


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


__a     =			{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

__a     =			{
    """vocab_file""": {
        """google/electra-small-generator""": (
            """https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt"""
        ),
        """google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt""",
        """google/electra-large-generator""": (
            """https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt"""
        ),
        """google/electra-small-discriminator""": (
            """https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt"""
        ),
        """google/electra-base-discriminator""": (
            """https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt"""
        ),
        """google/electra-large-discriminator""": (
            """https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """google/electra-small-generator""": (
            """https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json"""
        ),
        """google/electra-base-generator""": (
            """https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json"""
        ),
        """google/electra-large-generator""": (
            """https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json"""
        ),
        """google/electra-small-discriminator""": (
            """https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json"""
        ),
        """google/electra-base-discriminator""": (
            """https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json"""
        ),
        """google/electra-large-discriminator""": (
            """https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json"""
        ),
    },
}

__a     =			{
    """google/electra-small-generator""": 512,
    """google/electra-base-generator""": 512,
    """google/electra-large-generator""": 512,
    """google/electra-small-discriminator""": 512,
    """google/electra-base-discriminator""": 512,
    """google/electra-large-discriminator""": 512,
}

__a     =			{
    """google/electra-small-generator""": {"""do_lower_case""": True},
    """google/electra-base-generator""": {"""do_lower_case""": True},
    """google/electra-large-generator""": {"""do_lower_case""": True},
    """google/electra-small-discriminator""": {"""do_lower_case""": True},
    """google/electra-base-discriminator""": {"""do_lower_case""": True},
    """google/electra-large-discriminator""": {"""do_lower_case""": True},
}






class   __lowercase				(  __snake_case  ):
      UpperCamelCase								=       VOCAB_FILES_NAMES
      UpperCamelCase								=       PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase								=       PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase								=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase								=       ElectraTokenizer





      def __init__(		self						:					Dict					,       __lowerCamelCase						:					str=None					,       __lowerCamelCase						:					Optional[int]=None					,       __lowerCamelCase						:					Tuple=True					,       __lowerCamelCase						:					Tuple="[UNK]"					,       __lowerCamelCase						:					List[Any]="[SEP]"					,       __lowerCamelCase						:					Optional[Any]="[PAD]"					,       __lowerCamelCase						:					Any="[CLS]"					,       __lowerCamelCase						:					Any="[MASK]"					,       __lowerCamelCase						:					str=True					,       __lowerCamelCase						:					str=None					,       **__lowerCamelCase						:					List[str]					,       )    ->   Any:






         """simple docstring"""







         super().__init__(
             __lowerCamelCase					,       tokenizer_file=__lowerCamelCase					,       do_lower_case=__lowerCamelCase					,       unk_token=__lowerCamelCase					,       sep_token=__lowerCamelCase					,       pad_token=__lowerCamelCase					,       cls_token=__lowerCamelCase					,       mask_token=__lowerCamelCase					,       tokenize_chinese_chars=__lowerCamelCase					,       strip_accents=__lowerCamelCase					,       **__lowerCamelCase					,       )

         UpperCAmelCase							=     json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
         if (
             normalizer_state.get("""lowercase"""					,       __lowerCamelCase       ) != do_lower_case
             or normalizer_state.get("""strip_accents"""					,       __lowerCamelCase       ) != strip_accents
             or normalizer_state.get("""handle_chinese_chars"""					,       __lowerCamelCase       ) != tokenize_chinese_chars
         ):
            UpperCAmelCase							=     getattr(__lowerCamelCase					,       normalizer_state.pop("""type"""       )       )
            UpperCAmelCase							=     do_lower_case
            UpperCAmelCase							=     strip_accents
            UpperCAmelCase							=     tokenize_chinese_chars
            UpperCAmelCase							=     normalizer_class(**__lowerCamelCase       )

         UpperCAmelCase							=     do_lower_case





      def        _lowercase				(		self						:					int					,       __lowerCamelCase						:					Optional[int]					,       __lowerCamelCase						:					List[Any]=None       )    ->   Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase							=     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
            output += token_ids_a + [self.sep_token_id]

         return output





      def        _lowercase				(		self						:					Optional[Any]					,       __lowerCamelCase						:					List[int]					,       __lowerCamelCase						:					Optional[List[int]] = None       )    ->   List[int]:






         """simple docstring"""







         UpperCAmelCase							=     [self.sep_token_id]
         UpperCAmelCase							=     [self.cls_token_id]
         if token_ids_a is None:
            return len(cls + token_ids_a + sep       ) * [0]
         return len(cls + token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1]





      def        _lowercase				(		self						:					Optional[Any]					,       __lowerCamelCase						:					str					,       __lowerCamelCase						:					Optional[str] = None       )    ->   Tuple[str]:






         """simple docstring"""







         UpperCAmelCase							=     self._tokenizer.model.save(__lowerCamelCase					,       name=__lowerCamelCase       )
         return tuple(__lowerCamelCase       )







from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


__a     =			"""\
@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}
}
"""

__a     =			"""\
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.
"""

__a     =			"""
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   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   )						->List[str]:
   return float((preds == labels).mean()   )
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_="binary"   )						->Union[str, Any]:
   UpperCAmelCase							=     simple_accuracy(lowerCAmelCase_  ,						lowerCAmelCase_   )
   UpperCAmelCase							=     float(fa_score(y_true=lowerCAmelCase_  ,						y_pred=lowerCAmelCase_  ,						average=lowerCAmelCase_   )   )
   return {
       "accuracy": acc,
       "f1": fa,
   }
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   )						->List[Any]:
   UpperCAmelCase							=     {}
   for id_pred, label in zip(lowerCAmelCase_  ,						lowerCAmelCase_   ):
      UpperCAmelCase							=     F"""{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"""
      UpperCAmelCase							=     id_pred["""prediction"""]
      if question_id in question_map:
         question_map[question_id].append((pred, label)   )
      else:
         UpperCAmelCase							=     [(pred, label)]
   UpperCAmelCase				,  UpperCAmelCase							=     [], []
   for question, preds_labels in question_map.items():
      UpperCAmelCase				,  UpperCAmelCase							=     zip(*lowerCAmelCase_   )
      UpperCAmelCase							=     fa_score(y_true=lowerCAmelCase_  ,						y_pred=lowerCAmelCase_  ,						average="""macro"""   )
      fas.append(lowerCAmelCase_   )
      UpperCAmelCase							=     int(sum(pred == label for pred, label in preds_labels   ) == len(lowerCAmelCase_   )   )
      ems.append(lowerCAmelCase_   )
   UpperCAmelCase							=     float(sum(lowerCAmelCase_   ) / len(lowerCAmelCase_   )   )
   UpperCAmelCase							=     sum(lowerCAmelCase_   ) / len(lowerCAmelCase_   )
   UpperCAmelCase							=     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   __lowercase				(  datasets.Metric  ):





      def        _lowercase				(		self						:					int       )    ->   Any:






         """simple docstring"""







         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        _lowercase				(		self						:					Optional[Any]       )    ->   Any:






         """simple docstring"""







         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        _lowercase				(		self						:					Dict					,       __lowerCamelCase						:					int					,       __lowerCamelCase						:					Optional[int]       )    ->   List[Any]:






         """simple docstring"""







         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":
            UpperCAmelCase							=     [
                {
                    """qas""": [
                        {"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
                        for ref in references
                    ]
                }
            ]
            UpperCAmelCase							=     {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\",]"""       )



