




"""simple docstring"""







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


__A   :  int					=      logging.get_logger(__name__)

__A   :  Tuple					=      {
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}




class       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





				__UpperCAmelCase :     Any       =      "biogpt"



				def __init__(     self					:	Dict    ,   lowercase__					:	Optional[Any]=4_2_3_8_4    ,   lowercase__					:	Optional[int]=1_0_2_4    ,   lowercase__					:	List[str]=2_4    ,   lowercase__					:	Optional[int]=1_6    ,   lowercase__					:	str=4_0_9_6    ,   lowercase__					:	Optional[int]="gelu"    ,   lowercase__					:	Tuple=0.1    ,   lowercase__					:	Union[str, Any]=0.1    ,   lowercase__					:	Any=1_0_2_4    ,   lowercase__					:	Any=0.0_2    ,   lowercase__					:	Union[str, Any]=1e-12    ,   lowercase__					:	Any=True    ,   lowercase__					:	Tuple=True    ,   lowercase__					:	List[Any]=0.0    ,   lowercase__					:	Dict=0.0    ,   lowercase__					:	Tuple=1    ,   lowercase__					:	List[Any]=0    ,   lowercase__					:	int=2    ,   **lowercase__					:	List[str]    ,   ):
						__lowercase				:		List[Any]												=				vocab_size
						__lowercase				:		List[str]												=				max_position_embeddings
						__lowercase				:		str												=				hidden_size
						__lowercase				:		str												=				num_hidden_layers
						__lowercase				:		List[str]												=				num_attention_heads
						__lowercase				:		Optional[int]												=				intermediate_size
						__lowercase				:		List[Any]												=				hidden_act
						__lowercase				:		Any												=				hidden_dropout_prob
						__lowercase				:		Union[str, Any]												=				attention_probs_dropout_prob
						__lowercase				:		Optional[Any]												=				initializer_range
						__lowercase				:		Optional[Any]												=				layer_norm_eps
						__lowercase				:		List[str]												=				scale_embedding
						__lowercase				:		List[Any]												=				use_cache
						__lowercase				:		Any												=				layerdrop
						__lowercase				:		Optional[Any]												=				activation_dropout
						super().__init__(pad_token_id=lowercase__    ,   bos_token_id=lowercase__    ,   eos_token_id=lowercase__    ,   **lowercase__						)






"""simple docstring"""







from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  )			->list[float]:






		"""simple docstring"""


		__lowercase						,__lowercase				:		Dict												=				coefficient_matrix.shape
		__lowercase						,__lowercase				:		Tuple												=				constant_matrix.shape

		if rowsa != colsa:
				__lowercase				:		str												=				F'Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'
				raise ValueError(_lowerCamelCase				)

		if colsa != 1:
				__lowercase				:		Dict												=				F'Constant matrix must be nx1 but received {rowsa}x{colsa}'
				raise ValueError(_lowerCamelCase				)

		if rowsa != rowsa:
				__lowercase				:		Optional[Any]												=				(
				    "Coefficient and constant matrices dimensions must be nxn and nx1 but "
				    F'received {rowsa}x{colsa} and {rowsa}x{colsa}'
				)
				raise ValueError(_lowerCamelCase				)

		if len(_lowerCamelCase				) != rowsa:
				__lowercase				:		Dict												=				(
				    "Number of initial values must be equal to number of rows in coefficient "
				    F'matrix but received {len(_lowerCamelCase				)} and {rowsa}'
				)
				raise ValueError(_lowerCamelCase				)

		if iterations <= 0:
				raise ValueError("Iterations must be at least 1"				)

		__lowercase				:		NDArray[floataa]												=				np.concatenate(
		    (coefficient_matrix, constant_matrix),  axis=1				)

		__lowercase						,__lowercase				:		str												=				table.shape

		strictly_diagonally_dominant(_lowerCamelCase				)

		# Iterates the whole matrix for given number of times
		for _ in range(_lowerCamelCase				):
				__lowercase				:		List[Any]												=				[]
				for row in range(_lowerCamelCase				):
						__lowercase				:		Optional[Any]												=				0
						for col in range(_lowerCamelCase				):
								if col == row:
										__lowercase				:		Tuple												=				table[row][col]
								elif col == cols - 1:
										__lowercase				:		Tuple												=				table[row][col]
								else:
										temp += (-1) * table[row][col] * init_val[col]
						__lowercase				:		List[Any]												=				(temp + val) / denom
						new_val.append(_lowerCamelCase				)
				__lowercase				:		Dict												=				new_val

		return [float(_lowerCamelCase				) for i in new_val]

def 						snake_case__       (					_lowerCamelCase				)			->bool:






		"""simple docstring"""


		__lowercase						,__lowercase				:		int												=				table.shape

		__lowercase				:		Optional[int]												=				True

		for i in range(0,  _lowerCamelCase				):
				__lowercase				:		Optional[int]												=				0
				for j in range(0,  cols - 1				):
						if i == j:
								continue
						else:
								total += table[i][j]

				if table[i][i] <= total:
						raise ValueError("Coefficient matrix is not strictly diagonally dominant"				)

		return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
						import doctest

						doctest.testmod()
