



"""simple docstring"""






from scipy.stats import pearsonr

import datasets


__snake_case							:     Dict   =					'\nPearson correlation coefficient and p-value for testing non-correlation.\nThe 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.\nThe 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.\n'


__snake_case							:     Union[str, Any]   =					'\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    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.\n    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.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric("pearsonr")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        [\'p-value\', \'pearsonr\']\n        >>> print(round(results[\'pearsonr\'], 2))\n        -0.74\n        >>> print(round(results[\'p-value\'], 2))\n        0.15\n'


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



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




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE     (  self:	Any)       -> int:

				"""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.pearsonr.html"] ,    )







	def 							_SCREAMING_SNAKE_CASE     (  self:	int ,    _SCREAMING_SNAKE_CASE:	List[Any] ,    _SCREAMING_SNAKE_CASE:	List[Any] ,    _SCREAMING_SNAKE_CASE:	str=False)       -> List[str]:

				"""simple docstring"""

				if return_pvalue:
							__lowerCAmelCase   :						Optional[Any]				= pearsonr(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)
							return {"pearsonr": results[0], "p-value": results[1]}
				else:
							return {"pearsonr": float(pearsonr(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)[0])}




"""simple docstring"""






import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__snake_case							:     Any   =					get_tests_dir('fixtures/spiece.model')



@require_sentencepiece
@require_tokenizers
class     A__     (						__SCREAMING_SNAKE_CASE						,	unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			AlbertTokenizer
	SCREAMING_SNAKE_CASE					   =			AlbertTokenizerFast
	SCREAMING_SNAKE_CASE					   =			True
	SCREAMING_SNAKE_CASE					   =			True
	SCREAMING_SNAKE_CASE					   =			True

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> Optional[int]:

				"""simple docstring"""

				super().setUp()

				# We have a SentencePiece fixture for testing
				__lowerCAmelCase   :						List[Any]				= AlbertTokenizer(_SCREAMING_SNAKE_CASE)
				tokenizer.save_pretrained(self.tmpdirname)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any] ,    _SCREAMING_SNAKE_CASE:	Optional[int])       -> str:

				"""simple docstring"""

				__lowerCAmelCase   :						int				= "this is a test"
				__lowerCAmelCase   :						Any				= "this is a test"
				return input_text, output_text

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> List[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						Tuple				= "<pad>"
				__lowerCAmelCase   :						List[str]				= 0

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> List[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						Dict				= list(self.get_tokenizer().get_vocab().keys())

				self.assertEqual(vocab_keys[0] ,    "<pad>")
				self.assertEqual(vocab_keys[1] ,    "<unk>")
				self.assertEqual(vocab_keys[-1] ,    "▁eloquent")
				self.assertEqual(len(_SCREAMING_SNAKE_CASE) ,    3_0000)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> str:

				"""simple docstring"""

				self.assertEqual(self.get_tokenizer().vocab_size ,    3_0000)

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

				"""simple docstring"""

				if not self.test_rust_tokenizer:
							return

				__lowerCAmelCase   :						List[str]				= self.get_tokenizer()
				__lowerCAmelCase   :						Dict				= self.get_rust_tokenizer()

				__lowerCAmelCase   :						Optional[int]				= "I was born in 92000, and this is falsé."

				__lowerCAmelCase   :						Tuple				= tokenizer.tokenize(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						List[str]				= rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE)
				self.assertListEqual(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						List[Any]				= tokenizer.encode(_SCREAMING_SNAKE_CASE ,    add_special_tokens=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Any				= rust_tokenizer.encode(_SCREAMING_SNAKE_CASE ,    add_special_tokens=_SCREAMING_SNAKE_CASE)
				self.assertListEqual(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						List[str]				= self.get_rust_tokenizer()
				__lowerCAmelCase   :						str				= tokenizer.encode(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Dict				= rust_tokenizer.encode(_SCREAMING_SNAKE_CASE)
				self.assertListEqual(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> int:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[int]				= AlbertTokenizer(_SCREAMING_SNAKE_CASE ,    keep_accents=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						List[str]				= tokenizer.tokenize("This is a test")
				self.assertListEqual(_SCREAMING_SNAKE_CASE ,    ["▁this", "▁is", "▁a", "▁test"])

				self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE) ,    [48, 25, 21, 1289])

				__lowerCAmelCase   :						Union[str, Any]				= tokenizer.tokenize("I was born in 92000, and this is falsé.")
				self.assertListEqual(
				    _SCREAMING_SNAKE_CASE ,    ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."])
				__lowerCAmelCase   :						Dict				= tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE)
				self.assertListEqual(_SCREAMING_SNAKE_CASE ,    [31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9])

				__lowerCAmelCase   :						Any				= tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE)
				self.assertListEqual(
				    _SCREAMING_SNAKE_CASE ,    ["▁i", "▁was", "▁born", "▁in", "▁9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."] ,    )

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> str:

				"""simple docstring"""

				__lowerCAmelCase   :						int				= AlbertTokenizer(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Dict				= tokenizer.encode("sequence builders")
				__lowerCAmelCase   :						List[Any]				= tokenizer.encode("multi-sequence build")

				__lowerCAmelCase   :						Union[str, Any]				= tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						List[str]				= tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

				assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
				assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
				    tokenizer.sep_token_id
				]







	@slow
	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> List[str]:

				"""simple docstring"""

				__lowerCAmelCase   :						Tuple				= {"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, 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], [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]], "input_ids": [[2, 2_1970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 1_2051, 18, 17, 7103, 2153, 673, 8, 3515, 1_8684, 8, 4461, 6, 1927, 297, 8, 1_2060, 2607, 18, 13, 5, 4461, 15, 1_0538, 38, 8, 135, 15, 822, 58, 15, 993, 1_0363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 1_0641, 6, 29, 84, 2512, 2430, 782, 1_8684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 1_1712, 15, 7103, 2153, 673, 17, 2_4883, 9990, 9, 3], [2, 1_1502, 25, 1006, 20, 782, 8, 1_1809, 855, 1732, 1_9393, 1_8667, 37, 367, 2_1018, 69, 1854, 34, 1_1860, 1_9124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 3, 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], [2, 14, 2231, 886, 2385, 1_7659, 84, 14, 1_6792, 1952, 9, 3, 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]], "token_type_ids": [[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, 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, 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, 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=_SCREAMING_SNAKE_CASE ,    model_name="albert-base-v2" ,    revision="6b6560eaf5ff2e250b00c50f380c5389a9c2d82e" ,    )