

from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


lowerCAmelCase							:  Any   	=						{
    """susnato/ernie-m-base_pytorch""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json""",
    """susnato/ernie-m-large_pytorch""": """https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json""",
}



class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							Tuple										=     '''ernie_m'''
			_UpperCAmelCase   :							Dict[str, str]										=     {"dropout": "classifier_dropout", "num_classes": "num_labels"}



			def __init__( self  :     List[str]   ,						lowerCAmelCase__  :     int = 25_0002   ,						lowerCAmelCase__  :     int = 768   ,						lowerCAmelCase__  :     int = 12   ,						lowerCAmelCase__  :     int = 12   ,						lowerCAmelCase__  :     int = 3072   ,						lowerCAmelCase__  :     str = "gelu"   ,						lowerCAmelCase__  :     float = 0.1   ,						lowerCAmelCase__  :     float = 0.1   ,						lowerCAmelCase__  :     int = 514   ,						lowerCAmelCase__  :     float = 0.02   ,						lowerCAmelCase__  :     int = 1   ,						lowerCAmelCase__  :     float = 1E-05   ,						lowerCAmelCase__  :     str=None   ,						lowerCAmelCase__  :     Union[str, Any]=False   ,						lowerCAmelCase__  :     Tuple=0.0   ,						**lowerCAmelCase__  :     Any   ,						):
									super().__init__(pad_token_id=lowerCAmelCase__   ,						**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Any  		=       vocab_size
									SCREAMING_SNAKE_CASE_: str  		=       hidden_size
									SCREAMING_SNAKE_CASE_: Dict  		=       num_hidden_layers
									SCREAMING_SNAKE_CASE_: Any  		=       num_attention_heads
									SCREAMING_SNAKE_CASE_: List[Any]  		=       intermediate_size
									SCREAMING_SNAKE_CASE_: Any  		=       hidden_act
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       hidden_dropout_prob
									SCREAMING_SNAKE_CASE_: Any  		=       attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_: int  		=       max_position_embeddings
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       initializer_range
									SCREAMING_SNAKE_CASE_: List[str]  		=       layer_norm_eps
									SCREAMING_SNAKE_CASE_: str  		=       classifier_dropout
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       is_decoder
									SCREAMING_SNAKE_CASE_: List[str]  		=       act_dropout









def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	):
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       [redshift, radiation_density, matter_density, dark_energy]
						if any(p < 0 for p in parameters    ):
												raise ValueError("All input parameters must be positive"    )

						if any(p > 1 for p in parameters[1:4]    ):
												raise ValueError("Relative densities cannot be greater than one"    )
						else:
												SCREAMING_SNAKE_CASE_: int  		=       1 - (matter_density + radiation_density + dark_energy)

												SCREAMING_SNAKE_CASE_: Dict  		=       (
												    radiation_density * (redshift + 1) ** 4
												    + matter_density * (redshift + 1) ** 3
												    + curvature * (redshift + 1) ** 2
												    + dark_energy
												)

												SCREAMING_SNAKE_CASE_: Any  		=       hubble_constant * e_a ** (1 / 2)
												return hubble


if __name__ == "__main__":
						import doctest

						# run doctest
						doctest.testmod()

						# demo LCDM approximation
						lowerCAmelCase							:  List[Any]   	=						0.3

						print(
						    hubble_parameter(
						        hubble_constant=68.3,
						        radiation_density=1E-4,
						        matter_density=matter_density,
						        dark_energy=1 - matter_density,
						        redshift=0,
						    )
						)






