






"""simple docstring"""







import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


a			:    List[str]				 =		get_tests_dir("""fixtures/test_sentencepiece.model""")

if is_torch_available():
			from transformers.models.mbart.modeling_mbart import shift_tokens_right

a			:    int				 =		250004
a			:    List[str]				 =		250020


@require_sentencepiece
@require_tokenizers
class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__  ,  unittest.TestCase							):




						"""simple docstring"""




						__lowerCamelCase    	= MBartaaTokenizer
						__lowerCamelCase    	= MBartaaTokenizerFast
						__lowerCamelCase    	= True
						__lowerCamelCase    	= True




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												super().setUp()

												# We have a SentencePiece fixture for testing
												lowercase__    :				Any=	MBartaaTokenizer(snake_case__	,  src_lang="en_XX"	,  tgt_lang="ro_RO"	,  keep_accents=snake_case__      )
												tokenizer.save_pretrained(self.tmpdirname      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				List[str]=	"<s>"
												lowercase__    :				Union[str, Any]=	0

												self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__      )	,  snake_case__      )
												self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__      )	,  snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Optional[int]=	list(self.get_tokenizer().get_vocab().keys()      )

												self.assertEqual(vocab_keys[0]	,  "<s>"      )
												self.assertEqual(vocab_keys[1]	,  "<pad>"      )
												self.assertEqual(vocab_keys[-1]	,  "<mask>"      )
												self.assertEqual(len(snake_case__      )	,  1054      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												self.assertEqual(self.get_tokenizer().vocab_size	,  1054      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				List[str]=	MBartaaTokenizer(snake_case__	,  src_lang="en_XX"	,  tgt_lang="ro_RO"	,  keep_accents=snake_case__      )

												lowercase__    :				Union[str, Any]=	tokenizer.tokenize("This is a test"      )
												self.assertListEqual(snake_case__	,  ["▁This", "▁is", "▁a", "▁t", "est"]      )

												self.assertListEqual(
												    tokenizer.convert_tokens_to_ids(snake_case__      )	,  [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]	,  )

												lowercase__    :				int=	tokenizer.tokenize("I was born in 92000, and this is falsé."      )
												self.assertListEqual(
												    snake_case__	,  [SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", "."]	,  )
												lowercase__    :				Optional[int]=	tokenizer.convert_tokens_to_ids(snake_case__      )
												self.assertListEqual(
												    snake_case__	,  [
												        value + tokenizer.fairseq_offset
												        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
												    ]	,  )

												lowercase__    :				int=	tokenizer.convert_ids_to_tokens(snake_case__      )
												self.assertListEqual(
												    snake_case__	,  [SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "<unk>", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "<unk>", "."]	,  )




						@slow
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												# fmt: off
												lowercase__    :				Union[str, Any]=	{"input_ids": [[250004, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [250004, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [250004, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
												# fmt: on

												self.tokenizer_integration_test_util(
												    expected_encoding=snake_case__	,  model_name="facebook/mbart-large-50"	,  revision="d3913889c59cd5c9e456b269c376325eabad57e2"	,  )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												if not self.test_slow_tokenizer:
																		# as we don't have a slow version, we can't compare the outputs between slow and fast versions
																		return

												lowercase__    :				str=	(self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart50", {})
												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
																								lowercase__    :				List[Any]=	self.rust_tokenizer_class.from_pretrained(snake_case__	,  **snake_case__      )
																								lowercase__    :				str=	self.tokenizer_class.from_pretrained(snake_case__	,  **snake_case__      )

																								lowercase__    :				int=	tempfile.mkdtemp()

																								lowercase__    :				int=	tokenizer_r.save_pretrained(snake_case__      )
																								lowercase__    :				Union[str, Any]=	tokenizer_p.save_pretrained(snake_case__      )

																								# Checks it save with the same files + the tokenizer.json file for the fast one
																								self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files      )      )
																								lowercase__    :				List[str]=	tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f      )
																								self.assertSequenceEqual(snake_case__	,  snake_case__      )

																								# Checks everything loads correctly in the same way
																								lowercase__    :				Tuple=	tokenizer_r.from_pretrained(snake_case__      )
																								lowercase__    :				Tuple=	tokenizer_p.from_pretrained(snake_case__      )

																								# Check special tokens are set accordingly on Rust and Python
																								for key in tokenizer_pp.special_tokens_map:
																														self.assertTrue(hasattr(snake_case__	,  snake_case__      )      )
																														# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																														# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																								shutil.rmtree(snake_case__      )

																								# Save tokenizer rust, legacy_format=True
																								lowercase__    :				str=	tempfile.mkdtemp()

																								lowercase__    :				List[str]=	tokenizer_r.save_pretrained(snake_case__	,  legacy_format=snake_case__      )
																								lowercase__    :				Optional[int]=	tokenizer_p.save_pretrained(snake_case__      )

																								# Checks it save with the same files
																								self.assertSequenceEqual(snake_case__	,  snake_case__      )

																								# Checks everything loads correctly in the same way
																								lowercase__    :				Optional[Any]=	tokenizer_r.from_pretrained(snake_case__      )
																								lowercase__    :				List[Any]=	tokenizer_p.from_pretrained(snake_case__      )

																								# Check special tokens are set accordingly on Rust and Python
																								for key in tokenizer_pp.special_tokens_map:
																														self.assertTrue(hasattr(snake_case__	,  snake_case__      )      )

																								shutil.rmtree(snake_case__      )

																								# Save tokenizer rust, legacy_format=False
																								lowercase__    :				Union[str, Any]=	tempfile.mkdtemp()

																								lowercase__    :				List[str]=	tokenizer_r.save_pretrained(snake_case__	,  legacy_format=snake_case__      )
																								lowercase__    :				str=	tokenizer_p.save_pretrained(snake_case__      )

																								# Checks it saved the tokenizer.json file
																								self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files      )      )

																								# Checks everything loads correctly in the same way
																								lowercase__    :				str=	tokenizer_r.from_pretrained(snake_case__      )
																								lowercase__    :				List[Any]=	tokenizer_p.from_pretrained(snake_case__      )

																								# Check special tokens are set accordingly on Rust and Python
																								for key in tokenizer_pp.special_tokens_map:
																														self.assertTrue(hasattr(snake_case__	,  snake_case__      )      )

																								shutil.rmtree(snake_case__      )


@require_torch
@require_sentencepiece
@require_tokenizers
class    __UpperCAmelCase(       unittest.TestCase							):




						"""simple docstring"""




						__lowerCamelCase    	= "facebook/mbart-large-50-one-to-many-mmt"
						__lowerCamelCase    	= [
						    " UN Chief Says There Is No Military Solution in Syria",
						    " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
						]
						__lowerCamelCase    	= [
						    "Şeful ONU declară că nu există o soluţie militară în Siria",
						    "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
						    " pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
						    " face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
						]
						__lowerCamelCase    	= [EN_CODE, 8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2]




						@classmethod
						def 		UpperCAmelCase_	(       cls      ):
												'''simple docstring'''
												lowercase__    :				MBartaaTokenizer=	MBartaaTokenizer.from_pretrained(
												    cls.checkpoint_name	,  src_lang="en_XX"	,  tgt_lang="ro_RO"      )
												lowercase__    :				List[Any]=	1
												return cls




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]	,  250001      )
												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]	,  250004      )
												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]	,  250020      )
												self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["mr_IN"]	,  250038      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Union[str, Any]=	self.tokenizer.batch_encode_plus(self.src_text      ).input_ids[0]
												self.assertListEqual(self.expected_src_tokens	,  snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												self.assertIn(snake_case__	,  self.tokenizer.all_special_ids      )
												lowercase__    :				Tuple=	[RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
												lowercase__    :				str=	self.tokenizer.decode(snake_case__	,  skip_special_tokens=snake_case__      )
												lowercase__    :				Tuple=	self.tokenizer.decode(generated_ids[1:]	,  skip_special_tokens=snake_case__      )
												self.assertEqual(snake_case__	,  snake_case__      )
												self.assertNotIn(self.tokenizer.eos_token	,  snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Optional[int]=	["this is gunna be a long sentence " * 20]
												assert isinstance(src_text[0]	,  snake_case__      )
												lowercase__    :				Optional[int]=	10
												lowercase__    :				Optional[Any]=	self.tokenizer(snake_case__	,  max_length=snake_case__	,  truncation=snake_case__      ).input_ids[0]
												self.assertEqual(ids[0]	,  snake_case__      )
												self.assertEqual(ids[-1]	,  2      )
												self.assertEqual(len(snake_case__      )	,  snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]      )	,  [250053, 250001]      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Any=	tempfile.mkdtemp()
												lowercase__    :				List[str]=	self.tokenizer.fairseq_tokens_to_ids
												self.tokenizer.save_pretrained(snake_case__      )
												lowercase__    :				int=	MBartaaTokenizer.from_pretrained(snake_case__      )
												self.assertDictEqual(new_tok.fairseq_tokens_to_ids	,  snake_case__      )




						@require_torch
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Any=	self.tokenizer(self.src_text	,  text_target=self.tgt_text	,  padding=snake_case__	,  return_tensors="pt"      )
												lowercase__    :				Optional[Any]=	shift_tokens_right(batch["labels"]	,  self.tokenizer.pad_token_id      )

												# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
												assert batch.input_ids[1][0] == EN_CODE
												assert batch.input_ids[1][-1] == 2
												assert batch.labels[1][0] == RO_CODE
												assert batch.labels[1][-1] == 2
												assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]




						@require_torch
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Optional[int]=	self.tokenizer(
												    self.src_text	,  text_target=self.tgt_text	,  padding=snake_case__	,  truncation=snake_case__	,  max_length=len(self.expected_src_tokens      )	,  return_tensors="pt"	,  )
												lowercase__    :				Union[str, Any]=	shift_tokens_right(batch["labels"]	,  self.tokenizer.pad_token_id      )

												self.assertIsInstance(snake_case__	,  snake_case__      )

												self.assertEqual((2, 14)	,  batch.input_ids.shape      )
												self.assertEqual((2, 14)	,  batch.attention_mask.shape      )
												lowercase__    :				int=	batch.input_ids.tolist()[0]
												self.assertListEqual(self.expected_src_tokens	,  snake_case__      )
												self.assertEqual(2	,  batch.decoder_input_ids[0, 0]      )  # decoder_start_token_id
												# Test that special tokens are reset
												self.assertEqual(self.tokenizer.prefix_tokens	,  [EN_CODE]      )
												self.assertEqual(self.tokenizer.suffix_tokens	,  [self.tokenizer.eos_token_id]      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Dict=	self.tokenizer(self.src_text	,  padding=snake_case__	,  truncation=snake_case__	,  max_length=3	,  return_tensors="pt"      )
												lowercase__    :				Union[str, Any]=	self.tokenizer(
												    text_target=self.tgt_text	,  padding=snake_case__	,  truncation=snake_case__	,  max_length=10	,  return_tensors="pt"      )
												lowercase__    :				List[Any]=	targets["input_ids"]
												lowercase__    :				Dict=	shift_tokens_right(snake_case__	,  self.tokenizer.pad_token_id      )

												self.assertEqual(batch.input_ids.shape[1]	,  3      )
												self.assertEqual(batch.decoder_input_ids.shape[1]	,  10      )




						@require_torch
						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Dict=	self.tokenizer._build_translation_inputs(
												    "A test"	,  return_tensors="pt"	,  src_lang="en_XX"	,  tgt_lang="ar_AR"      )

												self.assertEqual(
												    nested_simplify(snake_case__      )	,  {
												        # en_XX, A, test, EOS
												        "input_ids": [[250004, 62, 3034, 2]],
												        "attention_mask": [[1, 1, 1, 1]],
												        # ar_AR
												        "forced_bos_token_id": 250001,
												    }	,  )














"""simple docstring"""







from scipy.stats import spearmanr

import datasets


a			:    Dict				 =		"""
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.
"""

a			:    List[Any]				 =		"""
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
"""

a			:    int				 =		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    __UpperCAmelCase(       datasets.Metric							):




						"""simple docstring"""




						def 		UpperCAmelCase_	(       self      ):
												'''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 		UpperCAmelCase_	(       self	,  snake_case__	,  snake_case__	,  snake_case__=False      ):
												'''simple docstring'''
												lowercase__    :				Optional[int]=	spearmanr(snake_case__	,  snake_case__      )
												if return_pvalue:
																		return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
												else:
																		return {"spearmanr": results[0]}






