



'''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  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE=2 ,		SCREAMING_SNAKE_CASE=True ,		SCREAMING_SNAKE_CASE=False     ):
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      Accelerator(step_scheduler_with_optimizer=SCREAMING_SNAKE_CASE ,		split_batches=SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:Any										=      torch.nn.Linear(2 ,		4     )
		SCREAMING_SNAKE_CASE_						:int										=      torch.optim.AdamW(model.parameters() ,		lr=1.0     )
		SCREAMING_SNAKE_CASE_						:str										=      torch.optim.lr_scheduler.OneCycleLR(SCREAMING_SNAKE_CASE ,		max_lr=0.0_1 ,		steps_per_epoch=2 ,		epochs=1     )
		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:int										=      accelerator.prepare(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )

		# 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  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE=2 ,		SCREAMING_SNAKE_CASE=True ,		SCREAMING_SNAKE_CASE=False     ):
		SCREAMING_SNAKE_CASE_						:List[str]										=      Accelerator(step_scheduler_with_optimizer=SCREAMING_SNAKE_CASE ,		split_batches=SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      torch.nn.Linear(2 ,		4     )
		SCREAMING_SNAKE_CASE_						:List[Any]										=      torch.optim.AdamW(model.parameters() ,		lr=1.0     )
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      torch.optim.lr_scheduler.LambdaLR(SCREAMING_SNAKE_CASE ,		lr_lambda=lambda SCREAMING_SNAKE_CASE     : 1 - n / 10     )
		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Tuple										=      accelerator.prepare(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )

		# Optimizer has stepped
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      False
		scheduler.step()
		SCREAMING_SNAKE_CASE_						: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
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      True
		scheduler.step()
		if not step_scheduler_with_optimizer:
				SCREAMING_SNAKE_CASE_						:Tuple										=      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  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE = 2     ):
		from transformers import get_linear_schedule_with_warmup

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

				SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      accelerator.prepare(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )

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






			def 				_lowercase	(   self  :							Tuple	):




					"""simple docstring"""







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






			def 				_lowercase	(   self  :							Optional[int]	):




					"""simple docstring"""







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






			def 				_lowercase	(   self  :							str	):




					"""simple docstring"""







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






			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







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






			def 				_lowercase	(   self  :							List[str]	):




					"""simple docstring"""







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






			def 				_lowercase	(   self  :							Any	):




					"""simple docstring"""







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






			def 				_lowercase	(   self  :							Optional[int]	):




					"""simple docstring"""







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






			def 				_lowercase	(   self  :							Dict	):




					"""simple docstring"""







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






			@require_huggingface_suite
			def 				_lowercase	(   self  :							Union[str, Any]	):




					"""simple docstring"""







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





'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


SCREAMING_SNAKE_CASE__     : int				=   {"""configuration_bert_generation""": ["""BertGenerationConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__     : List[Any]				=   ["""BertGenerationTokenizer"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__     : Optional[Any]				=   [
							    """BertGenerationDecoder""",
							    """BertGenerationEncoder""",
							    """BertGenerationPreTrainedModel""",
							    """load_tf_weights_in_bert_generation""",
							]


if TYPE_CHECKING:
							from .configuration_bert_generation import BertGenerationConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bert_generation import BertGenerationTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_bert_generation import (
														    BertGenerationDecoder,
														    BertGenerationEncoder,
														    BertGenerationPreTrainedModel,
														    load_tf_weights_in_bert_generation,
														)

else:
							import sys

							SCREAMING_SNAKE_CASE__     : Any				=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
