"""simple docstring"""







from __future__ import annotations

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


def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,):






	"""simple docstring"""





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

	if rowsa != colsa:
		_lowercase :    Any							=							F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
		raise ValueError(__UpperCAmelCase			)

	if colsa != 1:
		_lowercase :    List[Any]							=							F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
		raise ValueError(__UpperCAmelCase			)

	if rowsa != rowsa:
		_lowercase :    Dict							=							(
		    'Coefficient and constant matrices dimensions must be nxn and nx1 but '
		    F'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
		)
		raise ValueError(__UpperCAmelCase			)

	if len(__UpperCAmelCase			) != rowsa:
		_lowercase :    List[Any]							=							(
		    'Number of initial values must be equal to number of rows in coefficient '
		    F'''matrix but received {len(__UpperCAmelCase			)} and {rowsa}'''
		)
		raise ValueError(__UpperCAmelCase			)

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

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

	_lowercase				,      _lowercase :    Any							=							table.shape

	strictly_diagonally_dominant(__UpperCAmelCase			)

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

	return [float(__UpperCAmelCase			) for i in new_val]


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase				,      _lowercase :    Optional[Any]							=							table.shape

	_lowercase :    Tuple							=							True

	for i in range(0     ,__UpperCAmelCase			):
		_lowercase :    Optional[Any]							=							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()




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE									=						{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE									=						[
						    'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'BioGptForCausalLM',
						    'BioGptForTokenClassification',
						    'BioGptForSequenceClassification',
						    'BioGptModel',
						    'BioGptPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
						from .tokenization_biogpt import BioGptTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_biogpt import (
												    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BioGptForCausalLM,
												    BioGptForSequenceClassification,
												    BioGptForTokenClassification,
												    BioGptModel,
												    BioGptPreTrainedModel,
												)


else:
						import sys

						SCREAMING_SNAKE_CASE									=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



