


'''simple docstring'''




from __future__ import annotations

from enum import Enum, unique
from typing import TypeVar

# Create a generic variable that can be 'Enum', or any subclass.
__lowercase  :      Optional[Any]      		=				TypeVar("""T""", bound="""Enum""")





@unique
class _snake_case   ( a__   ):
							"""simple docstring"""





							__a												=     8_0
							__a												=     7_0
							__a												=     6_0
							__a												=     5_0
							__a												=     4_0
							__a												=     3_0
							__a												=     2_0
							__a												=     1_0





@unique
class _snake_case   ( a__   ):
							"""simple docstring"""





							__a												=     2_4
							__a												=     2_1
							__a												=     1_8
							__a												=     1_5
							__a												=     1_2
							__a												=     9
							__a												=     6
							__a												=     3
							__a												=     2
							__a												=     1
							__a												=     -1
							__a												=     -2
							__a												=     -3
							__a												=     -6
							__a												=     -9
							__a												=     -1_2
							__a												=     -1_5
							__a												=     -1_8
							__a												=     -2_1
							__a												=     -2_4







							@classmethod
							def 		__SCREAMING_SNAKE_CASE(   cls	)     ->dict:



											"""simple docstring"""







											return {unit.name: unit.value for unit in cls if unit.value > 0}







							@classmethod
							def 		__SCREAMING_SNAKE_CASE(   cls	)     ->dict:



											"""simple docstring"""







											return {unit.name: unit.value for unit in cls if unit.value < 0}





def 							a_       (						lowercase    )			->str:






				'''simple docstring'''

				__lowerCamelCase		:      str											=					SIUnit.get_positive() if value > 0 else SIUnit.get_negative()
				for name_prefix, value_prefix in prefixes.items():
								__lowerCamelCase		:      Optional[Any]											=					value / (10**value_prefix)
								if numerical_part > 1:
												return F'{numerical_part!s} {name_prefix}'
				return str(lowercase    )





def 							a_       (						lowercase    )			->str:






				'''simple docstring'''

				for prefix in BinaryUnit:
								__lowerCamelCase		:      List[str]											=					value / (2**prefix.value)
								if numerical_part > 1:
												return F'{numerical_part!s} {prefix.name}'
				return str(lowercase    )


if __name__ == "__main__":
				import doctest

				doctest.testmod()









'''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_       (						lowercase=2							,						lowercase=True							,						lowercase=False    )			->Optional[Any]:






				'''simple docstring'''

				__lowerCamelCase		:      List[str]											=					Accelerator(step_scheduler_with_optimizer=lowercase							,						split_batches=lowercase    )
				__lowerCamelCase		:      Union[str, Any]											=					torch.nn.Linear(2							,						4    )
				__lowerCamelCase		:      str											=					torch.optim.AdamW(model.parameters()							,						lr=1.0    )
				__lowerCamelCase		:      List[str]											=					torch.optim.lr_scheduler.OneCycleLR(lowercase							,						max_lr=0.0_1							,						steps_per_epoch=2							,						epochs=1    )
				__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      List[Any]											=					accelerator.prepare(lowercase							,						lowercase							,						lowercase    )

				# 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_       (						lowercase=2							,						lowercase=True							,						lowercase=False    )			->Dict:






				'''simple docstring'''

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

				# Optimizer has stepped
				__lowerCamelCase		:      List[Any]											=					False
				scheduler.step()
				__lowerCamelCase		:      List[Any]											=					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		:      Any											=					True
				scheduler.step()
				if not step_scheduler_with_optimizer:
								__lowerCamelCase		:      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 							a_       (						lowercase = 2    )			->Dict:






				'''simple docstring'''

				from transformers import get_linear_schedule_with_warmup

				__lowerCamelCase		:      str											=					[1, 2, 4]
				for num_steps in steps:
								__lowerCamelCase		:      List[Any]											=					GradientAccumulationPlugin(num_steps=lowercase							,						adjust_scheduler=num_steps > 1    )
								__lowerCamelCase		:      Dict											=					Accelerator(gradient_accumulation_plugin=lowercase    )
								__lowerCamelCase		:      Any											=					torch.nn.Linear(2							,						4    )
								__lowerCamelCase		:      List[Any]											=					torch.optim.AdamW(model.parameters()							,						lr=1_0.0    )
								__lowerCamelCase		:      Optional[Any]											=					get_linear_schedule_with_warmup(optimizer=lowercase							,						num_warmup_steps=0							,						num_training_steps=20    )

								__lowerCamelCase						,					__lowerCamelCase						,					__lowerCamelCase		:      Optional[int]											=					accelerator.prepare(lowercase							,						lowercase							,						lowercase    )

								for i in range(10 * num_steps    ):
												with accelerator.accumulate(lowercase    ):
																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 _snake_case   ( unittest.TestCase   ):
							"""simple docstring"""





							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:



											"""simple docstring"""







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







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:



											"""simple docstring"""







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







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



											"""simple docstring"""







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







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:



											"""simple docstring"""







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







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



											"""simple docstring"""







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







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



											"""simple docstring"""







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







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Any:



											"""simple docstring"""







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







							def 		__SCREAMING_SNAKE_CASE(   self	)     ->List[Any]:



											"""simple docstring"""







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







							@require_huggingface_suite
							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:



											"""simple docstring"""







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





