'''simple docstring'''





# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")




class 		SCREAMING_SNAKE_CASE     :







				'''simple docstring'''





				def __init__(						self					:						str       ,		lowercase__					:						Union[str, Any]       ,		lowercase__					:						List[Any]       ,		lowercase__					:						bool = True       ,		lowercase__					:						bool = False					)							->List[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Any   			=						scheduler
										_UpperCamelCase			:			List[Any]   			=						optimizers if isinstance(lowercase__       ,		(list, tuple)					) else [optimizers]
										_UpperCamelCase			:			List[str]   			=						split_batches
										_UpperCamelCase			:			Any   			=						step_with_optimizer
										_UpperCamelCase			:			List[str]   			=						GradientState()





				def  snake_case__				(						self					:						int       ,		*lowercase__					:						Optional[Any]       ,		**lowercase__					:						Optional[Any]					)							->Dict:


										'''simple docstring'''




										if not self.step_with_optimizer:
																# No link between scheduler and optimizer -> just step
																self.scheduler.step(*lowercase__       ,		**lowercase__					)
																return

										# Otherwise, first make sure the optimizer was stepped.
										if not self.gradient_state.sync_gradients:
																if self.gradient_state.adjust_scheduler:
																						self.scheduler._step_count += 1
																return

										for opt in self.optimizers:
																if opt.step_was_skipped:
																						return
										if self.split_batches:
																# Split batches -> the training dataloader batch size is not changed so one step per training step
																self.scheduler.step(*lowercase__       ,		**lowercase__					)
										else:
																# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
																# num_processes steps per training step
																_UpperCamelCase			:			str   			=						AcceleratorState().num_processes
																for _ in range(lowercase__					):
																						# Special case when using OneCycle and `drop_last` was not used
																						if hasattr(self.scheduler       ,		"total_steps"					):
																												if self.scheduler._step_count <= self.scheduler.total_steps:
																																		self.scheduler.step(*lowercase__       ,		**lowercase__					)
																						else:
																												self.scheduler.step(*lowercase__       ,		**lowercase__					)





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


										'''simple docstring'''




										return self.scheduler.get_last_lr()





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


										'''simple docstring'''




										return self.scheduler.state_dict()





				def  snake_case__				(						self					:						Any       ,		lowercase__					:						Tuple					)							->Union[str, Any]:


										'''simple docstring'''




										self.scheduler.load_state_dict(lowercase__					)





				def  snake_case__				(						self					:						Optional[int]					)							->Any:


										'''simple docstring'''




										return self.scheduler.get_lr()







				def  snake_case__				(						self					:						Dict       ,		*lowercase__					:						Union[str, Any]       ,		**lowercase__					:						Tuple					)							->Union[str, Any]:


										'''simple docstring'''




										return self.scheduler.print_lr(*lowercase__       ,		**lowercase__					)



'''simple docstring'''





lowerCAmelCase_	:		Union[str, Any]			=     """
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

lowerCAmelCase_	:		List[Any]			=     [{"""type""": """code""", """content""": INSTALL_CONTENT}]
lowerCAmelCase_	:		int			=     {
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}


