


"""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					(   _SCREAMING_SNAKE_CASE=2					,			_SCREAMING_SNAKE_CASE=True					,			_SCREAMING_SNAKE_CASE=False					)     ->					Dict:
							snake_case_     							=		Accelerator(step_scheduler_with_optimizer=__UpperCamelCase					,			split_batches=__UpperCamelCase					)
							snake_case_     							=		torch.nn.Linear(2					,			4					)
							snake_case_     							=		torch.optim.AdamW(model.parameters()					,			lr=1.0					)
							snake_case_     							=		torch.optim.lr_scheduler.OneCycleLR(__UpperCamelCase					,			max_lr=0.01					,			steps_per_epoch=2					,			epochs=1					)
							snake_case_							,    snake_case_							,    snake_case_     							=		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     _a					(   _SCREAMING_SNAKE_CASE=2					,			_SCREAMING_SNAKE_CASE=True					,			_SCREAMING_SNAKE_CASE=False					)     ->					int:
							snake_case_     							=		Accelerator(step_scheduler_with_optimizer=__UpperCamelCase					,			split_batches=__UpperCamelCase					)
							snake_case_     							=		torch.nn.Linear(2					,			4					)
							snake_case_     							=		torch.optim.AdamW(model.parameters()					,			lr=1.0					)
							snake_case_     							=		torch.optim.lr_scheduler.LambdaLR(__UpperCamelCase					,			lr_lambda=lambda _SCREAMING_SNAKE_CASE					: 1 - n / 10					)
							snake_case_							,    snake_case_							,    snake_case_     							=		accelerator.prepare(__UpperCamelCase					,			__UpperCamelCase					,			__UpperCamelCase					)

							# Optimizer has stepped
							snake_case_     							=		False
							scheduler.step()
							snake_case_     							=		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
							snake_case_     							=		True
							scheduler.step()
							if not step_scheduler_with_optimizer:
														snake_case_     							=		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					(   _SCREAMING_SNAKE_CASE = 2					)     ->					Tuple:
							from transformers import get_linear_schedule_with_warmup

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

														snake_case_							,    snake_case_							,    snake_case_     							=		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):





							'''simple docstring'''

							def 		lowerCAmelCase							(					self				:   Dict							)       ->Any:



														"""simple docstring"""



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



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



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   List[str]							)       ->Optional[Any]:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   str							)       ->str:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   Dict							)       ->Optional[Any]:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   Dict							)       ->Any:



														"""simple docstring"""



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



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



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   Optional[Any]							)       ->List[Any]:



														"""simple docstring"""



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



							@require_huggingface_suite
							def 		lowerCAmelCase							(					self				:   Dict							)       ->List[str]:



														"""simple docstring"""



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





"""simple docstring"""


from ..utils import DummyObject, requires_backends


__A					=			None



def        _lowerCamelCase(*__UpperCamelCase       ,					**__UpperCamelCase			)							->					Tuple:
	requires_backends(__UpperCamelCase       ,					["""sentencepiece""", """tokenizers"""]			)



