'''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 __a			(						__lowerCamelCase      :    Any=2			,		__lowerCamelCase      :    Dict=True			,		__lowerCamelCase      :    str=False					)						->				str:
      '''simple docstring'''


      lowercase_     			=   Accelerator(step_scheduler_with_optimizer=_a			,		split_batches=_a					)
      lowercase_     			=   torch.nn.Linear(2			,		4					)
      lowercase_     			=   torch.optim.AdamW(model.parameters()			,		lr=1.0					)
      lowercase_     			=   torch.optim.lr_scheduler.OneCycleLR(_a			,		max_lr=0.0_1			,		steps_per_epoch=2			,		epochs=1					)
      lowercase_     			=   accelerator.prepare(_a			,		_a			,		_a					)

      # 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 __a			(						__lowerCamelCase      :    Tuple=2			,		__lowerCamelCase      :    Dict=True			,		__lowerCamelCase      :    Union[str, Any]=False					)						->				Tuple:
      '''simple docstring'''


      lowercase_     			=   Accelerator(step_scheduler_with_optimizer=_a			,		split_batches=_a					)
      lowercase_     			=   torch.nn.Linear(2			,		4					)
      lowercase_     			=   torch.optim.AdamW(model.parameters()			,		lr=1.0					)
      lowercase_     			=   torch.optim.lr_scheduler.LambdaLR(_a			,		lr_lambda=lambda __lowerCamelCase					: 1 - n / 10					)
      lowercase_     			=   accelerator.prepare(_a			,		_a			,		_a					)

      # Optimizer has stepped
      lowercase_     			=   False
      scheduler.step()
      lowercase_     			=   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
      lowercase_     			=   True
      scheduler.step()
      if not step_scheduler_with_optimizer:
            lowercase_     			=   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 __a			(						__lowerCamelCase      :    int = 2					)						->				Tuple:
      '''simple docstring'''


      from transformers import get_linear_schedule_with_warmup

      lowercase_     			=   [1, 2, 4]
      for num_steps in steps:
            lowercase_     			=   GradientAccumulationPlugin(num_steps=_a			,		adjust_scheduler=num_steps > 1					)
            lowercase_     			=   Accelerator(gradient_accumulation_plugin=_a					)
            lowercase_     			=   torch.nn.Linear(2			,		4					)
            lowercase_     			=   torch.optim.AdamW(model.parameters()			,		lr=1_0.0					)
            lowercase_     			=   get_linear_schedule_with_warmup(optimizer=_a			,		num_warmup_steps=0			,		num_training_steps=20					)

            lowercase_     			=   accelerator.prepare(_a			,		_a			,		_a					)

            for i in range(10 * num_steps					):
                  with accelerator.accumulate(_a					):
                        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 				lowercase    (    unittest.TestCase			):


   def __UpperCAmelCase							( self					:						Union[str, Any])   ->       Optional[int]:
         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					:						Dict)   ->       List[Any]:
         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					:						Optional[int])   ->       Union[str, Any]:
         debug_launcher(partial(_lowercase ,		num_processes=1 ,		step_scheduler_with_optimizer=_lowercase) ,		num_processes=1)


   def __UpperCAmelCase							( self					:						List[str])   ->       List[str]:
         debug_launcher(partial(_lowercase ,		num_processes=1 ,		step_scheduler_with_optimizer=_lowercase) ,		num_processes=1)


   def __UpperCAmelCase							( self					:						List[str])   ->       Dict:
         AcceleratorState._reset_state(_lowercase)
         debug_launcher(_lowercase)
         debug_launcher(partial(_lowercase ,		num_processes=1 ,		split_batches=_lowercase) ,		num_processes=1)


   def __UpperCAmelCase							( self					:						Union[str, Any])   ->       List[Any]:
         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])   ->       Optional[int]:
         AcceleratorState._reset_state(_lowercase)
         debug_launcher(partial(_lowercase ,		step_scheduler_with_optimizer=_lowercase))


   def __UpperCAmelCase							( self					:						str)   ->       Union[str, Any]:
         AcceleratorState._reset_state(_lowercase)
         debug_launcher(partial(_lowercase ,		step_scheduler_with_optimizer=_lowercase))


   @require_huggingface_suite
   def __UpperCAmelCase							( self					:						List[str])   ->       int:
         AcceleratorState._reset_state(_lowercase)
         debug_launcher(partial(_lowercase ,		num_processes=1))
         debug_launcher(_lowercase)













def 				_lowerCAmelCase			(	_a :       str      )						->      int:
     lowerCAmelCase_       :							List[str]			 =		53_81
     for x in s:
          lowerCAmelCase_       :							Dict			 =		((hash_value << 5) + hash_value) + ord(_a      )
     return hash_value & 0XFFFFFFFF
