



'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCAmelCase									=   logging.get_logger(__name__)

__lowerCAmelCase									=   {
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    int   				=					'roberta-prelayernorm'







							def __init__(							self					:       int	,_UpperCAmelCase					:       Any=50265	,_UpperCAmelCase					:       int=768	,_UpperCAmelCase					:       Any=12	,_UpperCAmelCase					:       Union[str, Any]=12	,_UpperCAmelCase					:       Tuple=3072	,_UpperCAmelCase					:       List[str]="gelu"	,_UpperCAmelCase					:       Tuple=0.1	,_UpperCAmelCase					:       str=0.1	,_UpperCAmelCase					:       Tuple=512	,_UpperCAmelCase					:       Tuple=2	,_UpperCAmelCase					:       List[Any]=0.02	,_UpperCAmelCase					:       Any=1E-12	,_UpperCAmelCase					:       List[str]=1	,_UpperCAmelCase					:       List[Any]=0	,_UpperCAmelCase					:       Optional[int]=2	,_UpperCAmelCase					:       int="absolute"	,_UpperCAmelCase					:       List[Any]=True	,_UpperCAmelCase					:       Tuple=None	,**_UpperCAmelCase					:       int	,):
									super().__init__(pad_token_id=_UpperCAmelCase	,bos_token_id=_UpperCAmelCase	,eos_token_id=_UpperCAmelCase	,**_UpperCAmelCase		)

									_a     :      Union[str, Any]        =							vocab_size
									_a     :      Dict        =							hidden_size
									_a     :      int        =							num_hidden_layers
									_a     :      List[str]        =							num_attention_heads
									_a     :      Union[str, Any]        =							hidden_act
									_a     :      int        =							intermediate_size
									_a     :      Optional[int]        =							hidden_dropout_prob
									_a     :      Optional[int]        =							attention_probs_dropout_prob
									_a     :      str        =							max_position_embeddings
									_a     :      List[str]        =							type_vocab_size
									_a     :      List[Any]        =							initializer_range
									_a     :      Dict        =							layer_norm_eps
									_a     :      Optional[int]        =							position_embedding_type
									_a     :      int        =							use_cache
									_a     :      int        =							classifier_dropout

class 	__magic_name__						(    _UpperCamelCase   ):







							@property
							def 							__lowercase			(							self					:       Union[str, Any]		):
									if self.task == "multiple-choice":
											_a     :      List[str]        =							{0: 'batch', 1: 'choice', 2: 'sequence'}
									else:
											_a     :      List[Any]        =							{0: 'batch', 1: 'sequence'}
									return OrderedDict(
									    [
									        ('input_ids', dynamic_axis),
									        ('attention_mask', dynamic_axis),
									    ]		)











'''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


__SCREAMING_SNAKE_CASE     :List[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 	UpperCAmelCase_							(					__lowercase							:      Any			,     __lowercase							:      Tuple       )		->       int:


     '''simple docstring'''



     warnings.warn(__lowercase			,     __lowercase       )
     requires_backends(__lowercase			,     "sklearn"       )
     return (preds == labels).mean()
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      str       )		->       Optional[Any]:


     '''simple docstring'''



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


     '''simple docstring'''



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


     '''simple docstring'''



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






def 	UpperCAmelCase_							(					__lowercase							:      List[Any]			,     __lowercase							:      Dict			,     __lowercase							:      str       )		->       Union[str, Any]:


     '''simple docstring'''



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




