


from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


UpperCAmelCase_   :				List[Any]						      = '\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n'

UpperCAmelCase_   :				Dict						      = '\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n'

UpperCAmelCase_   :				int						      = '\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    "accuracy": Accuracy\n    "f1": F1 score\n    "pearson": Pearson Correlation\n    "spearmanr": Spearman Correlation\n    "matthews_correlation": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\')  # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\')  # \'mrpc\' or \'qqp\'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})\n    {\'pearson\': 1.0, \'spearmanr\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'matthews_correlation\': 1.0}\n'







def  SCREAMING_SNAKE_CASE_		(    __A				:    List[Any]						,					__A				:    Optional[int]       )			->	int:

       """simple docstring"""

       return float((preds == labels).mean()       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    Tuple						,					__A				:    Optional[int]       )			->	Dict:

       """simple docstring"""

       a_      :	Any							=						simple_accuracy(__A						,					__A       )
       a_      :	List[str]							=						float(fa_score(y_true=__A						,					y_pred=__A       )       )
       return {
           "accuracy": acc,
           "f1": fa,
       }







def  SCREAMING_SNAKE_CASE_		(    __A				:    Dict						,					__A				:    Optional[Any]       )			->	List[str]:

       """simple docstring"""

       a_      :	Optional[int]							=						float(pearsonr(__A						,					__A       )[0]       )
       a_      :	Optional[int]							=						float(spearmanr(__A						,					__A       )[0]       )
       return {
           "pearson": pearson_corr,
           "spearmanr": spearman_corr,
       }
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,					_KWARGS_DESCRIPTION    )
class 				SCREAMING_SNAKE_CASE__						(       datasets.Metric    ):
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Tuple:
             if self.config_name not in [
                 "sst2",
                 "mnli",
                 "mnli_mismatched",
                 "mnli_matched",
                 "cola",
                 "stsb",
                 "mrpc",
                 "qqp",
                 "qnli",
                 "rte",
                 "wnli",
                 "hans",
             ]:
                    raise KeyError(
                        'You should supply a configuration name selected in '
                        '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
                        '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'  )
             return datasets.MetricInfo(
                 description=_DESCRIPTION  ,							citation=_CITATION  ,							inputs_description=_KWARGS_DESCRIPTION  ,							features=datasets.Features(
                     {
                         'predictions': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'  ),
                         'references': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'  ),
                     }  )  ,							codebase_urls=[]  ,							reference_urls=[]  ,							format='numpy'  ,							)

      def  SCREAMING_SNAKE_CASE							( self       : str  ,							SCREAMING_SNAKE_CASE__       : Tuple  ,							SCREAMING_SNAKE_CASE__       : Tuple  )	->    Optional[Any]:
             if self.config_name == "cola":
                    return {"matthews_correlation": matthews_corrcoef(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )}
             elif self.config_name == "stsb":
                    return pearson_and_spearman(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
             elif self.config_name in ["mrpc", "qqp"]:
                    return acc_and_fa(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
             elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
                    return {"accuracy": simple_accuracy(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )}
             else:
                    raise KeyError(
                        'You should supply a configuration name selected in '
                        '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
                        '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'  )






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>']  )


