



'''simple docstring'''




import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
							from transformers.models.rag.configuration_rag import RagConfig
							from transformers.models.rag.tokenization_rag import RagTokenizer

@require_faiss
@require_torch
class     UpperCamelCase_				(							__magic_name__    ):





	def 				_lowercase(						self					)    ->    Dict:
								UpperCAmelCase  :	int							  =	tempfile.mkdtemp()
								UpperCAmelCase  :	Union[str, Any]							  =	8

								# DPR tok
								UpperCAmelCase  :	Optional[Any]							  =	[
								    """[UNK]""",
								    """[CLS]""",
								    """[SEP]""",
								    """[PAD]""",
								    """[MASK]""",
								    """want""",
								    """##want""",
								    """##ed""",
								    """wa""",
								    """un""",
								    """runn""",
								    """##ing""",
								    """,""",
								    """low""",
								    """lowest""",
								]
								UpperCAmelCase  :	Optional[Any]							  =	os.path.join(self.tmpdirname	,	"""dpr_tokenizer"""					)
								os.makedirs(A	,	exist_ok=A					)
								UpperCAmelCase  :	str							  =	os.path.join(A	,	DPR_VOCAB_FILES_NAMES["""vocab_file"""]					)
								with open(self.vocab_file	,	"""w"""	,	encoding="""utf-8"""					) as vocab_writer:
															vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]					)					)

								# BART tok
								UpperCAmelCase  :	Any							  =	[
								    """l""",
								    """o""",
								    """w""",
								    """e""",
								    """r""",
								    """s""",
								    """t""",
								    """i""",
								    """d""",
								    """n""",
								    """\u0120""",
								    """\u0120l""",
								    """\u0120n""",
								    """\u0120lo""",
								    """\u0120low""",
								    """er""",
								    """\u0120lowest""",
								    """\u0120newer""",
								    """\u0120wider""",
								    """<unk>""",
								]
								UpperCAmelCase  :	Optional[Any]							  =	dict(zip(A	,	range(len(A					)					)					)					)
								UpperCAmelCase  :	Optional[Any]							  =	["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
								UpperCAmelCase  :	Optional[Any]							  =	{"""unk_token""": """<unk>"""}

								UpperCAmelCase  :	List[str]							  =	os.path.join(self.tmpdirname	,	"""bart_tokenizer"""					)
								os.makedirs(A	,	exist_ok=A					)
								UpperCAmelCase  :	Tuple							  =	os.path.join(A	,	BART_VOCAB_FILES_NAMES["""vocab_file"""]					)
								UpperCAmelCase  :	Optional[Any]							  =	os.path.join(A	,	BART_VOCAB_FILES_NAMES["""merges_file"""]					)
								with open(self.vocab_file	,	"""w"""	,	encoding="""utf-8"""					) as fp:
															fp.write(json.dumps(A					) + """\n"""					)
								with open(self.merges_file	,	"""w"""	,	encoding="""utf-8"""					) as fp:
															fp.write("""\n""".join(A					)					)





	def 				_lowercase(						self					)    ->    DPRQuestionEncoderTokenizer:
								return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname	,	"""dpr_tokenizer"""					)					)





	def 				_lowercase(						self					)    ->    BartTokenizer:
								return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname	,	"""bart_tokenizer"""					)					)





	def 				_lowercase(						self					)    ->    Dict:
								shutil.rmtree(self.tmpdirname					)





	@require_tokenizers
	def 				_lowercase(						self					)    ->    Optional[Any]:
								UpperCAmelCase  :	Optional[Any]							  =	os.path.join(self.tmpdirname	,	"""rag_tokenizer"""					)
								UpperCAmelCase  :	List[Any]							  =	RagConfig(question_encoder=DPRConfig().to_dict()	,	generator=BartConfig().to_dict()					)
								UpperCAmelCase  :	List[Any]							  =	RagTokenizer(question_encoder=self.get_dpr_tokenizer()	,	generator=self.get_bart_tokenizer()					)
								rag_config.save_pretrained(A					)
								rag_tokenizer.save_pretrained(A					)
								UpperCAmelCase  :	Optional[int]							  =	RagTokenizer.from_pretrained(A	,	config=A					)
								self.assertIsInstance(new_rag_tokenizer.question_encoder	,	A					)
								self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()	,	rag_tokenizer.question_encoder.get_vocab()					)
								self.assertIsInstance(new_rag_tokenizer.generator	,	A					)
								self.assertEqual(new_rag_tokenizer.generator.get_vocab()	,	rag_tokenizer.generator.get_vocab()					)





	@slow
	def 				_lowercase(						self					)    ->    str:
								UpperCAmelCase  :	Optional[Any]							  =	RagTokenizer.from_pretrained("""facebook/rag-token-nq"""					)
								UpperCAmelCase  :	str							  =	[
								    """who got the first nobel prize in physics""",
								    """when is the next deadpool movie being released""",
								    """which mode is used for short wave broadcast service""",
								    """who is the owner of reading football club""",
								    """when is the next scandal episode coming out""",
								    """when is the last time the philadelphia won the superbowl""",
								    """what is the most current adobe flash player version""",
								    """how many episodes are there in dragon ball z""",
								    """what is the first step in the evolution of the eye""",
								    """where is gall bladder situated in human body""",
								    """what is the main mineral in lithium batteries""",
								    """who is the president of usa right now""",
								    """where do the greasers live in the outsiders""",
								    """panda is a national animal of which country""",
								    """what is the name of manchester united stadium""",
								]
								UpperCAmelCase  :	List[str]							  =	tokenizer(A					)
								self.assertIsNotNone(A					)






	@slow
	def 				_lowercase(						self					)    ->    List[str]:
								UpperCAmelCase  :	Union[str, Any]							  =	RagTokenizer.from_pretrained("""facebook/rag-sequence-nq"""					)
								UpperCAmelCase  :	List[str]							  =	[
								    """who got the first nobel prize in physics""",
								    """when is the next deadpool movie being released""",
								    """which mode is used for short wave broadcast service""",
								    """who is the owner of reading football club""",
								    """when is the next scandal episode coming out""",
								    """when is the last time the philadelphia won the superbowl""",
								    """what is the most current adobe flash player version""",
								    """how many episodes are there in dragon ball z""",
								    """what is the first step in the evolution of the eye""",
								    """where is gall bladder situated in human body""",
								    """what is the main mineral in lithium batteries""",
								    """who is the president of usa right now""",
								    """where do the greasers live in the outsiders""",
								    """panda is a national animal of which country""",
								    """what is the name of manchester united stadium""",
								]
								UpperCAmelCase  :	int							  =	tokenizer(A					)
								self.assertIsNotNone(A					)






'''simple docstring'''




from scipy.stats import pearsonr

import datasets


a     :    str      						=					"""
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.
"""


a     :    Dict      						=					"""
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
"""


a     :    Optional[int]      						=					"""
@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     UpperCamelCase_				(							datasets.Metric    ):





	def 				_lowercase(						self					)    ->    Optional[int]:
								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 				_lowercase(						self	,	A	,	A	,	A=False					)    ->    int:
								if return_pvalue:
															UpperCAmelCase  :	int							  =	pearsonr(A	,	A					)
															return {"pearsonr": results[0], "p-value": results[1]}
								else:
															return {"pearsonr": float(pearsonr(A	,	A					)[0]					)}

