

"""simple docstring"""


from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


UpperCAmelCase													="\\n@article{wang2019superglue,\n  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n  journal={arXiv preprint arXiv:1905.00537},\n  year={2019}\n}\n"

UpperCAmelCase													="\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n"

UpperCAmelCase													="\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n    predictions: list of predictions to score. Depending on the SuperGlUE subset:\n        - for 'record': list of question-answer dictionaries with the following keys:\n            - 'idx': index of the question as specified by the dataset\n            - 'prediction_text': the predicted answer text\n        - for 'multirc': list of question-answer dictionaries with the following keys:\n            - 'idx': index of the question-answer pair as specified by the dataset\n            - 'prediction': the predicted answer label\n        - otherwise: list of predicted labels\n    references: list of reference labels. Depending on the SuperGLUE subset:\n        - for 'record': list of question-answers dictionaries with the following keys:\n            - 'idx': index of the question as specified by the dataset\n            - 'answers': list of possible answers\n        - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n    - for 'record':\n        - 'exact_match': Exact match between answer and gold answer\n        - 'f1': F1 score\n    - for 'multirc':\n        - 'exact_match': Exact match between answer and gold answer\n        - 'f1_m': Per-question macro-F1 score\n        - 'f1_a': Average F1 score over all answers\n    - for 'axb':\n        'matthews_correlation': Matthew Correlation\n    - for 'cb':\n        - 'accuracy': Accuracy\n        - 'f1': F1 score\n    - for all others:\n        - 'accuracy': Accuracy\nExamples:\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0, 'f1': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')\n    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]\n    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 1.0, 'f1': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')\n    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]\n    >>> references = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}\n\n    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'matthews_correlation': 1.0}\n"



def   _A					(	_a	:						Union[str, Any]     ,   _a	:						Optional[int]				):



      """simple docstring"""






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



def   _A					(	_a	:						List[Any]     ,   _a	:						Dict     ,   _a	:						Union[str, Any]="binary"				):



      """simple docstring"""






      A				  =		simple_accuracy(_a     ,   _a				)
      A				  =		float(fa_score(y_true=_a     ,   y_pred=_a     ,   average=_a				)				)
      return {
          "accuracy": acc,
          "f1": fa,
      }



def   _A					(	_a	:						int     ,   _a	:						str				):



      """simple docstring"""






      A				  =		{}
      for id_pred, label in zip(_a     ,   _a				):
            A				  =		f'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
            A				  =		id_pred["""prediction"""]
            if question_id in question_map:
                  question_map[question_id].append((pred, label)				)
            else:
                  A				  =		[(pred, label)]
      A  ,			A				  =		[], []
      for question, preds_labels in question_map.items():
            A  ,			A				  =		zip(*_a				)
            A				  =		fa_score(y_true=_a     ,   y_pred=_a     ,   average="""macro"""				)
            fas.append(_a				)
            A				  =		int(sum(pred == label for pred, label in preds_labels				) == len(_a				)				)
            ems.append(_a				)
      A				  =		float(sum(_a				) / len(_a				)				)
      A				  =		sum(_a				) / len(_a				)
      A				  =		float(fa_score(y_true=_a     ,   y_pred=[id_pred["""prediction"""] for id_pred in ids_preds]				)				)
      return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,   _KWARGS_DESCRIPTION    )
class lowerCamelCase__   (				datasets.Metric    ):



    '''simple docstring'''




    def UpperCamelCase__		( self    )    ->      List[Any]:
          if self.config_name not in [
              "boolq",
              "cb",
              "copa",
              "multirc",
              "record",
              "rte",
              "wic",
              "wsc",
              "wsc.fixed",
              "axb",
              "axg",
          ]:
                raise KeyError(
                    """You should supply a configuration name selected in """
                    """[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"""    )
          return datasets.MetricInfo(
              description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(self._get_feature_types()    ) ,codebase_urls=[] ,reference_urls=[] ,format="""numpy""" if not self.config_name == """record""" and not self.config_name == """multirc""" else None ,)




    def UpperCamelCase__		( self    )    ->      Any:
          if self.config_name == "record":
                return {
                    "predictions": {
                        "idx": {
                            "passage": datasets.Value("""int64"""    ),
                            "query": datasets.Value("""int64"""    ),
                        },
                        "prediction_text": datasets.Value("""string"""    ),
                    },
                    "references": {
                        "idx": {
                            "passage": datasets.Value("""int64"""    ),
                            "query": datasets.Value("""int64"""    ),
                        },
                        "answers": datasets.Sequence(datasets.Value("""string"""    )    ),
                    },
                }
          elif self.config_name == "multirc":
                return {
                    "predictions": {
                        "idx": {
                            "answer": datasets.Value("""int64"""    ),
                            "paragraph": datasets.Value("""int64"""    ),
                            "question": datasets.Value("""int64"""    ),
                        },
                        "prediction": datasets.Value("""int64"""    ),
                    },
                    "references": datasets.Value("""int64"""    ),
                }
          else:
                return {
                    "predictions": datasets.Value("""int64"""    ),
                    "references": datasets.Value("""int64"""    ),
                }




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_    )    ->      Optional[Any]:
          if self.config_name == "axb":
                return {"matthews_correlation": matthews_corrcoef(lowerCamelCase_ ,lowerCamelCase_    )}
          elif self.config_name == "cb":
                return acc_and_fa(lowerCamelCase_ ,lowerCamelCase_ ,fa_avg="""macro"""    )
          elif self.config_name == "record":
                A				  =		[
                    {
                        """qas""": [
                            {"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
                            for ref in references
                        ]
                    }
                ]
                A				  =		{pred["""idx"""]["""query"""]: pred["""prediction_text"""] for pred in predictions}
                return evaluate_record(lowerCamelCase_ ,lowerCamelCase_    )[0]
          elif self.config_name == "multirc":
                return evaluate_multirc(lowerCamelCase_ ,lowerCamelCase_    )
          elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
                return {"accuracy": simple_accuracy(lowerCamelCase_ ,lowerCamelCase_    )}
          else:
                raise KeyError(
                    """You should supply a configuration name selected in """
                    """[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"""    )







"""simple docstring"""


import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase													=get_tests_dir("fixtures/test_sentencepiece_bpe.model")





class lowerCamelCase__   (				SCREAMING_SNAKE_CASE     ,   unittest.TestCase    ):



    '''simple docstring'''




    _lowerCamelCase					=   BartphoTokenizer
    _lowerCamelCase					=   False
    _lowerCamelCase					=   True




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

          A				  =		["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]
          A				  =		dict(zip(lowerCamelCase_ ,range(len(lowerCamelCase_    )    )    )    )
          A				  =		{"""unk_token""": """<unk>"""}

          A				  =		os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""monolingual_vocab_file"""]    )
          with open(self.monolingual_vocab_file ,"""w""" ,encoding="""utf-8"""    ) as fp:
                for token in vocab_tokens:
                      fp.write(f'{token} {vocab_tokens[token]}\n'    )

          A				  =		BartphoTokenizer(lowerCamelCase_ ,self.monolingual_vocab_file ,**self.special_tokens_map    )
          tokenizer.save_pretrained(self.tmpdirname    )




    def UpperCamelCase__		( self ,**lowerCamelCase_    )    ->      Tuple:
          kwargs.update(self.special_tokens_map    )
          return BartphoTokenizer.from_pretrained(self.tmpdirname ,**lowerCamelCase_    )




    def UpperCamelCase__		( self ,lowerCamelCase_    )    ->      Optional[Any]:
          A				  =		"""This is a là test"""
          A				  =		"""This is a<unk><unk> test"""
          return input_text, output_text




    def UpperCamelCase__		( self    )    ->      int:
          A				  =		BartphoTokenizer(lowerCamelCase_ ,self.monolingual_vocab_file ,**self.special_tokens_map    )
          A				  =		"""This is a là test"""
          A				  =		"""▁This ▁is ▁a ▁l à ▁t est""".split()
          A				  =		tokenizer.tokenize(lowerCamelCase_    )
          self.assertListEqual(lowerCamelCase_ ,lowerCamelCase_    )

          A				  =		tokens + [tokenizer.unk_token]
          A				  =		[4, 5, 6, 3, 3, 7, 8, 3]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_    ) ,lowerCamelCase_    )




