from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     ):
					_UpperCamelCase     ,    _UpperCamelCase			     =     coefficient_matrix.shape
					_UpperCamelCase     ,    _UpperCamelCase			     =     constant_matrix.shape

					if rowsa != colsa:
										_UpperCamelCase			     =     f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
										raise ValueError(__snake_case						)

					if colsa != 1:
										_UpperCamelCase			     =     f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
										raise ValueError(__snake_case						)

					if rowsa != rowsa:
										_UpperCamelCase			     =     (
										    '''Coefficient and constant matrices dimensions must be nxn and nx1 but '''
										    f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
										)
										raise ValueError(__snake_case						)

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

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

					_UpperCamelCase			     =     np.concatenate(
					    (coefficient_matrix, constant_matrix)  ,     axis=1						)

					_UpperCamelCase     ,    _UpperCamelCase			     =     table.shape

					strictly_diagonally_dominant(__snake_case						)

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

					return [float(__snake_case						) for i in new_val]
def 					_snake_case	(  __snake_case						):
					_UpperCamelCase     ,    _UpperCamelCase			     =     table.shape

					_UpperCamelCase			     =     True

					for i in range(0  ,     __snake_case						):
										_UpperCamelCase			     =     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()

import logging
import os

from .state import PartialState
class     lowerCAmelCase_     (       logging.LoggerAdapter ):







							@staticmethod
							def  UpperCamelCase_						(   _A      :     Any		):
												_UpperCamelCase			     =     PartialState()
												return not main_process_only or (main_process_only and state.is_main_process)







							def  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     Optional[Any] ,			_A      :     str ,			*_A      :     int ,			**_A      :     List[Any]		):
												if PartialState._shared_state == {}:
																	raise RuntimeError(
																	    '''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'''		)
												_UpperCamelCase			     =     kwargs.pop('''main_process_only''' ,			_A		)
												_UpperCamelCase			     =     kwargs.pop('''in_order''' ,			_A		)

												if self.isEnabledFor(_A		):
																	if self._should_log(_A		):
																						_UpperCamelCase     ,    _UpperCamelCase			     =     self.process(_A ,			_A		)
																						self.logger.log(_A ,			_A ,			*_A ,			**_A		)

																	elif in_order:
																						_UpperCamelCase			     =     PartialState()
																						for i in range(state.num_processes		):
																											if i == state.process_index:
																																_UpperCamelCase     ,    _UpperCamelCase			     =     self.process(_A ,			_A		)
																																self.logger.log(_A ,			_A ,			*_A ,			**_A		)
																											state.wait_for_everyone()




def 					_snake_case	(  __snake_case  ,     __snake_case = None						):
					if log_level is None:
										_UpperCamelCase			     =     os.environ.get('''ACCELERATE_LOG_LEVEL'''  ,     __snake_case						)
					_UpperCamelCase			     =     logging.getLogger(__snake_case						)
					if log_level is not None:
										logger.setLevel(log_level.upper()						)
										logger.root.setLevel(log_level.upper()						)
					return MultiProcessAdapter(__snake_case  ,     {}						)
