_map_nested, patch(
			    "datasets.parallel.parallel.Pool"						) as mock_multiprocessing_pool:
						snake_case_				:					Any   =       {F'''{i}''': i for i in range(__magic_name__						)}
						snake_case_				:					Any   =       map_nested(lambda __magic_name__						: x + 10     ,__magic_name__     ,num_proc=__magic_name__     ,parallel_min_length=16						)
						if expected_num_proc == 1:
									assert mock_single_map_nested.called
									assert not mock_multiprocessing_pool.called
						else:
									assert not mock_single_map_nested.called
									assert mock_multiprocessing_pool.called
									assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc







class    A_	(a_							):
							"""simple docstring"""




							@require_tf
							def 							_A	(		self			:Any				)				->  Any:


										'''simple docstring'''




										import tensorflow as tf
										from tensorflow.keras import layers

										snake_case_				:					List[str]   =       layers.Dense(2				)

										def gen_random_output():
													snake_case_				:					List[Any]   =       tf.random.uniform((1, 3)				)
													return model(lowerCAmelCase__				).numpy()

										with temp_seed(42       ,							set_tensorflow=lowerCAmelCase__				):
													snake_case_				:					Union[str, Any]   =       gen_random_output()
										with temp_seed(42       ,							set_tensorflow=lowerCAmelCase__				):
													snake_case_				:					str   =       gen_random_output()
										snake_case_				:					str   =       gen_random_output()

										np.testing.assert_equal(lowerCAmelCase__       ,							lowerCAmelCase__				)
										self.assertGreater(np.abs(outa - outa				).sum()       ,							0				)

							@require_torch
							def 							_A	(		self			:Tuple				)				->  Any:


										'''simple docstring'''




										import torch

										def gen_random_output():
													snake_case_				:					Dict   =       torch.nn.Linear(3       ,							2				)
													snake_case_				:					int   =       torch.rand(1       ,							3				)
													return model(lowerCAmelCase__				).detach().numpy()

										with temp_seed(42       ,							set_pytorch=lowerCAmelCase__				):
													snake_case_				:					Any   =       gen_random_output()
										with temp_seed(42       ,							set_pytorch=lowerCAmelCase__				):
													snake_case_				:					Any   =       gen_random_output()
										snake_case_				:					Tuple   =       gen_random_output()

										np.testing.assert_equal(lowerCAmelCase__       ,							lowerCAmelCase__				)
										self.assertGreater(np.abs(outa - outa				).sum()       ,							0				)






							def 							_A	(		self			:Any				)				->  Tuple:


										'''simple docstring'''




										def gen_random_output():
													return np.random.rand(1       ,							3				)

										with temp_seed(42				):
													snake_case_				:					List[str]   =       gen_random_output()
										with temp_seed(42				):
													snake_case_				:					int   =       gen_random_output()
										snake_case_				:					Any   =       gen_random_output()

										np.testing.assert_equal(lowerCAmelCase__       ,							lowerCAmelCase__				)
										self.assertGreater(np.abs(outa - outa				).sum()       ,							0				)

@pytest.mark.parametrize("input_data"     ,[{}]						)
def 	__UpperCAmelCase       ( __magic_name__						)->    Dict:



			"""simple docstring"""



			snake_case_				:					str   =       NestedDataStructure(__magic_name__						).data
			assert output_data == input_data

@pytest.mark.parametrize(
    "data, expected_output"     ,[
        ({}, []),
        ([], []),
        ("foo", ["foo"]),
        (["foo", "bar"], ["foo", "bar"]),
        ([["foo", "bar"]], ["foo", "bar"]),
        ([[["foo"], ["bar"]]], ["foo", "bar"]),
        ([[["foo"], "bar"]], ["foo", "bar"]),
        ({"a": 1, "b": 2}, [1, 2]),
        ({"a": [1, 2], "b": [3, 4]}, [1, 2, 3, 4]),
        ({"a": [[1, 2]], "b": [[3, 4]]}, [1, 2, 3, 4]),
        ({"a": [[1, 2]], "b": [3, 4]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [[[3], [4]]]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [[3, 4]]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [3, 4]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [3, [4]]}, [1, 2, 3, 4]),
        ({"a": {"1": 1}, "b": 2}, [1, 2]),
        ({"a": {"1": [1]}, "b": 2}, [1, 2]),
        ({"a": {"1": [1]}, "b": [2]}, [1, 2]),
    ]     ,)
def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					Any   =       NestedDataStructure(__magic_name__						).flatten()
			assert output == expected_output

def 	__UpperCAmelCase       ( )->    Union[str, Any]:



			"""simple docstring"""



			snake_case_				:					Tuple   =       A(x=1     ,y="foobar"						)
			snake_case_				:					Tuple   =       {"x": 1, "y": "foobar"}
			assert asdict(__magic_name__						) == expected_output

			snake_case_				:					int   =       {"a": {"b": A(x=10     ,y="foo"						)}, "c": [A(x=20     ,y="bar"						)]}
			snake_case_				:					str   =       {"a": {"b": {"x": 10, "y": "foo"}}, "c": [{"x": 20, "y": "bar"}]}
			assert asdict(__magic_name__						) == expected_output

			with pytest.raises(__magic_name__						):
						asdict([1, A(x=10     ,y="foo"						)]						)

def 	__UpperCAmelCase       ( __magic_name__						)->    Tuple:



			"""simple docstring"""



			return text.split()

def 	__UpperCAmelCase       ( __magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			yield (time.time(), content)
			time.sleep(2						)
			yield (time.time(), content)






def 	__UpperCAmelCase       ( )->    Tuple:



			"""simple docstring"""



			with Pool(2						) as pool:
						snake_case_				:					List[Any]   =       list(iflatmap_unordered(__magic_name__     ,_split_text     ,kwargs_iterable=[{"text": "hello there"}] * 10						)						)
						assert out.count("hello"						) == 10
						assert out.count("there"						) == 10
						assert len(__magic_name__						) == 20

			# check multiprocess from pathos (uses dill for pickling)
			with multiprocess.Pool(2						) as pool:
						snake_case_				:					List[str]   =       list(iflatmap_unordered(__magic_name__     ,_split_text     ,kwargs_iterable=[{"text": "hello there"}] * 10						)						)
						assert out.count("hello"						) == 10
						assert out.count("there"						) == 10
						assert len(__magic_name__						) == 20

			# check that we get items as fast as possible
			with Pool(2						) as pool:
						snake_case_				:					Any   =       []
						for yield_time, content in iflatmap_unordered(
						    __magic_name__     ,_aseconds_generator_of_aitems_with_timing     ,kwargs_iterable=[{"content": "a"}, {"content": "b"}]						):
									assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
									out.append(__magic_name__						)
						assert out.count("a"						) == 2
						assert out.count("b"						) == 2
						assert len(__magic_name__						) == 4












'''simple docstring'''




from scipy.stats import spearmanr

import datasets


__lowerCamelCase   :					str   							=					'''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.

Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''

__lowerCamelCase   :					int   							=					'''
Args:
    predictions (`List[float]`): Predicted labels, as returned by a model.
    references (`List[float]`): Ground truth labels.
    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
            only the spearmanr score. Defaults to `False`.
Returns:
    spearmanr (`float`): Spearman correlation coefficient.
    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
    Example 1:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
        >>> print(results)
        {\'spearmanr\': -0.7}

    Example 2:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
        ...                                     predictions=[10, 9, 2.5, 6, 4],
        ...                                     return_pvalue=True)
        >>> print(results[\'spearmanr\'])
        -0.7
        >>> print(round(results[\'spearmanr_pvalue\'], 2))
        0.19
'''

__lowerCamelCase   :					List[str]   							=					R'''\
@book{kokoska2000crc,
  title={CRC standard probability and statistics tables and formulae},
  author={Kokoska, Stephen and Zwillinger, Daniel},
  year={2000},
  publisher={Crc Press}
}
@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, {\.I}lhan 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, Ant{\^o}nio 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    A_	(datasets.Metric							):
							"""simple docstring"""




							def 							_A	(		self			:str				)				->  Union[str, Any]:


										'''simple docstring'''




										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.spearmanr.html"]       ,							)






							def 							_A	(		self			:Optional[int]       ,							lowerCAmelCase__			:Optional[Any]       ,							lowerCAmelCase__			:int       ,							lowerCAmelCase__			:Optional[Any]=False				)				->  List[Any]:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       spearmanr(lowerCAmelCase__       ,							lowerCAmelCase__				)
										if return_pvalue:
													return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
										else:
													return {"spearmanr": results[0]}




