"""simple docstring"""




import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
    from scipy.stats import pearsonr, spearmanr
    from sklearn.metrics import fa_score, matthews_corrcoef


__lowercase										= (
    '''This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '''
    '''library. You can have a look at this example script for pointers: '''
    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''
)



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    warnings.warn(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
    requires_backends(SCREAMING_SNAKE_CASE  ,      '''sklearn'''     )
    return (preds == labels).mean()



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    warnings.warn(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
    requires_backends(SCREAMING_SNAKE_CASE  ,      '''sklearn'''     )
    __UpperCamelCase					:List[Any]        =		simple_accuracy(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:List[str]        =		fa_score(y_true=SCREAMING_SNAKE_CASE  ,      y_pred=SCREAMING_SNAKE_CASE     )
    return {
        "acc": acc,
        "f1": fa,
        "acc_and_f1": (acc + fa) / 2,
    }



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    warnings.warn(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
    requires_backends(SCREAMING_SNAKE_CASE  ,      '''sklearn'''     )
    __UpperCamelCase					:Union[str, Any]        =		pearsonr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )[0]
    __UpperCamelCase					:str        =		spearmanr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )[0]
    return {
        "pearson": pearson_corr,
        "spearmanr": spearman_corr,
        "corr": (pearson_corr + spearman_corr) / 2,
    }



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    warnings.warn(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
    requires_backends(SCREAMING_SNAKE_CASE  ,      '''sklearn'''     )
    assert len(SCREAMING_SNAKE_CASE     ) == len(SCREAMING_SNAKE_CASE     ), f"""Predictions and labels have mismatched lengths {len(SCREAMING_SNAKE_CASE     )} and {len(SCREAMING_SNAKE_CASE     )}"""
    if task_name == "cola":
        return {"mcc": matthews_corrcoef(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )}
    elif task_name == "sst-2":
        return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )}
    elif task_name == "mrpc":
        return acc_and_fa(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
    elif task_name == "sts-b":
        return pearson_and_spearman(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
    elif task_name == "qqp":
        return acc_and_fa(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
    elif task_name == "mnli":
        return {"mnli/acc": simple_accuracy(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )}
    elif task_name == "mnli-mm":
        return {"mnli-mm/acc": simple_accuracy(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )}
    elif task_name == "qnli":
        return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )}
    elif task_name == "rte":
        return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )}
    elif task_name == "wnli":
        return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )}
    elif task_name == "hans":
        return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )}
    else:
        raise KeyError(SCREAMING_SNAKE_CASE     )



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    warnings.warn(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
    requires_backends(SCREAMING_SNAKE_CASE  ,      '''sklearn'''     )
    if len(SCREAMING_SNAKE_CASE     ) != len(SCREAMING_SNAKE_CASE     ):
        raise ValueError(f"""Predictions and labels have mismatched lengths {len(SCREAMING_SNAKE_CASE     )} and {len(SCREAMING_SNAKE_CASE     )}"""     )
    if task_name == "xnli":
        return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )}
    else:
        raise KeyError(SCREAMING_SNAKE_CASE     )






from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


__lowercase										= logging.get_logger(__name__)

__lowercase										= {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__lowercase										= {
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

__lowercase										= {'''allegro/herbert-base-cased''': 514}
__lowercase										= {}


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						Tuple      =				VOCAB_FILES_NAMES
    a__     :						Dict      =				PRETRAINED_VOCAB_FILES_MAP
    a__     :						Tuple      =				PRETRAINED_INIT_CONFIGURATION
    a__     :						Optional[Any]      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__     :						Union[str, Any]      =				HerbertTokenizer







    def __init__(     self			,			__lowercase=None			,			__lowercase=None			,			__lowercase=None			,			__lowercase="<s>"			,			__lowercase="<unk>"			,			__lowercase="<pad>"			,			__lowercase="<mask>"			,			__lowercase="</s>"			,			**__lowercase			,			)	->     Optional[Any]:
        super().__init__(
            __lowercase			,			__lowercase			,			tokenizer_file=__lowercase			,			cls_token=__lowercase			,			unk_token=__lowercase			,			pad_token=__lowercase			,			mask_token=__lowercase			,			sep_token=__lowercase			,			**__lowercase			,			)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None)	->     List[int]:
        __UpperCamelCase					:List[str]        =		[self.cls_token_id]
        __UpperCamelCase					:Tuple        =		[self.sep_token_id]
        if token_ids_a is None:
            return cls + token_ids_a + sep

        return cls + token_ids_a + sep + token_ids_a + sep







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None			,			__lowercase = False)	->     List[int]:
        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=__lowercase			,			token_ids_a=__lowercase			,			already_has_special_tokens=__lowercase)

        if token_ids_a is None:
            return [1] + ([0] * len(__lowercase)) + [1]
        return [1] + ([0] * len(__lowercase)) + [1] + ([0] * len(__lowercase)) + [1]







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None)	->     List[int]:
        __UpperCamelCase					:Optional[Any]        =		[self.sep_token_id]
        __UpperCamelCase					:int        =		[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       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None)	->     Tuple[str]:
        __UpperCamelCase					:Optional[int]        =		self._tokenizer.model.save(__lowercase			,			name=__lowercase)
        return tuple(__lowercase)





