


'''simple docstring'''





from sklearn.metrics import matthews_corrcoef

import datasets


UpperCAmelCase_   :				List[str]						      = '\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction.  The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n'

UpperCAmelCase_   :				List[str]						      = '\nArgs:\n    predictions (list of int): Predicted labels, as returned by a model.\n    references (list of int): Ground truth labels.\n    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n    matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n    Example 1, a basic example with only predictions and references as inputs:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3])\n        >>> print(round(results[\'matthews_correlation\'], 2))\n        0.54\n\n    Example 2, the same example as above, but also including sample weights:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])\n        >>> print(round(results[\'matthews_correlation\'], 2))\n        0.1\n\n    Example 3, the same example as above, but with sample weights that cause a negative correlation:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])\n        >>> print(round(results[\'matthews_correlation\'], 2))\n        -0.25\n'

UpperCAmelCase_   :				Dict						      = '\\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,					_KWARGS_DESCRIPTION    )
class 				SCREAMING_SNAKE_CASE__						(       datasets.Metric    ):
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    List[Any]:
             return datasets.MetricInfo(
                 description=_DESCRIPTION  ,							citation=_CITATION  ,							inputs_description=_KWARGS_DESCRIPTION  ,							features=datasets.Features(
                     {
                         'predictions': datasets.Value('int32'  ),
                         'references': datasets.Value('int32'  ),
                     }  )  ,							reference_urls=[
                     'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'
                 ]  ,							)

      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  ,							SCREAMING_SNAKE_CASE__       : Dict  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : int=None  )	->    Union[str, Any]:
             return {
                 "matthews_correlation": float(matthews_corrcoef(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							sample_weight=SCREAMING_SNAKE_CASE__  )  ),
             }






import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
@require_tokenizers
class 				SCREAMING_SNAKE_CASE__						(       lowercase__		,					unittest.TestCase    ):
      snake_case__       :  List[str]       =		MvpTokenizer
      snake_case__       :  Dict       =		MvpTokenizerFast
      snake_case__       :  Any       =		True
      snake_case__       :  Optional[int]       =		filter_roberta_detectors
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Optional[int]:
             super().setUp()
             a_      :	Any							=						[
                 'l',
                 'o',
                 'w',
                 'e',
                 'r',
                 's',
                 't',
                 'i',
                 'd',
                 'n',
                 '\u0120',
                 '\u0120l',
                 '\u0120n',
                 '\u0120lo',
                 '\u0120low',
                 'er',
                 '\u0120lowest',
                 '\u0120newer',
                 '\u0120wider',
                 '<unk>',
             ]
             a_      :	int							=						dict(zip(SCREAMING_SNAKE_CASE__  ,							range(len(SCREAMING_SNAKE_CASE__  )  )  )  )
             a_      :	int							=						['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
             a_      :	str							=						{'unk_token': '<unk>'}

             a_      :	Tuple							=						os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES['vocab_file']  )
             a_      :	Optional[int]							=						os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES['merges_file']  )
             with open(self.vocab_file  ,							'w'  ,							encoding='utf-8'  ) as fp:
                    fp.write(json.dumps(SCREAMING_SNAKE_CASE__  ) + '\n'  )
             with open(self.merges_file  ,							'w'  ,							encoding='utf-8'  ) as fp:
                    fp.write('\n'.join(SCREAMING_SNAKE_CASE__  )  )
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  ,							**SCREAMING_SNAKE_CASE__       : str  )	->    List[Any]:
             kwargs.update(self.special_tokens_map  )
             return self.tokenizer_class.from_pretrained(self.tmpdirname  ,							**SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Any  ,							**SCREAMING_SNAKE_CASE__       : Optional[Any]  )	->    List[str]:
             kwargs.update(self.special_tokens_map  )
             return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,							**SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Any  )	->    Union[str, Any]:
             return "lower newer", "lower newer"
      @cached_property
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    str:
             return MvpTokenizer.from_pretrained('RUCAIBox/mvp'  )
      @cached_property
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Optional[int]:
             return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'  )
      @require_torch
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Union[str, Any]:
             a_      :	Union[str, Any]							=						['A long paragraph for summarization.', 'Another paragraph for summarization.']
             a_      :	List[Any]							=						[0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    a_      :	List[str]							=						tokenizer(SCREAMING_SNAKE_CASE__  ,							max_length=len(SCREAMING_SNAKE_CASE__  )  ,							padding=SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  )
                    self.assertIsInstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

                    self.assertEqual((2, 9)  ,							batch.input_ids.shape  )
                    self.assertEqual((2, 9)  ,							batch.attention_mask.shape  )
                    a_      :	Optional[Any]							=						batch.input_ids.tolist()[0]
                    self.assertListEqual(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                    # Test that special tokens are reset
      @require_torch
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Optional[Any]:
             a_      :	Any							=						['A long paragraph for summarization.', 'Another paragraph for summarization.']
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    a_      :	Optional[Any]							=						tokenizer(SCREAMING_SNAKE_CASE__  ,							padding=SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  )
                    # check if input_ids are returned and no labels
                    self.assertIn('input_ids'  ,							SCREAMING_SNAKE_CASE__  )
                    self.assertIn('attention_mask'  ,							SCREAMING_SNAKE_CASE__  )
                    self.assertNotIn('labels'  ,							SCREAMING_SNAKE_CASE__  )
                    self.assertNotIn('decoder_attention_mask'  ,							SCREAMING_SNAKE_CASE__  )
      @require_torch
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    int:
             a_      :	Union[str, Any]							=						[
                 'Summary of the text.',
                 'Another summary.',
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    a_      :	List[str]							=						tokenizer(text_target=SCREAMING_SNAKE_CASE__  ,							max_length=3_2  ,							padding='max_length'  ,							return_tensors='pt'  )
                    self.assertEqual(3_2  ,							targets['input_ids'].shape[1]  )
      @require_torch
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    List[str]:
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    a_      :	Dict							=						tokenizer(
                        ['I am a small frog' * 1_0_2_4, 'I am a small frog']  ,							padding=SCREAMING_SNAKE_CASE__  ,							truncation=SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  )
                    self.assertIsInstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                    self.assertEqual(batch.input_ids.shape  ,							(2, 1_0_2_4)  )
      @require_torch
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Tuple:
             a_      :	Dict							=						['A long paragraph for summarization.']
             a_      :	Dict							=						[
                 'Summary of the text.',
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    a_      :	Union[str, Any]							=						tokenizer(SCREAMING_SNAKE_CASE__  ,							text_target=SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  )
                    a_      :	Dict							=						inputs['input_ids']
                    a_      :	str							=						inputs['labels']
                    self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
                    self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
                    self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
                    self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    Union[str, Any]:
             pass

      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    int:
             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
                           a_      :	List[str]							=						self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )
                           a_      :	Tuple							=						self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )
                           a_      :	int							=						'A, <mask> AllenNLP sentence.'
                           a_      :	Optional[Any]							=						tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE__  ,							add_special_tokens=SCREAMING_SNAKE_CASE__  ,							return_token_type_ids=SCREAMING_SNAKE_CASE__  )
                           a_      :	int							=						tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE__  ,							add_special_tokens=SCREAMING_SNAKE_CASE__  ,							return_token_type_ids=SCREAMING_SNAKE_CASE__  )

                           # token_type_ids should put 0 everywhere
                           self.assertEqual(sum(tokens_r['token_type_ids']  )  ,							sum(tokens_p['token_type_ids']  )  )

                           # attention_mask should put 1 everywhere, so sum over length should be 1
                           self.assertEqual(
                               sum(tokens_r['attention_mask']  ) / len(tokens_r['attention_mask']  )  ,							sum(tokens_p['attention_mask']  ) / len(tokens_p['attention_mask']  )  ,							)

                           a_      :	str							=						tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']  )
                           a_      :	Any							=						tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']  )

                           # Rust correctly handles the space before the mask while python doesnt
                           self.assertSequenceEqual(tokens_p['input_ids']  ,							[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]  )
                           self.assertSequenceEqual(tokens_r['input_ids']  ,							[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]  )

                           self.assertSequenceEqual(
                               SCREAMING_SNAKE_CASE__  ,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']  )
                           self.assertSequenceEqual(
                               SCREAMING_SNAKE_CASE__  ,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']  )


