

'''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


_snake_case         =  (
    '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  _A		(     snake_case		,							snake_case					)      ->     Any:
   warnings.warn(snake_case		,							snake_case					)
   requires_backends(snake_case		,							"sklearn"					)
   return (preds == labels).mean()







def  _A		(     snake_case		,							snake_case					)      ->     List[Any]:
   warnings.warn(snake_case		,							snake_case					)
   requires_backends(snake_case		,							"sklearn"					)
   _lowercase      :				Optional[Any]      					=       simple_accuracy(snake_case		,							snake_case					)
   _lowercase      :				str      					=       fa_score(y_true=snake_case		,							y_pred=snake_case					)
   return {
       "acc": acc,
       "f1": fa,
       "acc_and_f1": (acc + fa) / 2,
   }







def  _A		(     snake_case		,							snake_case					)      ->     Optional[Any]:
   warnings.warn(snake_case		,							snake_case					)
   requires_backends(snake_case		,							"sklearn"					)
   _lowercase      :				int      					=       pearsonr(snake_case		,							snake_case					)[0]
   _lowercase      :				Tuple      					=       spearmanr(snake_case		,							snake_case					)[0]
   return {
       "pearson": pearson_corr,
       "spearmanr": spearman_corr,
       "corr": (pearson_corr + spearman_corr) / 2,
   }







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







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









'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case         =  logging.get_logger(__name__)

_snake_case         =  {
    'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					Any     	=							'roc_bert'


   def __init__(			self      ,    _UpperCamelCase=30522      ,    _UpperCamelCase=768      ,    _UpperCamelCase=12      ,    _UpperCamelCase=12      ,    _UpperCamelCase=3072      ,    _UpperCamelCase="gelu"      ,    _UpperCamelCase=0.1      ,    _UpperCamelCase=0.1      ,    _UpperCamelCase=512      ,    _UpperCamelCase=2      ,    _UpperCamelCase=0.0_2      ,    _UpperCamelCase=1E-1_2      ,    _UpperCamelCase=True      ,    _UpperCamelCase=0      ,    _UpperCamelCase="absolute"      ,    _UpperCamelCase=None      ,    _UpperCamelCase=True      ,    _UpperCamelCase=True      ,    _UpperCamelCase=768      ,    _UpperCamelCase=910      ,    _UpperCamelCase=512      ,    _UpperCamelCase=24858      ,    _UpperCamelCase=True      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      _lowercase      :				str      					=       vocab_size
      _lowercase      :				List[str]      					=       max_position_embeddings
      _lowercase      :				List[Any]      					=       hidden_size
      _lowercase      :				Dict      					=       num_hidden_layers
      _lowercase      :				str      					=       num_attention_heads
      _lowercase      :				int      					=       intermediate_size
      _lowercase      :				Optional[Any]      					=       hidden_act
      _lowercase      :				Union[str, Any]      					=       hidden_dropout_prob
      _lowercase      :				Dict      					=       attention_probs_dropout_prob
      _lowercase      :				Dict      					=       initializer_range
      _lowercase      :				List[Any]      					=       type_vocab_size
      _lowercase      :				Tuple      					=       layer_norm_eps
      _lowercase      :				Optional[int]      					=       use_cache
      _lowercase      :				Tuple      					=       enable_pronunciation
      _lowercase      :				Optional[int]      					=       enable_shape
      _lowercase      :				int      					=       pronunciation_embed_dim
      _lowercase      :				List[str]      					=       pronunciation_vocab_size
      _lowercase      :				int      					=       shape_embed_dim
      _lowercase      :				str      					=       shape_vocab_size
      _lowercase      :				str      					=       concat_input
      _lowercase      :				Dict      					=       position_embedding_type
      _lowercase      :				Optional[Any]      					=       classifier_dropout
      super().__init__(pad_token_id=_UpperCamelCase      ,    **_UpperCamelCase  )






