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


__lowerCamelCase							=       logging.get_logger(__name__)

__lowerCamelCase							=       {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}






class   UpperCAmelCase		(						A_ ):
   A__					:		List[str]						     =     "megatron-bert"



   def __init__(self		:					Optional[int]    ,						snake_case__		:					List[str]=2_90_56    ,						snake_case__		:					List[Any]=10_24    ,						snake_case__		:					str=24    ,						snake_case__		:					Tuple=16    ,						snake_case__		:					Union[str, Any]=40_96    ,						snake_case__		:					str="gelu"    ,						snake_case__		:					str=0.1    ,						snake_case__		:					Optional[int]=0.1    ,						snake_case__		:					Tuple=5_12    ,						snake_case__		:					Union[str, Any]=2    ,						snake_case__		:					Dict=0.02    ,						snake_case__		:					List[Any]=1e-12    ,						snake_case__		:					int=0    ,						snake_case__		:					Tuple="absolute"    ,						snake_case__		:					Any=True    ,						**snake_case__		:					Union[str, Any]    ,						)		->	Optional[Any]:


          '''simple docstring'''


          super().__init__(pad_token_id=snake_case__    ,						**snake_case__ )

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







import pytest

from datasets import inspect_metric, list_metrics, load_metric


@pytest.fixture
def 			UpperCamelCase					(    __lowerCamelCase     : Optional[int]    ):
       monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"				,					set()    )


@pytest.fixture
def 			UpperCamelCase					(    __lowerCamelCase     : str    ):






       class   UpperCAmelCase		:



          def __init__(self		:					Optional[int]    ,						snake_case__		:					str )		->	Any:


                 '''simple docstring'''


                 snake_case       :   List[str]							=      metric_id






       class   UpperCAmelCase		:
          A__					:		List[str]						     =     [MetricMock(A_ ) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]



          def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	List[str]:


                 '''simple docstring'''


                 return self._metrics



       monkeypatch.setattr("datasets.inspect.huggingface_hub"				,					HfhMock()    )




@pytest.mark.parametrize(
    "func, args"				,					[(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))]    )
def 			UpperCamelCase					(    __lowerCamelCase     : Optional[int]				,					__lowerCamelCase     : Any				,					__lowerCamelCase     : Any				,					__lowerCamelCase     : int				,					__lowerCamelCase     : Any    ):
       if "tmp_path" in args:
              snake_case       :   str							=      tuple(arg if arg != "tmp_path" else tmp_path for arg in args    )
       with pytest.warns(__lowerCamelCase				,					match="https://huggingface.co/docs/evaluate"    ):
              func(*__lowerCamelCase    )





