


'''simple docstring'''



def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       list[int]						,				_SCREAMING_SNAKE_CASE				:       list[int]       ):

					# Check if the input is valid
					if not len(_SCREAMING_SNAKE_CASE       ) == len(_SCREAMING_SNAKE_CASE       ) == 3:
										raise ValueError('Please enter a valid equation.'       )
					if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
										raise ValueError('Both a & b of two equations can\'t be zero.'       )

					# Extract the coefficients
					__a      :	Optional[int]    =  equationa
					__a      :	List[Any]    =  equationa

					# Calculate the determinants of the matrices
					__a      :	Dict    =  aa * ba - aa * ba
					__a      :	Union[str, Any]    =  ca * ba - ca * ba
					__a      :	str    =  aa * ca - aa * ca

					# Check if the system of linear equations has a solution (using Cramer's rule)
					if determinant == 0:
										if determinant_x == determinant_y == 0:
															raise ValueError('Infinite solutions. (Consistent system)'       )
										else:
															raise ValueError('No solution. (Inconsistent system)'       )
					else:
										if determinant_x == determinant_y == 0:
															# Trivial solution (Inconsistent system)
															return (0.0, 0.0)
										else:
															__a      :	Any    =  determinant_x / determinant
															__a      :	Union[str, Any]    =  determinant_y / determinant
															# Non-Trivial Solution (Consistent system)
															return (x, y)







'''simple docstring'''



from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


__lowercase     :						List[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 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   "ernie_m"
	A_           =   {"dropout": "classifier_dropout", "num_classes": "num_labels"}




	def __init__(       self						,    __a = 25_0002						,    __a = 768						,    __a = 12						,    __a = 12						,    __a = 3072						,    __a = "gelu"						,    __a = 0.1						,    __a = 0.1						,    __a = 514						,    __a = 0.02						,    __a = 1						,    __a = 1E-0_5						,    __a=None						,    __a=False						,    __a=0.0						,    **__a						,    ):

						'''simple docstring'''





						super().__init__(pad_token_id=__a						,    **__a							)
						__a      :	int    =  vocab_size
						__a      :	Dict    =  hidden_size
						__a      :	str    =  num_hidden_layers
						__a      :	Dict    =  num_attention_heads
						__a      :	List[str]    =  intermediate_size
						__a      :	Union[str, Any]    =  hidden_act
						__a      :	List[Any]    =  hidden_dropout_prob
						__a      :	str    =  attention_probs_dropout_prob
						__a      :	Any    =  max_position_embeddings
						__a      :	int    =  initializer_range
						__a      :	Dict    =  layer_norm_eps
						__a      :	int    =  classifier_dropout
						__a      :	Dict    =  is_decoder
						__a      :	int    =  act_dropout



