




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  (     UpperCAmelCase=2,     UpperCAmelCase=True,     UpperCAmelCase=False	)  ->int:






					"""simple docstring"""






					__magic_name__ :							int		       =							Accelerator(step_scheduler_with_optimizer=UpperCAmelCase,     split_batches=UpperCAmelCase	)
					__magic_name__ :							Any		       =							torch.nn.Linear(2,     4	)
					__magic_name__ :							Union[str, Any]		       =							torch.optim.AdamW(model.parameters(),     lr=1.0	)
					__magic_name__ :							Union[str, Any]		       =							torch.optim.lr_scheduler.OneCycleLR(UpperCAmelCase,     max_lr=0.01,     steps_per_epoch=2,     epochs=1	)
					__magic_name__       ,			__magic_name__       ,			__magic_name__ :							Optional[int]		       =							accelerator.prepare(UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase	)

					# 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  (     UpperCAmelCase=2,     UpperCAmelCase=True,     UpperCAmelCase=False	)  ->Optional[int]:






					"""simple docstring"""






					__magic_name__ :							List[Any]		       =							Accelerator(step_scheduler_with_optimizer=UpperCAmelCase,     split_batches=UpperCAmelCase	)
					__magic_name__ :							List[Any]		       =							torch.nn.Linear(2,     4	)
					__magic_name__ :							Dict		       =							torch.optim.AdamW(model.parameters(),     lr=1.0	)
					__magic_name__ :							Tuple		       =							torch.optim.lr_scheduler.LambdaLR(UpperCAmelCase,     lr_lambda=lambda UpperCAmelCase	: 1 - n / 10	)
					__magic_name__       ,			__magic_name__       ,			__magic_name__ :							Union[str, Any]		       =							accelerator.prepare(UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase	)

					# Optimizer has stepped
					__magic_name__ :							int		       =							False
					scheduler.step()
					__magic_name__ :							List[str]		       =							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
					__magic_name__ :							int		       =							True
					scheduler.step()
					if not step_scheduler_with_optimizer:
										__magic_name__ :							List[Any]		       =							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  (     UpperCAmelCase = 2	)  ->Optional[Any]:






					"""simple docstring"""






					from transformers import get_linear_schedule_with_warmup

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

										__magic_name__       ,			__magic_name__       ,			__magic_name__ :							List[Any]		       =							accelerator.prepare(UpperCAmelCase,     UpperCAmelCase,     UpperCAmelCase	)

										for i in range(10 * num_steps	):
															with accelerator.accumulate(UpperCAmelCase	):
																				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			):






	def 			lowercase				(     self						)				->       Any:




						"""simple docstring"""






						debug_launcher(partial(lowerCamelCase    ,					num_processes=1						)    ,					num_processes=1						)
						debug_launcher(partial(lowerCamelCase    ,					num_processes=1    ,					split_batches=lowerCamelCase						)    ,					num_processes=1						)






	def 			lowercase				(     self						)				->       Optional[Any]:




						"""simple docstring"""






						debug_launcher(partial(lowerCamelCase    ,					num_processes=1						)    ,					num_processes=1						)
						debug_launcher(partial(lowerCamelCase    ,					num_processes=1    ,					split_batches=lowerCamelCase						)    ,					num_processes=1						)






	def 			lowercase				(     self						)				->       Any:




						"""simple docstring"""






						debug_launcher(partial(lowerCamelCase    ,					num_processes=1    ,					step_scheduler_with_optimizer=lowerCamelCase						)    ,					num_processes=1						)






	def 			lowercase				(     self						)				->       int:




						"""simple docstring"""






						debug_launcher(partial(lowerCamelCase    ,					num_processes=1    ,					step_scheduler_with_optimizer=lowerCamelCase						)    ,					num_processes=1						)






	def 			lowercase				(     self						)				->       Any:




						"""simple docstring"""






						AcceleratorState._reset_state(lowerCamelCase						)
						debug_launcher(lowerCamelCase						)
						debug_launcher(partial(lowerCamelCase    ,					num_processes=1    ,					split_batches=lowerCamelCase						)    ,					num_processes=1						)






	def 			lowercase				(     self						)				->       Optional[int]:




						"""simple docstring"""






						AcceleratorState._reset_state(lowerCamelCase						)
						debug_launcher(lowerCamelCase						)
						debug_launcher(partial(lowerCamelCase    ,					num_processes=1    ,					split_batches=lowerCamelCase						)    ,					num_processes=1						)






	def 			lowercase				(     self						)				->       int:




						"""simple docstring"""






						AcceleratorState._reset_state(lowerCamelCase						)
						debug_launcher(partial(lowerCamelCase    ,					step_scheduler_with_optimizer=lowerCamelCase						)						)






	def 			lowercase				(     self						)				->       Union[str, Any]:




						"""simple docstring"""






						AcceleratorState._reset_state(lowerCamelCase						)
						debug_launcher(partial(lowerCamelCase    ,					step_scheduler_with_optimizer=lowerCamelCase						)						)






	@require_huggingface_suite
	def 			lowercase				(     self						)				->       Any:




						"""simple docstring"""






						AcceleratorState._reset_state(lowerCamelCase						)
						debug_launcher(partial(lowerCamelCase    ,					num_processes=1						)						)
						debug_launcher(lowerCamelCase						)









import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
				import torch

				from transformers import CamembertModel





@require_torch
@require_sentencepiece
@require_tokenizers
class 		A__    (       unittest.TestCase			):






	@slow
	def 			lowercase				(     self						)				->       int:




						"""simple docstring"""






						__magic_name__ :							Dict		       =							CamembertModel.from_pretrained('''camembert-base'''						)
						model.to(lowerCamelCase						)

						__magic_name__ :							Dict		       =							torch.tensor(
						    [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]    ,					device=lowerCamelCase    ,					dtype=torch.long    ,					)  # J'aime le camembert !
						with torch.no_grad():
											__magic_name__ :							Tuple		       =							model(lowerCamelCase						)['''last_hidden_state''']
						__magic_name__ :							int		       =							torch.Size((1, 10, 768)						)
						self.assertEqual(output.shape    ,					lowerCamelCase						)
						# compare the actual values for a slice.
						__magic_name__ :							Tuple		       =							torch.tensor(
						    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]    ,					device=lowerCamelCase    ,					dtype=torch.float    ,					)
						# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
						# camembert.eval()
						# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

						self.assertTrue(torch.allclose(output[:, :3, :3]    ,					lowerCamelCase    ,					atol=1e-4						)						)



