


import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 			a       (	__lowerCAmelCase			,						unittest.TestCase ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				Optional[int]							     =     BarthezTokenizer
     SCREAMING_SNAKE_CASE :				Optional[int]							     =     BarthezTokenizerFast
     SCREAMING_SNAKE_CASE :				str							     =     True
     SCREAMING_SNAKE_CASE :				Optional[int]							     =     True

     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							str:
          super().setUp()

          __UpperCAmelCase			:				Optional[Any]													=    BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''    )
          tokenizer.save_pretrained(self.tmpdirname    )
          tokenizer.save_pretrained(self.tmpdirname							,				legacy_format=lowerCamelCase__    )
          __UpperCAmelCase			:				int													=    tokenizer

     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							str:
          __UpperCAmelCase			:				Optional[int]													=    '''<pad>'''
          __UpperCAmelCase			:				List[str]													=    1

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

     def 				lowerCamelCase__				(       self  :	int    )		->							Tuple:
          __UpperCAmelCase			:				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(lowerCamelCase__    )							,				10_1122    )

     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							str:
          self.assertEqual(self.get_tokenizer().vocab_size							,				10_1122    )

     @require_torch
     def 				lowerCamelCase__				(       self  :	Dict    )		->							Tuple:
          __UpperCAmelCase			:				Dict													=    ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          __UpperCAmelCase			:				Any													=    [0, 57, 3018, 7_0307, 91, 2]

          __UpperCAmelCase			:				Optional[int]													=    self.tokenizer(
              lowerCamelCase__							,				max_length=len(lowerCamelCase__    )							,				padding=lowerCamelCase__							,				truncation=lowerCamelCase__							,				return_tensors='''pt'''    )
          self.assertIsInstance(lowerCamelCase__							,				lowerCamelCase__    )

          self.assertEqual((2, 6)							,				batch.input_ids.shape    )
          self.assertEqual((2, 6)							,				batch.attention_mask.shape    )
          __UpperCAmelCase			:				List[Any]													=    batch.input_ids.tolist()[0]
          self.assertListEqual(lowerCamelCase__							,				lowerCamelCase__    )

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






          if not self.test_rust_tokenizer:
               return

          __UpperCAmelCase			:				List[Any]													=    self.get_tokenizer()
          __UpperCAmelCase			:				Optional[int]													=    self.get_rust_tokenizer()

          __UpperCAmelCase			:				Any													=    '''I was born in 92000, and this is falsé.'''

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

          __UpperCAmelCase			:				Tuple													=    tokenizer.encode(lowerCamelCase__							,				add_special_tokens=lowerCamelCase__    )
          __UpperCAmelCase			:				str													=    rust_tokenizer.encode(lowerCamelCase__							,				add_special_tokens=lowerCamelCase__    )
          self.assertListEqual(lowerCamelCase__							,				lowerCamelCase__    )

          __UpperCAmelCase			:				Tuple													=    self.get_rust_tokenizer()
          __UpperCAmelCase			:				Optional[int]													=    tokenizer.encode(lowerCamelCase__    )
          __UpperCAmelCase			:				str													=    rust_tokenizer.encode(lowerCamelCase__    )
          self.assertListEqual(lowerCamelCase__							,				lowerCamelCase__    )






     @slow
     def 				lowerCamelCase__				(       self  :	List[str]    )		->							Dict:
          __UpperCAmelCase			:				Optional[int]													=    {'''input_ids''': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 173, 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], [0, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 5, 2]], '''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, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
          # fmt: on

          # moussaKam/mbarthez is a french model. So we also use french texts.
          __UpperCAmelCase			:				int													=    [
              '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
              '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
              '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
              '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
              '''telles que la traduction et la synthèse de texte.''',
          ]

          self.tokenizer_integration_test_util(
              expected_encoding=lowerCamelCase__							,				model_name='''moussaKam/mbarthez'''							,				revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''							,				sequences=lowerCamelCase__							,				)



'''simple docstring'''



from scipy.stats import pearsonr

import datasets


__UpperCAmelCase    :Tuple				       =     "\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"


__UpperCAmelCase    :List[str]				       =     "\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"


__UpperCAmelCase    :List[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 				lowerCamelCase__				(       self  :	List[Any]    )		->							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 				lowerCamelCase__				(       self  :	Any							,				snake_case  :	Union[str, Any]							,				snake_case  :	int							,				snake_case  :	Dict=False    )		->							List[Any]:
          if return_pvalue:
               __UpperCAmelCase			:				Tuple													=    pearsonr(snake_case							,				snake_case    )
               return {"pearsonr": results[0], "p-value": results[1]}
          else:
               return {"pearsonr": float(pearsonr(snake_case							,				snake_case    )[0]    )}