






'''simple docstring'''







import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin


def 						_UpperCamelCase							(     snake_case__=2							,			snake_case__=True							,			snake_case__=False							):
      __A		   =      Accelerator(step_scheduler_with_optimizer=snake_case__							,			split_batches=snake_case__							)
      __A		   =      torch.nn.Linear(2							,			4							)
      __A		   =      torch.optim.AdamW(model.parameters()							,			lr=1.0							)
      __A		   =      torch.optim.lr_scheduler.OneCycleLR(snake_case__							,			max_lr=0.01							,			steps_per_epoch=2							,			epochs=1							)
      __A   ,				__A   ,				__A		   =      accelerator.prepare(snake_case__							,			snake_case__							,			snake_case__							)

      # Optimizer has stepped
      scheduler.step()
      if step_scheduler_with_optimizer or (num_processes == 1):
            assert (
                scheduler.scheduler.last_epoch == num_processes
            ), f"Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"
      else:
            assert (
                scheduler.scheduler.last_epoch != num_processes
            ), f"Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"


def 						_UpperCamelCase							(     snake_case__=2							,			snake_case__=True							,			snake_case__=False							):
      __A		   =      Accelerator(step_scheduler_with_optimizer=snake_case__							,			split_batches=snake_case__							)
      __A		   =      torch.nn.Linear(2							,			4							)
      __A		   =      torch.optim.AdamW(model.parameters()							,			lr=1.0							)
      __A		   =      torch.optim.lr_scheduler.LambdaLR(snake_case__							,			lr_lambda=lambda snake_case__							: 1 - n / 10							)
      __A   ,				__A   ,				__A		   =      accelerator.prepare(snake_case__							,			snake_case__							,			snake_case__							)

      # Optimizer has stepped
      __A		   =      False
      scheduler.step()
      __A		   =      1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
      assert (
          scheduler.get_last_lr()[0] == expected_lr
      ), f"Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"

      # Optimizer has not stepped
      __A		   =      True
      scheduler.step()
      if not step_scheduler_with_optimizer:
            __A		   =      1 - 2 / 10
      assert (
          scheduler.get_last_lr()[0] == expected_lr
      ), f"Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"


def 						_UpperCamelCase							(     snake_case__ = 2							):
      from transformers import get_linear_schedule_with_warmup

      __A		   =      [1, 2, 4]
      for num_steps in steps:
            __A		   =      GradientAccumulationPlugin(num_steps=snake_case__							,			adjust_scheduler=num_steps > 1							)
            __A		   =      Accelerator(gradient_accumulation_plugin=snake_case__							)
            __A		   =      torch.nn.Linear(2							,			4							)
            __A		   =      torch.optim.AdamW(model.parameters()							,			lr=10.0							)
            __A		   =      get_linear_schedule_with_warmup(optimizer=snake_case__							,			num_warmup_steps=0							,			num_training_steps=20							)

            __A   ,				__A   ,				__A		   =      accelerator.prepare(snake_case__							,			snake_case__							,			snake_case__							)

            for i in range(10 * num_steps							):
                  with accelerator.accumulate(snake_case__							):
                        optimizer.step()
                        scheduler.step()

                  if i == (10 * num_steps - 2):
                        assert (
                            scheduler.get_last_lr()[0] != 0
                        ), f"Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"
            assert (
                scheduler.get_last_lr()[0] == 0
            ), f"Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"
            GradientState._reset_state()






@require_cpu
class 					a__    (			unittest.TestCase						):
     '''simple docstring'''







     def     _UpperCamelCase   (self    :	List[Any]					)  ->       Tuple:
           debug_launcher(partial(lowercase__				,				num_processes=1					)				,				num_processes=1					)
           debug_launcher(partial(lowercase__				,				num_processes=1				,				split_batches=lowercase__					)				,				num_processes=1					)






     def     _UpperCamelCase   (self    :	Any					)  ->       str:
           debug_launcher(partial(lowercase__				,				num_processes=1					)				,				num_processes=1					)
           debug_launcher(partial(lowercase__				,				num_processes=1				,				split_batches=lowercase__					)				,				num_processes=1					)






     def     _UpperCamelCase   (self    :	str					)  ->       Any:
           debug_launcher(partial(lowercase__				,				num_processes=1				,				step_scheduler_with_optimizer=lowercase__					)				,				num_processes=1					)






     def     _UpperCamelCase   (self    :	List[Any]					)  ->       Tuple:
           debug_launcher(partial(lowercase__				,				num_processes=1				,				step_scheduler_with_optimizer=lowercase__					)				,				num_processes=1					)






     def     _UpperCamelCase   (self    :	Any					)  ->       Dict:
           AcceleratorState._reset_state(lowercase__					)
           debug_launcher(lowercase__					)
           debug_launcher(partial(lowercase__				,				num_processes=1				,				split_batches=lowercase__					)				,				num_processes=1					)






     def     _UpperCamelCase   (self    :	List[Any]					)  ->       Tuple:
           AcceleratorState._reset_state(lowercase__					)
           debug_launcher(lowercase__					)
           debug_launcher(partial(lowercase__				,				num_processes=1				,				split_batches=lowercase__					)				,				num_processes=1					)






     def     _UpperCamelCase   (self    :	Tuple					)  ->       Optional[Any]:
           AcceleratorState._reset_state(lowercase__					)
           debug_launcher(partial(lowercase__				,				step_scheduler_with_optimizer=lowercase__					)					)






     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       Tuple:
           AcceleratorState._reset_state(lowercase__					)
           debug_launcher(partial(lowercase__				,				step_scheduler_with_optimizer=lowercase__					)					)






     @require_huggingface_suite
     def     _UpperCamelCase   (self    :	Tuple					)  ->       Optional[Any]:
           AcceleratorState._reset_state(lowercase__					)
           debug_launcher(partial(lowercase__				,				num_processes=1					)					)
           debug_launcher(lowercase__					)










'''simple docstring'''







import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp


def 						_UpperCamelCase							(     ):
      __A		   =      ArgumentParser(
          description=(
              "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
          )							)

      # Optional arguments for the launch helper
      parser.add_argument("--num_cores"							,			type=snake_case__							,			default=1							,			help="Number of TPU cores to use (1 or 8)."							)

      # positional
      parser.add_argument(
          "training_script"							,			type=snake_case__							,			help=(
              "The full path to the single TPU training "
              "program/script to be launched in parallel, "
              "followed by all the arguments for the "
              "training script"
          )							,			)

      # rest from the training program
      parser.add_argument("training_script_args"							,			nargs=snake_case__							)

      return parser.parse_args()


def 						_UpperCamelCase							(     ):
      __A		   =      parse_args()

      # Import training_script as a module.
      __A		   =      Path(args.training_script							)
      sys.path.append(str(script_fpath.parent.resolve()							)							)
      __A		   =      script_fpath.stem
      __A		   =      importlib.import_module(snake_case__							)

      # Patch sys.argv
      __A		   =      [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores							)]

      xmp.spawn(mod._mp_fn							,			args=()							,			nprocs=args.num_cores							)


if __name__ == "__main__":
       main()


