





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 				_lowerCAmelCase			(	_a :       Any=2       , _a :       Dict=True       , _a :       str=False      )						->      str:
     lowerCAmelCase_       :							Tuple			 =		Accelerator(step_scheduler_with_optimizer=_a       , split_batches=_a      )
     lowerCAmelCase_       :							Tuple			 =		torch.nn.Linear(2       , 4      )
     lowerCAmelCase_       :							List[Any]			 =		torch.optim.AdamW(model.parameters()       , lr=1.0      )
     lowerCAmelCase_       :							Any			 =		torch.optim.lr_scheduler.OneCycleLR(_a       , max_lr=0.01       , steps_per_epoch=2       , epochs=1      )
     lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_       :							Any			 =		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 				_lowerCAmelCase			(	_a :       Tuple=2       , _a :       Dict=True       , _a :       Union[str, Any]=False      )						->      Tuple:
     lowerCAmelCase_       :							Union[str, Any]			 =		Accelerator(step_scheduler_with_optimizer=_a       , split_batches=_a      )
     lowerCAmelCase_       :							Optional[int]			 =		torch.nn.Linear(2       , 4      )
     lowerCAmelCase_       :							str			 =		torch.optim.AdamW(model.parameters()       , lr=1.0      )
     lowerCAmelCase_       :							str			 =		torch.optim.lr_scheduler.LambdaLR(_a       , lr_lambda=lambda _a      : 1 - n / 10      )
     lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_       :							Dict			 =		accelerator.prepare(_a       , _a       , _a      )

     # Optimizer has stepped
     lowerCAmelCase_       :							Dict			 =		False
     scheduler.step()
     lowerCAmelCase_       :							Tuple			 =		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
     lowerCAmelCase_       :							Union[str, Any]			 =		True
     scheduler.step()
     if not step_scheduler_with_optimizer:
          lowerCAmelCase_       :							Optional[int]			 =		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 				_lowerCAmelCase			(	_a :       int = 2      )						->      Tuple:
     from transformers import get_linear_schedule_with_warmup

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

          lowerCAmelCase_ ,	lowerCAmelCase_ ,	lowerCAmelCase_       :							int			 =		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    ):
       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    ):
       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    ):
       debug_launcher(partial(_lowercase     ,    num_processes=1     ,    step_scheduler_with_optimizer=_lowercase    )     ,    num_processes=1    )







  def 						UpperCAmelCase__		(						self    ):
       debug_launcher(partial(_lowercase     ,    num_processes=1     ,    step_scheduler_with_optimizer=_lowercase    )     ,    num_processes=1    )







  def 						UpperCAmelCase__		(						self    ):
       AcceleratorState._reset_state(_lowercase    )
       debug_launcher(_lowercase    )
       debug_launcher(partial(_lowercase     ,    num_processes=1     ,    split_batches=_lowercase    )     ,    num_processes=1    )







  def 						UpperCAmelCase__		(						self    ):
       AcceleratorState._reset_state(_lowercase    )
       debug_launcher(_lowercase    )
       debug_launcher(partial(_lowercase     ,    num_processes=1     ,    split_batches=_lowercase    )     ,    num_processes=1    )







  def 						UpperCAmelCase__		(						self    ):
       AcceleratorState._reset_state(_lowercase    )
       debug_launcher(partial(_lowercase     ,    step_scheduler_with_optimizer=_lowercase    )    )







  def 						UpperCAmelCase__		(						self    ):
       AcceleratorState._reset_state(_lowercase    )
       debug_launcher(partial(_lowercase     ,    step_scheduler_with_optimizer=_lowercase    )    )







  @require_huggingface_suite
  def 						UpperCAmelCase__		(						self    ):
       AcceleratorState._reset_state(_lowercase    )
       debug_launcher(partial(_lowercase     ,    num_processes=1    )    )
       debug_launcher(_lowercase    )







def 				_lowerCAmelCase			(	_a :       str      )						->      int:
     lowerCAmelCase_       :							Dict			 =		0
     for plain_chr in plain_text:
          lowerCAmelCase_       :							List[Any]			 =		(
              ord(_a      ) + (hash_value << 6) + (hash_value << 16) - hash_value
          )
     return hash_value
