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							:      List[Any]      							=(
    '''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__  (			lowercase:     str,		lowercase:     Optional[Any]					)				->		Any:
       warnings.warn(lowercase,		lowercase					)
       requires_backends(lowercase,		'sklearn'					)
       return (preds == labels).mean()



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



def A__  (			lowercase:     Optional[int],		lowercase:     int					)				->		Union[str, Any]:
       warnings.warn(lowercase,		lowercase					)
       requires_backends(lowercase,		'sklearn'					)
       A						:     str         =pearsonr(lowercase,		lowercase					)[0]
       A						:     Dict         =spearmanr(lowercase,		lowercase					)[0]
       return {
           "pearson": pearson_corr,
           "spearmanr": spearman_corr,
           "corr": (pearson_corr + spearman_corr) / 2,
       }



def A__  (			lowercase:     Tuple,		lowercase:     List[Any],		lowercase:     Dict					)				->		List[str]:
       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 A__  (			lowercase:     int,		lowercase:     List[str],		lowercase:     str					)				->		List[str]:
       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					)







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
    from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


_lowercase							:      Optional[int]      							=logging.get_logger(__name__)

_lowercase							:      List[str]      							={
    '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json''',
    '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json''',
    '''microsoft/deberta-v2-xlarge-mnli''': (
        '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'''
    ),
    '''microsoft/deberta-v2-xxlarge-mnli''': (
        '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'''
    ),
}



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     int            =				"deberta-v2"







  def __init__(				self    :					List[str]      ,	SCREAMING_SNAKE_CASE__    :					str=12_81_00      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=15_36      ,	SCREAMING_SNAKE_CASE__    :					Dict=24      ,	SCREAMING_SNAKE_CASE__    :					List[str]=24      ,	SCREAMING_SNAKE_CASE__    :					List[str]=61_44      ,	SCREAMING_SNAKE_CASE__    :					List[Any]="gelu"      ,	SCREAMING_SNAKE_CASE__    :					int=0.1      ,	SCREAMING_SNAKE_CASE__    :					Any=0.1      ,	SCREAMING_SNAKE_CASE__    :					List[str]=5_12      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0      ,	SCREAMING_SNAKE_CASE__    :					Tuple=0.0_2      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=1e-7      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=False      ,	SCREAMING_SNAKE_CASE__    :					Tuple=-1      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0      ,	SCREAMING_SNAKE_CASE__    :					Dict=True      ,	SCREAMING_SNAKE_CASE__    :					List[str]=None      ,	SCREAMING_SNAKE_CASE__    :					List[str]=0      ,	SCREAMING_SNAKE_CASE__    :					List[str]="gelu"      ,	**SCREAMING_SNAKE_CASE__    :					Dict      ,	)    ->				Dict:
         super().__init__(**SCREAMING_SNAKE_CASE__     )

         A						:     Dict         =hidden_size
         A						:     Optional[Any]         =num_hidden_layers
         A						:     Optional[int]         =num_attention_heads
         A						:     Optional[int]         =intermediate_size
         A						:     Any         =hidden_act
         A						:     Any         =hidden_dropout_prob
         A						:     Union[str, Any]         =attention_probs_dropout_prob
         A						:     Optional[Any]         =max_position_embeddings
         A						:     Tuple         =type_vocab_size
         A						:     Tuple         =initializer_range
         A						:     int         =relative_attention
         A						:     int         =max_relative_positions
         A						:     Optional[Any]         =pad_token_id
         A						:     Union[str, Any]         =position_biased_input

         # Backwards compatibility
         if type(SCREAMING_SNAKE_CASE__     ) == str:
                A						:     Any         =[x.strip() for x in pos_att_type.lower().split('|'     )]

         A						:     Any         =pos_att_type
         A						:     Tuple         =vocab_size
         A						:     Any         =layer_norm_eps

         A						:     Optional[Any]         =kwargs.get('pooler_hidden_size'      ,	SCREAMING_SNAKE_CASE__     )
         A						:     str         =pooler_dropout
         A						:     Any         =pooler_hidden_act







class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					str     )    ->				Mapping[str, Mapping[int, str]]:
         if self.task == "multiple-choice":
                A						:     List[Any]         ={0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
                A						:     int         ={0: 'batch', 1: 'sequence'}
         if self._config.type_vocab_size > 0:
                return OrderedDict(
                    [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)]     )
         else:
                return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)]     )







  @property
  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				int:
         return 12







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]      ,	SCREAMING_SNAKE_CASE__    :					int = -1      ,	SCREAMING_SNAKE_CASE__    :					int = -1      ,	SCREAMING_SNAKE_CASE__    :					int = -1      ,	SCREAMING_SNAKE_CASE__    :					bool = False      ,	SCREAMING_SNAKE_CASE__    :					Optional["TensorType"] = None      ,	SCREAMING_SNAKE_CASE__    :					int = 3      ,	SCREAMING_SNAKE_CASE__    :					int = 40      ,	SCREAMING_SNAKE_CASE__    :					int = 40      ,	SCREAMING_SNAKE_CASE__    :					"PreTrainedTokenizerBase" = None      ,	)    ->				Mapping[str, Any]:
         A						:     str         =super().generate_dummy_inputs(preprocessor=SCREAMING_SNAKE_CASE__      ,	framework=SCREAMING_SNAKE_CASE__     )
         if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                del dummy_inputs["token_type_ids"]
         return dummy_inputs






