


"""simple docstring"""


import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A     :   str			    =			logging.get_logger(__name__)
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    str				      =["""input_ids""", """attention_mask"""]

							def __init__(     self    ,   __a="</s>"    ,   __a="<unk>"    ,   __a="<pad>"    ,   __a=1_25    ,   __a=None    ,   **__a    ,   ):
										# Add extra_ids to the special token list
										if extra_ids > 0 and additional_special_tokens is None:
													__lowerCAmelCase				    =			[f"<extra_id_{i}>" for i in range(__a	)]
										elif extra_ids > 0 and additional_special_tokens is not None:
													# Check that we have the right number of extra_id special tokens
													__lowerCAmelCase				    =			len(set(filter(lambda __a	: bool("extra_id" in str(__a	)	)    ,   __a	)	)	)
													if extra_tokens != extra_ids:
																raise ValueError(
																    f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
																    " provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"
																    " extra_ids tokens"	)

										__lowerCAmelCase				    =			AddedToken(__a    ,   lstrip=__a    ,   rstrip=__a	) if isinstance(__a    ,   __a	) else pad_token
										__lowerCAmelCase				    =			AddedToken(__a    ,   lstrip=__a    ,   rstrip=__a	) if isinstance(__a    ,   __a	) else eos_token
										__lowerCAmelCase				    =			AddedToken(__a    ,   lstrip=__a    ,   rstrip=__a	) if isinstance(__a    ,   __a	) else unk_token

										super().__init__(
										    eos_token=__a    ,   unk_token=__a    ,   pad_token=__a    ,   extra_ids=__a    ,   additional_special_tokens=__a    ,   **__a    ,   )

										__lowerCAmelCase				    =			extra_ids

										__lowerCAmelCase				    =			2**8  # utf is 8 bits

										# define special tokens dict
										__lowerCAmelCase				    =			{
										    self.pad_token: 0,
										    self.eos_token: 1,
										    self.unk_token: 2,
										}
										__lowerCAmelCase				    =			len(self.special_tokens_encoder	)
										__lowerCAmelCase				    =			len(__a	)
										for i, token in enumerate(__a	):
													__lowerCAmelCase				    =			self.vocab_size + i - n
										__lowerCAmelCase				    =			{v: k for k, v in self.special_tokens_encoder.items()}

							@property
							def 			snake_case				(     self	):
										return self._utf_vocab_size + self._num_special_tokens + self._extra_ids

							def 			snake_case				(     self    ,   __a    ,   __a = None    ,   __a = False	):
										if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=__a    ,   token_ids_a=__a    ,   already_has_special_tokens=__a	)

										# normal case: some special tokens
										if token_ids_a is None:
													return ([0] * len(__a	)) + [1]
										return ([0] * len(__a	)) + [1] + ([0] * len(__a	)) + [1]

							def 			snake_case				(     self    ,   __a	):
										if len(__a	) > 0 and token_ids[-1] == self.eos_token_id:
													warnings.warn(
													    f"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
													    " eos tokens being added."	)
													return token_ids
										else:
													return token_ids + [self.eos_token_id]

							def 			snake_case				(     self    ,   __a    ,   __a = None	):
										__lowerCAmelCase				    =			[self.eos_token_id]

										if token_ids_a is None:
													return len(token_ids_a + eos	) * [0]
										return len(token_ids_a + eos + token_ids_a + eos	) * [0]

							def 			snake_case				(     self    ,   __a    ,   __a = None	):
										__lowerCAmelCase				    =			self._add_eos_if_not_present(__a	)
										if token_ids_a is None:
													return token_ids_a
										else:
													__lowerCAmelCase				    =			self._add_eos_if_not_present(__a	)
													return token_ids_a + token_ids_a

							def 			snake_case				(     self    ,   __a	):
										__lowerCAmelCase				    =			[chr(__a	) for i in text.encode("utf-8"	)]
										return tokens

							def 			snake_case				(     self    ,   __a	):
										if token in self.special_tokens_encoder:
													__lowerCAmelCase				    =			self.special_tokens_encoder[token]
										elif token in self.added_tokens_encoder:
													__lowerCAmelCase				    =			self.added_tokens_encoder[token]
										elif len(__a	) != 1:
													__lowerCAmelCase				    =			self.unk_token_id
										else:
													__lowerCAmelCase				    =			ord(__a	) + self._num_special_tokens
										return token_id

							def 			snake_case				(     self    ,   __a	):
										if index in self.special_tokens_decoder:
													__lowerCAmelCase				    =			self.special_tokens_decoder[index]
										else:
													__lowerCAmelCase				    =			chr(index - self._num_special_tokens	)
										return token

							def 			snake_case				(     self    ,   __a	):
										__lowerCAmelCase				    =			B""
										for token in tokens:
													if token in self.special_tokens_decoder:
																__lowerCAmelCase				    =			self.special_tokens_decoder[token].encode("utf-8"	)
													elif token in self.added_tokens_decoder:
																__lowerCAmelCase				    =			self.special_tokens_decoder[token].encode("utf-8"	)
													elif token in self.special_tokens_encoder:
																__lowerCAmelCase				    =			token.encode("utf-8"	)
													elif token in self.added_tokens_encoder:
																__lowerCAmelCase				    =			token.encode("utf-8"	)
													else:
																__lowerCAmelCase				    =			bytes([ord(__a	)]	)
													bstring += tok_string
										__lowerCAmelCase				    =			bstring.decode("utf-8"    ,   errors="ignore"	)
										return string



							def 			snake_case				(     self    ,   __a    ,   __a = None	):
										return ()








'''simple docstring'''
from scipy.stats import pearsonr

import datasets


__snake_case							      =     '''
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.
'''


__snake_case							      =     '''
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
'''


__snake_case							      =     '''
@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    lowercase	(							datasets.Metric					):

		"""simple docstring"""

		def     lowerCAmelCase__				(		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.pearsonr.html''']    ,   )




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_=False							):

							'''simple docstring'''


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