


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							def     snake_case       (  self			):
										__lowerCAmelCase       					=    inspect.getfile(accelerate.test_utils			)
										__lowerCAmelCase       					=    os.path.sep.join(mod_file.split(os.path.sep			)[:-1] + ["scripts", "test_script.py"]			)
										__lowerCAmelCase       					=    os.path.sep.join(
										    mod_file.split(os.path.sep			)[:-1] + ["scripts", "test_distributed_data_loop.py"]			)
										__lowerCAmelCase       					=    os.path.sep.join(mod_file.split(os.path.sep			)[:-1] + ["scripts", "test_ops.py"]			)

							@require_multi_gpu
							def     snake_case       (  self			):
										print(f"Found {torch.cuda.device_count()} devices."			)
										__lowerCAmelCase       					=    ["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
										with patch_environment(omp_num_threads=1			):
													execute_subprocess_async(__a					,     env=os.environ.copy()			)

							@require_multi_gpu
							def     snake_case       (  self			):
										print(f"Found {torch.cuda.device_count()} devices."			)
										__lowerCAmelCase       					=    ["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", self.operation_file_path]
										print(f"Command: {cmd}"			)
										with patch_environment(omp_num_threads=1			):
													execute_subprocess_async(__a					,     env=os.environ.copy()			)

							@require_multi_gpu
							def     snake_case       (  self			):
										__lowerCAmelCase       					=    ["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__			)]
										with patch_environment(omp_num_threads=1			):
													execute_subprocess_async(__a					,     env=os.environ.copy()			)



							@require_multi_gpu
							def     snake_case       (  self			):
										print(f"Found {torch.cuda.device_count()} devices, using 2 devices only"			)
										__lowerCAmelCase       					=    ["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", self.data_loop_file_path]
										with patch_environment(omp_num_threads=1					,     cuda_visible_devices="0,1"			):
													execute_subprocess_async(__a					,     env=os.environ.copy()			)


if __name__ == "__main__":
	A  :     Tuple    					=    Accelerator()
	A  :     int    					=    (accelerator.state.process_index + 2, 1_0)
	A  :     Optional[int]    					=    torch.randint(0, 1_0, shape).to(accelerator.device)

	A  :     Optional[int]    					=    ""

	A  :     Union[str, Any]    					=    accelerator.pad_across_processes(tensor)
	if tensora.shape[0] != accelerator.state.num_processes + 1:
		error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
	if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
		error_msg += "Tensors have different values."
	if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
		error_msg += "Padding was not done with the right value (0)."

	A  :     Optional[int]    					=    accelerator.pad_across_processes(tensor, pad_first=True)
	if tensora.shape[0] != accelerator.state.num_processes + 1:
		error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
	A  :     Union[str, Any]    					=    accelerator.state.num_processes - accelerator.state.process_index - 1
	if not torch.equal(tensora[index:], tensor):
		error_msg += "Tensors have different values."
	if not torch.all(tensora[:index] == 0):
		error_msg += "Padding was not done with the right value (0)."

	# Raise error at the end to make sure we don't stop at the first failure.
	if len(error_msg) > 0:
		raise ValueError(error_msg)








"""simple docstring"""


import gc
import threading
import time

import psutil
import torch
class 						_UpperCamelCase						:
							'''simple docstring'''







							def __init__(  self			):
										__lowerCAmelCase       					=    psutil.Process()
										__lowerCAmelCase       					=    False

							def     snake_case       (  self			):
										__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     snake_case       (  self			):
										__lowerCAmelCase       					=    True
										__lowerCAmelCase       					=    threading.Thread(target=self.peak_monitor			)
										__lowerCAmelCase       					=    True
										self.thread.start()



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    False
										self.thread.join()
										return self.cpu_memory_peak
A  :     Any    					=    PeakCPUMemory()


def _lowerCamelCase			(	):

			'''simple docstring'''




			__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(_UpperCamelCase   )
			torch.cuda.reset_peak_memory_stats()

			return measures


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__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(_UpperCamelCase   ) - start_measures[str(_UpperCamelCase   )]) / 2**20
						__lowerCAmelCase       					=    (torch.cuda.max_memory_allocated(_UpperCamelCase   ) - start_measures[str(_UpperCamelCase   )]) / 2**20

			return measures


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			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(_UpperCamelCase   )]:.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"   )




