





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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase							:      int      							=logging.get_logger(__name__)

_lowercase							:      Dict      							={
    '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}



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


  lowercase		:     Optional[int]            =				"xglm"
  lowercase		:     Any            =				["past_key_values"]

  lowercase		:     Dict            =				{
      "num_attention_heads": "attention_heads",
      "hidden_size": "d_model",
      "num_hidden_layers": "num_layers",
  }







  def __init__(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=25_60_08      ,	SCREAMING_SNAKE_CASE__    :					Dict=20_48      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=10_24      ,	SCREAMING_SNAKE_CASE__    :					str=40_96      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=24      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=16      ,	SCREAMING_SNAKE_CASE__    :					Any="gelu"      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0      ,	SCREAMING_SNAKE_CASE__    :					Tuple=0.0      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0_2      ,	SCREAMING_SNAKE_CASE__    :					int=True      ,	SCREAMING_SNAKE_CASE__    :					Dict=True      ,	SCREAMING_SNAKE_CASE__    :					Any=2      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=1      ,	SCREAMING_SNAKE_CASE__    :					str=0      ,	SCREAMING_SNAKE_CASE__    :					List[str]=2      ,	**SCREAMING_SNAKE_CASE__    :					Dict      ,	)    ->				int:
         A						:     str         =vocab_size
         A						:     Union[str, Any]         =max_position_embeddings
         A						:     Optional[Any]         =d_model
         A						:     Optional[int]         =ffn_dim
         A						:     int         =num_layers
         A						:     Any         =attention_heads
         A						:     Dict         =activation_function
         A						:     List[Any]         =dropout
         A						:     str         =attention_dropout
         A						:     List[Any]         =activation_dropout
         A						:     List[Any]         =layerdrop
         A						:     List[Any]         =init_std
         A						:     Union[str, Any]         =scale_embedding  # scale factor will be sqrt(d_model) if True
         A						:     List[str]         =use_cache

         super().__init__(
             pad_token_id=SCREAMING_SNAKE_CASE__      ,	bos_token_id=SCREAMING_SNAKE_CASE__      ,	eos_token_id=SCREAMING_SNAKE_CASE__      ,	decoder_start_token_id=SCREAMING_SNAKE_CASE__      ,	**SCREAMING_SNAKE_CASE__      ,	)






