


'''simple docstring'''




import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


__snake_case    						=logging.getLogger(__name__)

@dataclass(frozen=__lowercase		)
class 		UpperCAmelCase_      :
     lowerCamelCase				:	str
     lowerCamelCase				:	str
     lowerCamelCase				:	Optional[str]		=     None
     lowerCamelCase				:	Optional[str]		=     None
     lowerCamelCase				:	Optional[str]		=     None




@dataclass(frozen=__lowercase		)
class 		UpperCAmelCase_      :
     lowerCamelCase				:	List[int]
     lowerCamelCase				:	Optional[List[int]]		=     None
     lowerCamelCase				:	Optional[List[int]]		=     None
     lowerCamelCase				:	Optional[Union[int, float]]		=     None
     lowerCamelCase				:	Optional[int]		=     None


if is_torch_available():
       import torch
       from torch.utils.data import Dataset

       class 		UpperCAmelCase_      (  __lowercase		):
            lowerCamelCase				:	List[InputFeatures]





            def __init__( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      str    ,      UpperCAmelCase__       :      PreTrainedTokenizer    ,      UpperCAmelCase__       :      str    ,      UpperCAmelCase__       :      Optional[int] = None    ,      UpperCAmelCase__       :      Dict=False    ,      UpperCAmelCase__       :      bool = False    ,      )				->  int:
               lowerCAmelCase     =			hans_processors[task]()

               lowerCAmelCase     =			os.path.join(
                   UpperCAmelCase__    ,      'cached_{}_{}_{}_{}'.format(
                       'dev' if evaluate else 'train'    ,      tokenizer.__class__.__name__    ,      str(UpperCAmelCase__    )    ,      UpperCAmelCase__    ,      )    ,      )
               lowerCAmelCase     =			processor.get_labels()
               if tokenizer.__class__ in (
                   RobertaTokenizer,
                   RobertaTokenizerFast,
                   XLMRobertaTokenizer,
                   BartTokenizer,
                   BartTokenizerFast,
               ):
                  # HACK(label indices are swapped in RoBERTa pretrained model)
                  lowerCAmelCase     ,  lowerCAmelCase     =			label_list[2], label_list[1]
               lowerCAmelCase     =			label_list

               # Make sure only the first process in distributed training processes the dataset,
               # and the others will use the cache.
               lowerCAmelCase     =			cached_features_file + '.lock'
               with FileLock(UpperCAmelCase__    ):
                  if os.path.exists(UpperCAmelCase__    ) and not overwrite_cache:
                     logger.info(F'''Loading features from cached file {cached_features_file}'''    )
                     lowerCAmelCase     =			torch.load(UpperCAmelCase__    )
                  else:
                     logger.info(F'''Creating features from dataset file at {data_dir}'''    )

                     lowerCAmelCase     =			(
                         processor.get_dev_examples(UpperCAmelCase__    ) if evaluate else processor.get_train_examples(UpperCAmelCase__    )
                     )

                     logger.info('Training examples: %s'    ,      len(UpperCAmelCase__    )    )
                     lowerCAmelCase     =			hans_convert_examples_to_features(UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    )
                     logger.info('Saving features into cached file %s'    ,      UpperCAmelCase__    )
                     torch.save(self.features    ,      UpperCAmelCase__    )





            def __len__( self       :      List[str]    )				->  Optional[int]:
               return len(self.features    )





            def __getitem__( self       :      Optional[int]    ,      UpperCAmelCase__       :      Optional[int]    )				->  InputFeatures:
               return self.features[i]





            def 							__UpperCAmelCase      ( self       :      Any    )				->  Any:
               return self.label_list


if is_tf_available():
       import tensorflow as tf

       class 		UpperCAmelCase_      :
            lowerCamelCase				:	List[InputFeatures]





            def __init__( self       :      str    ,      UpperCAmelCase__       :      str    ,      UpperCAmelCase__       :      PreTrainedTokenizer    ,      UpperCAmelCase__       :      str    ,      UpperCAmelCase__       :      Optional[int] = 1_2_8    ,      UpperCAmelCase__       :      str=False    ,      UpperCAmelCase__       :      bool = False    ,      )				->  int:
               lowerCAmelCase     =			hans_processors[task]()
               lowerCAmelCase     =			processor.get_labels()
               if tokenizer.__class__ in (
                   RobertaTokenizer,
                   RobertaTokenizerFast,
                   XLMRobertaTokenizer,
                   BartTokenizer,
                   BartTokenizerFast,
               ):
                  # HACK(label indices are swapped in RoBERTa pretrained model)
                  lowerCAmelCase     ,  lowerCAmelCase     =			label_list[2], label_list[1]
               lowerCAmelCase     =			label_list

               lowerCAmelCase     =			processor.get_dev_examples(UpperCAmelCase__    ) if evaluate else processor.get_train_examples(UpperCAmelCase__    )
               lowerCAmelCase     =			hans_convert_examples_to_features(UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    )

               def gen():
                  for ex_index, ex in tqdm.tqdm(enumerate(self.features    )    ,      desc='convert examples to features'    ):
                     if ex_index % 1_0_0_0_0 == 0:
                        logger.info('Writing example %d of %d' % (ex_index, len(UpperCAmelCase__    ))    )

                     yield (
                         {
                             "example_id": 0,
                             "input_ids": ex.input_ids,
                             "attention_mask": ex.attention_mask,
                             "token_type_ids": ex.token_type_ids,
                         },
                         ex.label,
                     )

               lowerCAmelCase     =			tf.data.Dataset.from_generator(
                   UpperCAmelCase__    ,      (
                       {
                           'example_id': tf.intaa,
                           'input_ids': tf.intaa,
                           'attention_mask': tf.intaa,
                           'token_type_ids': tf.intaa,
                       },
                       tf.intaa,
                   )    ,      (
                       {
                           'example_id': tf.TensorShape([]    ),
                           'input_ids': tf.TensorShape([None, None]    ),
                           'attention_mask': tf.TensorShape([None, None]    ),
                           'token_type_ids': tf.TensorShape([None, None]    ),
                       },
                       tf.TensorShape([]    ),
                   )    ,      )





            def 							__UpperCAmelCase      ( self       :      Dict    )				->  Any:
               return self.dataset





            def __len__( self       :      List[Any]    )				->  List[str]:
               return len(self.features    )





            def __getitem__( self       :      int    ,      UpperCAmelCase__       :      int    )				->  InputFeatures:
               return self.features[i]





            def 							__UpperCAmelCase      ( self       :      Tuple    )				->  int:
               return self.label_list

class 		UpperCAmelCase_      (  __lowercase		):





     def 							__UpperCAmelCase      ( self       :      Optional[int]    ,      UpperCAmelCase__       :      Dict    )				->  Any:
        return self._create_examples(self._read_tsv(os.path.join(UpperCAmelCase__    ,      'heuristics_train_set.txt'    )    )    ,      'train'    )





     def 							__UpperCAmelCase      ( self       :      List[Any]    ,      UpperCAmelCase__       :      List[Any]    )				->  List[Any]:
        return self._create_examples(self._read_tsv(os.path.join(UpperCAmelCase__    ,      'heuristics_evaluation_set.txt'    )    )    ,      'dev'    )





     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  Any:
        return ["contradiction", "entailment", "neutral"]





     def 							__UpperCAmelCase      ( self       :      Dict    ,      UpperCAmelCase__       :      List[Any]    ,      UpperCAmelCase__       :      Optional[int]    )				->  Tuple:
        lowerCAmelCase     =			[]
        for i, line in enumerate(UpperCAmelCase__    ):
           if i == 0:
              continue
           lowerCAmelCase     =			'%s-%s' % (set_type, line[0])
           lowerCAmelCase     =			line[5]
           lowerCAmelCase     =			line[6]
           lowerCAmelCase     =			line[7][2:] if line[7].startswith('ex'    ) else line[7]
           lowerCAmelCase     =			line[0]
           examples.append(InputExample(guid=UpperCAmelCase__    ,      text_a=UpperCAmelCase__    ,      text_b=UpperCAmelCase__    ,      label=UpperCAmelCase__    ,      pairID=UpperCAmelCase__    )    )
        return examples





def 	a_					( lowerCamelCase	:     List[InputExample]	, lowerCamelCase	:     List[str]	, lowerCamelCase	:     int	, lowerCamelCase	:     PreTrainedTokenizer	, ):
   lowerCAmelCase     =			{label: i for i, label in enumerate(lowerCamelCase				)}

   lowerCAmelCase     =			[]
   for ex_index, example in tqdm.tqdm(enumerate(lowerCamelCase				)	, desc='convert examples to features'				):
      if ex_index % 10000 == 0:
         logger.info('Writing example %d' % (ex_index)				)

      lowerCAmelCase     =			tokenizer(
          example.text_a	, example.text_b	, add_special_tokens=lowerCamelCase	, max_length=lowerCamelCase	, padding='max_length'	, truncation=lowerCamelCase	, return_overflowing_tokens=lowerCamelCase	, )

      lowerCAmelCase     =			label_map[example.label] if example.label in label_map else 0

      lowerCAmelCase     =			int(example.pairID				)

      features.append(InputFeatures(**lowerCamelCase	, label=lowerCamelCase	, pairID=lowerCamelCase				)				)

   for i, example in enumerate(examples[:5]				):
      logger.info('*** Example ***'				)
      logger.info(f'''guid: {example}'''				)
      logger.info(f'''features: {features[i]}'''				)

   return features


__snake_case    						={
    """hans""": 3,
}




__snake_case    						={
    """hans""": HansProcessor,
}









'''simple docstring'''




from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


__snake_case    						="""\
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  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},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}
"""

__snake_case    						="""\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
"""

__snake_case    						="""
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
    predictions: list of predictions to score. Depending on the SuperGlUE subset:
        - for 'record': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'prediction_text': the predicted answer text
        - for 'multirc': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question-answer pair as specified by the dataset
            - 'prediction': the predicted answer label
        - otherwise: list of predicted labels
    references: list of reference labels. Depending on the SuperGLUE subset:
        - for 'record': list of question-answers dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'answers': list of possible answers
        - otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
    - for 'record':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1': F1 score
    - for 'multirc':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1_m': Per-question macro-F1 score
        - 'f1_a': Average F1 score over all answers
    - for 'axb':
        'matthews_correlation': Matthew Correlation
    - for 'cb':
        - 'accuracy': Accuracy
        - 'f1': F1 score
    - for all others:
        - 'accuracy': Accuracy
Examples:

    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')
    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]
    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')
    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""





def 	a_					( lowerCamelCase	:     str	, lowerCamelCase	:     Union[str, Any]				):
   return float((preds == labels).mean()				)





def 	a_					( lowerCamelCase	:     Union[str, Any]	, lowerCamelCase	:     Dict	, lowerCamelCase	:     str="binary"				):
   lowerCAmelCase     =			simple_accuracy(lowerCamelCase	, lowerCamelCase				)
   lowerCAmelCase     =			float(fa_score(y_true=lowerCamelCase	, y_pred=lowerCamelCase	, average=lowerCamelCase				)				)
   return {
       "accuracy": acc,
       "f1": fa,
   }





def 	a_					( lowerCamelCase	:     List[Any]	, lowerCamelCase	:     List[Any]				):
   lowerCAmelCase     =			{}
   for id_pred, label in zip(lowerCamelCase	, lowerCamelCase				):
      lowerCAmelCase     =			f'''{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}'''
      lowerCAmelCase     =			id_pred['prediction']
      if question_id in question_map:
         question_map[question_id].append((pred, label)				)
      else:
         lowerCAmelCase     =			[(pred, label)]
   lowerCAmelCase     ,  lowerCAmelCase     =			[], []
   for question, preds_labels in question_map.items():
      lowerCAmelCase     ,  lowerCAmelCase     =			zip(*lowerCamelCase				)
      lowerCAmelCase     =			fa_score(y_true=lowerCamelCase	, y_pred=lowerCamelCase	, average='macro'				)
      fas.append(lowerCamelCase				)
      lowerCAmelCase     =			int(sum(pred == label for pred, label in preds_labels				) == len(lowerCamelCase				)				)
      ems.append(lowerCamelCase				)
   lowerCAmelCase     =			float(sum(lowerCamelCase				) / len(lowerCamelCase				)				)
   lowerCAmelCase     =			sum(lowerCamelCase				) / len(lowerCamelCase				)
   lowerCAmelCase     =			float(fa_score(y_true=lowerCamelCase	, 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 		UpperCAmelCase_      (  datasets.Metric		):





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





