


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


_snake_case					:		str      =    (
    "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_					( __lowerCamelCase							,     __lowerCamelCase     ):
							warnings.warn(a__							,     a__     )
							requires_backends(a__							,     "sklearn"     )
							return (preds == labels).mean()







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							warnings.warn(a__							,     a__     )
							requires_backends(a__							,     "sklearn"     )
							__snake_case		:      str										=       simple_accuracy(a__							,     a__     )
							__snake_case		:      List[str]										=       fa_score(y_true=a__							,     y_pred=a__     )
							return {
							    "acc": acc,
							    "f1": fa,
							    "acc_and_f1": (acc + fa) / 2,
							}







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							warnings.warn(a__							,     a__     )
							requires_backends(a__							,     "sklearn"     )
							__snake_case		:      List[str]										=       pearsonr(a__							,     a__     )[0]
							__snake_case		:      int										=       spearmanr(a__							,     a__     )[0]
							return {
							    "pearson": pearson_corr,
							    "spearmanr": spearman_corr,
							    "corr": (pearson_corr + spearman_corr) / 2,
							}







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







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





import logging

from transformers.configuration_utils import PretrainedConfig


_snake_case					:		Optional[Any]      =    logging.getLogger(__name__)

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Tuple					  =  "masked_bert"

							def __init__(							self	:  Optional[int]   ,     lowerCamelCase	:  Any=30522   ,     lowerCamelCase	:  Tuple=768   ,     lowerCamelCase	:  str=12   ,     lowerCamelCase	:  Dict=12   ,     lowerCamelCase	:  List[str]=3072   ,     lowerCamelCase	:  List[str]="gelu"   ,     lowerCamelCase	:  Any=0.1   ,     lowerCamelCase	:  Any=0.1   ,     lowerCamelCase	:  List[Any]=512   ,     lowerCamelCase	:  int=2   ,     lowerCamelCase	:  str=0.02   ,     lowerCamelCase	:  List[str]=1E-12   ,     lowerCamelCase	:  Any=0   ,     lowerCamelCase	:  Dict="topK"   ,     lowerCamelCase	:  List[Any]="constant"   ,     lowerCamelCase	:  Dict=0.0   ,     **lowerCamelCase	:  List[Any]   ,     )     ->   Dict:
														super().__init__(pad_token_id=lowerCamelCase   ,     **lowerCamelCase    )

														__snake_case		:      Optional[Any]										=       vocab_size
														__snake_case		:      Optional[int]										=       hidden_size
														__snake_case		:      Tuple										=       num_hidden_layers
														__snake_case		:      Union[str, Any]										=       num_attention_heads
														__snake_case		:      Optional[Any]										=       hidden_act
														__snake_case		:      str										=       intermediate_size
														__snake_case		:      Tuple										=       hidden_dropout_prob
														__snake_case		:      Dict										=       attention_probs_dropout_prob
														__snake_case		:      List[str]										=       max_position_embeddings
														__snake_case		:      Union[str, Any]										=       type_vocab_size
														__snake_case		:      Any										=       initializer_range
														__snake_case		:      str										=       layer_norm_eps
														__snake_case		:      str										=       pruning_method
														__snake_case		:      int										=       mask_init
														__snake_case		:      Any										=       mask_scale

