
"""simple docstring"""
import json
import os
import unittest

from transformers.models.roc_bert.tokenization_roc_bert import (
    VOCAB_FILES_NAMES,
    RoCBertBasicTokenizer,
    RoCBertTokenizer,
    RoCBertWordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class       lowerCAmelCase_		(				A__				,				unittest.TestCase ):



							'''simple docstring'''





							_snake_case						      =		RoCBertTokenizer
							_snake_case						      =		None
							_snake_case						      =		False
							_snake_case						      =		True
							_snake_case						      =		filter_non_english




							def    A__						( self				)       ->  str:
													super().setUp()

													__lowerCAmelCase     =  ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """你""", """好""", """是""", """谁""", """a""", """b""", """c""", """d"""]
													__lowerCAmelCase     =  {}
													__lowerCAmelCase     =  {}
													for i, value in enumerate(snake_case_				):
																			__lowerCAmelCase     =  i
																			__lowerCAmelCase     =  i
													__lowerCAmelCase     =  os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES["""vocab_file"""]				)
													__lowerCAmelCase     =  os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES["""word_shape_file"""]				)
													__lowerCAmelCase     =  os.path.join(self.tmpdirname    ,				VOCAB_FILES_NAMES["""word_pronunciation_file"""]				)
													with open(self.vocab_file    ,				"""w"""    ,				encoding="""utf-8"""				) as vocab_writer:
																			vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]				)				)
													with open(self.word_shape_file    ,				"""w"""    ,				encoding="""utf-8"""				) as word_shape_writer:
																			json.dump(snake_case_    ,				snake_case_    ,				ensure_ascii=snake_case_				)
													with open(self.word_pronunciation_file    ,				"""w"""    ,				encoding="""utf-8"""				) as word_pronunciation_writer:
																			json.dump(snake_case_    ,				snake_case_    ,				ensure_ascii=snake_case_				)




							def    A__						( self				)       ->  Optional[int]:
													__lowerCAmelCase     =  self.tokenizer_class(self.vocab_file    ,				self.word_shape_file    ,				self.word_pronunciation_file				)

													__lowerCAmelCase     =  tokenizer.tokenize("""你好[SEP]你是谁"""				)
													self.assertListEqual(snake_case_    ,				["""你""", """好""", """[SEP]""", """你""", """是""", """谁"""]				)
													self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_				)    ,				[5, 6, 2, 5, 7, 8]				)
													self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(snake_case_				)    ,				[5, 6, 2, 5, 7, 8]				)
													self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(snake_case_				)    ,				[5, 6, 2, 5, 7, 8]				)




							def    A__						( self				)       ->  Union[str, Any]:
													__lowerCAmelCase     =  RoCBertBasicTokenizer()

													self.assertListEqual(tokenizer.tokenize("""ah\u535A\u63A8zz"""				)    ,				["""ah""", """\u535A""", """\u63A8""", """zz"""]				)




							def    A__						( self				)       ->  Dict:
													__lowerCAmelCase     =  RoCBertBasicTokenizer(do_lower_case=snake_case_				)

													self.assertListEqual(
													    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """				)    ,				["""hello""", """!""", """how""", """are""", """you""", """?"""]				)
													self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""				)    ,				["""hello"""]				)




							def    A__						( self				)       ->  Optional[Any]:
													__lowerCAmelCase     =  RoCBertBasicTokenizer(do_lower_case=snake_case_    ,				strip_accents=snake_case_				)

													self.assertListEqual(
													    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """				)    ,				["""hällo""", """!""", """how""", """are""", """you""", """?"""]				)
													self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""				)    ,				["""h\u00E9llo"""]				)




							def    A__						( self				)       ->  List[str]:
													__lowerCAmelCase     =  RoCBertBasicTokenizer(do_lower_case=snake_case_    ,				strip_accents=snake_case_				)

													self.assertListEqual(
													    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """				)    ,				["""hallo""", """!""", """how""", """are""", """you""", """?"""]				)
													self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""				)    ,				["""hello"""]				)




							def    A__						( self				)       ->  Union[str, Any]:
													__lowerCAmelCase     =  RoCBertBasicTokenizer(do_lower_case=snake_case_				)

													self.assertListEqual(
													    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """				)    ,				["""hallo""", """!""", """how""", """are""", """you""", """?"""]				)
													self.assertListEqual(tokenizer.tokenize("""H\u00E9llo"""				)    ,				["""hello"""]				)




							def    A__						( self				)       ->  List[str]:
													__lowerCAmelCase     =  RoCBertBasicTokenizer(do_lower_case=snake_case_				)

													self.assertListEqual(
													    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU?  """				)    ,				["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""]				)




							def    A__						( self				)       ->  Optional[Any]:
													__lowerCAmelCase     =  RoCBertBasicTokenizer(do_lower_case=snake_case_    ,				strip_accents=snake_case_				)

													self.assertListEqual(
													    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """				)    ,				["""HäLLo""", """!""", """how""", """Are""", """yoU""", """?"""]				)




							def    A__						( self				)       ->  int:
													__lowerCAmelCase     =  RoCBertBasicTokenizer(do_lower_case=snake_case_    ,				strip_accents=snake_case_				)

													self.assertListEqual(
													    tokenizer.tokenize(""" \tHäLLo!how  \n Are yoU?  """				)    ,				["""HaLLo""", """!""", """how""", """Are""", """yoU""", """?"""]				)




							def    A__						( self				)       ->  str:
													__lowerCAmelCase     =  RoCBertBasicTokenizer(do_lower_case=snake_case_    ,				never_split=["""[UNK]"""]				)

													self.assertListEqual(
													    tokenizer.tokenize(""" \tHeLLo!how  \n Are yoU? [UNK]"""				)    ,				["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?""", """[UNK]"""]				)




							def    A__						( self				)       ->  List[str]:
													__lowerCAmelCase     =  ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

													__lowerCAmelCase     =  {}
													for i, token in enumerate(snake_case_				):
																			__lowerCAmelCase     =  i
													__lowerCAmelCase     =  RoCBertWordpieceTokenizer(vocab=snake_case_    ,				unk_token="""[UNK]"""				)

													self.assertListEqual(tokenizer.tokenize(""""""				)    ,				[]				)

													self.assertListEqual(tokenizer.tokenize("""unwanted running"""				)    ,				["""un""", """##want""", """##ed""", """runn""", """##ing"""]				)

													self.assertListEqual(tokenizer.tokenize("""unwantedX running"""				)    ,				["""[UNK]""", """runn""", """##ing"""]				)




							def    A__						( self				)       ->  Tuple:
													self.assertTrue(_is_whitespace(""" """				)				)
													self.assertTrue(_is_whitespace("""\t"""				)				)
													self.assertTrue(_is_whitespace("""\r"""				)				)
													self.assertTrue(_is_whitespace("""\n"""				)				)
													self.assertTrue(_is_whitespace("""\u00A0"""				)				)

													self.assertFalse(_is_whitespace("""A"""				)				)
													self.assertFalse(_is_whitespace("""-"""				)				)




							def    A__						( self				)       ->  List[str]:
													self.assertTrue(_is_control("""\u0005"""				)				)

													self.assertFalse(_is_control("""A"""				)				)
													self.assertFalse(_is_control(""" """				)				)
													self.assertFalse(_is_control("""\t"""				)				)
													self.assertFalse(_is_control("""\r"""				)				)




							def    A__						( self				)       ->  List[str]:
													self.assertTrue(_is_punctuation("""-"""				)				)
													self.assertTrue(_is_punctuation("""$"""				)				)
													self.assertTrue(_is_punctuation("""`"""				)				)
													self.assertTrue(_is_punctuation("""."""				)				)

													self.assertFalse(_is_punctuation("""A"""				)				)
													self.assertFalse(_is_punctuation(""" """				)				)




							def    A__						( self				)       ->  str:
													__lowerCAmelCase     =  self.get_tokenizer()

													# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
													self.assertListEqual([tokenizer.tokenize(snake_case_				) for t in ["""Test""", """\xad""", """test"""]]    ,				[["""[UNK]"""], [], ["""[UNK]"""]]				)

													if self.test_rust_tokenizer:
																			__lowerCAmelCase     =  self.get_rust_tokenizer()
																			self.assertListEqual(
																			    [rust_tokenizer.tokenize(snake_case_				) for t in ["""Test""", """\xad""", """test"""]]    ,				[["""[UNK]"""], [], ["""[UNK]"""]]				)




							def    A__						( self				)       ->  Optional[int]:
													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
																									__lowerCAmelCase     =  self.rust_tokenizer_class.from_pretrained(snake_case_    ,				**snake_case_				)

																									__lowerCAmelCase     =  f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
																									__lowerCAmelCase     =  tokenizer_r.encode_plus(
																									    snake_case_    ,				return_attention_mask=snake_case_    ,				return_token_type_ids=snake_case_    ,				return_offsets_mapping=snake_case_    ,				add_special_tokens=snake_case_    ,				)

																									__lowerCAmelCase     =  tokenizer_r.do_lower_case if hasattr(snake_case_    ,				"""do_lower_case"""				) else False
																									__lowerCAmelCase     =  (
																									    [
																									        ((0, 0), tokenizer_r.cls_token),
																									        ((0, 1), """A"""),
																									        ((1, 2), ""","""),
																									        ((3, 5), """na"""),
																									        ((5, 6), """##ï"""),
																									        ((6, 8), """##ve"""),
																									        ((9, 15), tokenizer_r.mask_token),
																									        ((16, 21), """Allen"""),
																									        ((21, 23), """##NL"""),
																									        ((23, 24), """##P"""),
																									        ((25, 33), """sentence"""),
																									        ((33, 34), """."""),
																									        ((0, 0), tokenizer_r.sep_token),
																									    ]
																									    if not do_lower_case
																									    else [
																									        ((0, 0), tokenizer_r.cls_token),
																									        ((0, 1), """a"""),
																									        ((1, 2), ""","""),
																									        ((3, 8), """naive"""),
																									        ((9, 15), tokenizer_r.mask_token),
																									        ((16, 21), """allen"""),
																									        ((21, 23), """##nl"""),
																									        ((23, 24), """##p"""),
																									        ((25, 33), """sentence"""),
																									        ((33, 34), """."""),
																									        ((0, 0), tokenizer_r.sep_token),
																									    ]
																									)

																									self.assertEqual(
																									    [e[1] for e in expected_results]    ,				tokenizer_r.convert_ids_to_tokens(tokens["""input_ids"""]				)				)
																									self.assertEqual([e[0] for e in expected_results]    ,				tokens["""offset_mapping"""]				)




							def    A__						( self				)       ->  Optional[int]:
													__lowerCAmelCase     =  ["""的""", """人""", """有"""]
													__lowerCAmelCase     =  """""".join(snake_case_				)
													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
																									__lowerCAmelCase     =  True
																									__lowerCAmelCase     =  self.tokenizer_class.from_pretrained(snake_case_    ,				**snake_case_				)
																									__lowerCAmelCase     =  self.rust_tokenizer_class.from_pretrained(snake_case_    ,				**snake_case_				)

																									__lowerCAmelCase     =  tokenizer_p.encode(snake_case_    ,				add_special_tokens=snake_case_				)
																									__lowerCAmelCase     =  tokenizer_r.encode(snake_case_    ,				add_special_tokens=snake_case_				)

																									__lowerCAmelCase     =  tokenizer_r.convert_ids_to_tokens(snake_case_				)
																									__lowerCAmelCase     =  tokenizer_p.convert_ids_to_tokens(snake_case_				)

																									# it is expected that each Chinese character is not preceded by "##"
																									self.assertListEqual(snake_case_    ,				snake_case_				)
																									self.assertListEqual(snake_case_    ,				snake_case_				)

																									__lowerCAmelCase     =  False
																									__lowerCAmelCase     =  self.rust_tokenizer_class.from_pretrained(snake_case_    ,				**snake_case_				)
																									__lowerCAmelCase     =  self.tokenizer_class.from_pretrained(snake_case_    ,				**snake_case_				)

																									__lowerCAmelCase     =  tokenizer_r.encode(snake_case_    ,				add_special_tokens=snake_case_				)
																									__lowerCAmelCase     =  tokenizer_p.encode(snake_case_    ,				add_special_tokens=snake_case_				)

																									__lowerCAmelCase     =  tokenizer_r.convert_ids_to_tokens(snake_case_				)
																									__lowerCAmelCase     =  tokenizer_p.convert_ids_to_tokens(snake_case_				)

																									# it is expected that only the first Chinese character is not preceded by "##".
																									__lowerCAmelCase     =  [
																									    f"""##{token}""" if idx != 0 else token for idx, token in enumerate(snake_case_				)
																									]
																									self.assertListEqual(snake_case_    ,				snake_case_				)
																									self.assertListEqual(snake_case_    ,				snake_case_				)




							@slow
							def    A__						( self				)       ->  Optional[int]:
													__lowerCAmelCase     =  self.tokenizer_class(self.vocab_file    ,				self.word_shape_file    ,				self.word_pronunciation_file				)

													__lowerCAmelCase     =  tokenizer.encode("""你好"""    ,				add_special_tokens=snake_case_				)
													__lowerCAmelCase     =  tokenizer.encode("""你是谁"""    ,				add_special_tokens=snake_case_				)

													__lowerCAmelCase     =  tokenizer.build_inputs_with_special_tokens(snake_case_				)
													__lowerCAmelCase     =  tokenizer.build_inputs_with_special_tokens(snake_case_    ,				snake_case_				)

													assert encoded_sentence == [1] + text + [2]
													assert encoded_pair == [1] + text + [2] + text_a + [2]




							def    A__						( self				)       ->  Optional[int]:
													__lowerCAmelCase     =  self.get_tokenizers(do_lower_case=snake_case_				)
													for tokenizer in tokenizers:
																			with self.subTest(f"""{tokenizer.__class__.__name__}"""				):
																									__lowerCAmelCase     =  """你好，你是谁"""
																									__lowerCAmelCase     =  tokenizer.tokenize(snake_case_				)
																									__lowerCAmelCase     =  tokenizer.convert_tokens_to_ids(snake_case_				)
																									__lowerCAmelCase     =  tokenizer.convert_tokens_to_shape_ids(snake_case_				)
																									__lowerCAmelCase     =  tokenizer.convert_tokens_to_pronunciation_ids(snake_case_				)
																									__lowerCAmelCase     =  tokenizer.prepare_for_model(
																									    snake_case_    ,				snake_case_    ,				snake_case_    ,				add_special_tokens=snake_case_				)

																									__lowerCAmelCase     =  tokenizer.encode_plus(snake_case_    ,				add_special_tokens=snake_case_				)

																									self.assertEqual(snake_case_    ,				snake_case_				)


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

import datasets


SCREAMING_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.
'''


SCREAMING_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
'''


SCREAMING_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       lowerCAmelCase_		(				datasets.Metric ):



							'''simple docstring'''





							def    A__						( self				)       ->  Union[str, Any]:
													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    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_=False				)       ->  Dict:
													if return_pvalue:
																			__lowerCAmelCase     =  pearsonr(snake_case_    ,				snake_case_				)
																			return {"pearsonr": results[0], "p-value": results[1]}
													else:
																			return {"pearsonr": float(pearsonr(snake_case_    ,				snake_case_				)[0]				)}
