






"""simple docstring"""





import gc
import threading
import time

import psutil
import torch






class      a						:






    def __init__(       self     :							Any					)     ->		Optional[int]:
      lowerCamelCase_         =						psutil.Process()
      lowerCamelCase_         =						False






    def    UpperCamelCase       (       self     :							str					)     ->		str:
      lowerCamelCase_         =						-1

      while True:
        lowerCamelCase_         =						max(self.process.memory_info().rss       ,						self.cpu_memory_peak					)

        # can't sleep or will not catch the peak right (this comment is here on purpose)
        if not self.peak_monitoring:
          break






    def    UpperCamelCase       (       self     :							Optional[Any]					)     ->		Optional[Any]:
      lowerCamelCase_         =						True
      lowerCamelCase_         =						threading.Thread(target=self.peak_monitor					)
      lowerCamelCase_         =						True
      self.thread.start()






    def    UpperCamelCase       (       self     :							Any					)     ->		Optional[int]:
      lowerCamelCase_         =						False
      self.thread.join()
      return self.cpu_memory_peak
_SCREAMING_SNAKE_CASE					:				List[str]					    =						PeakCPUMemory()







def      lowerCamelCase__       (							)      ->				List[Any]:
  lowerCamelCase_         =						{'time': time.time()}

  gc.collect()
  torch.cuda.empty_cache()

  # CPU mem
  lowerCamelCase_         =						psutil.Process().memory_info().rss
  cpu_peak_tracker.start()

  # GPU mem
  for i in range(torch.cuda.device_count() ):
    lowerCamelCase_         =						torch.cuda.memory_allocated(_lowerCamelCase )
  torch.cuda.reset_peak_memory_stats()

  return measures







def      lowerCamelCase__       (							_lowerCamelCase       :		List[str] )      ->				str:
  lowerCamelCase_         =						{'time': time.time() - start_measures['time']}

  gc.collect()
  torch.cuda.empty_cache()

  # CPU mem
  lowerCamelCase_         =						(psutil.Process().memory_info().rss - start_measures['cpu']) / 2**20
  lowerCamelCase_         =						(cpu_peak_tracker.stop() - start_measures['cpu']) / 2**20

  # GPU mem
  for i in range(torch.cuda.device_count() ):
    lowerCamelCase_         =						(torch.cuda.memory_allocated(_lowerCamelCase ) - start_measures[str(_lowerCamelCase )]) / 2**20
    lowerCamelCase_         =						(torch.cuda.max_memory_allocated(_lowerCamelCase ) - start_measures[str(_lowerCamelCase )]) / 2**20

  return measures







def      lowerCamelCase__       (							_lowerCamelCase       :		List[Any]		,				_lowerCamelCase       :		Any )      ->				Any:
  print(F'''{description}:''' )
  print(F'''- Time: {measures["time"]:.2f}s''' )
  for i in range(torch.cuda.device_count() ):
    print(F'''- GPU {i} allocated: {measures[str(_lowerCamelCase )]:.2f}MiB''' )
    lowerCamelCase_         =						measures[F'''{i}-peak''']
    print(F'''- GPU {i} peak: {peak:.2f}MiB''' )
  print(F'''- CPU RAM allocated: {measures["cpu"]:.2f}MiB''' )
  print(F'''- CPU RAM peak: {measures["cpu-peak"]:.2f}MiB''' )










from __future__ import annotations

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




def 			__magic_name__				( A		:					NDArray[floataa],  A		:					NDArray[floataa],  A		:					list[int],  A		:					int,  ):






			'''simple docstring'''







			a    ,		a    =			coefficient_matrix.shape
			a    ,		a    =			constant_matrix.shape

			if rowsa != colsa:
						a    =			F"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
						raise ValueError(A   )

			if colsa != 1:
						a    =			F"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
						raise ValueError(A   )

			if rowsa != rowsa:
						a    =			(
						    "Coefficient and constant matrices dimensions must be nxn and nx1 but "
						    F"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
						)
						raise ValueError(A   )

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

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

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

			a    ,		a    =			table.shape

			strictly_diagonally_dominant(A   )

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

			return [float(A   ) for i in new_val]




def 			__magic_name__				( A		:					NDArray[floataa]   ):






			'''simple docstring'''







			a    ,		a    =			table.shape

			a    =			True

			for i in range(0,  A   ):
						a    =			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()






