

# 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      lowerCamelCase_      :




	def __init__(     self						:     Union[str, Any]					,      __A						:     Dict					,      __A						:     Union[str, Any]					,      __A						:     bool = True					,      __A						:     bool = False							):
		__A :			Any		    =			scheduler
		__A :			Optional[Any]		    =			optimizers if isinstance(__A					,      (list, tuple)							) else [optimizers]
		__A :			Union[str, Any]		    =			split_batches
		__A :			List[str]		    =			step_with_optimizer
		__A :			int		    =			GradientState()




	def        lowerCAmelCase_       (     self						:     Dict					,      *__A						:     int					,      **__A						:     List[Any]							):
		if not self.step_with_optimizer:
			# No link between scheduler and optimizer -> just step
			self.scheduler.step(*__A					,      **__A							)
			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(*__A					,      **__A							)
		else:
			# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
			# num_processes steps per training step
			__A :			str		    =			AcceleratorState().num_processes
			for _ in range(__A							):
				# 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(*__A					,      **__A							)
				else:
					self.scheduler.step(*__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     Any							):
		return self.scheduler.get_last_lr()




	def        lowerCAmelCase_       (     self						:     Optional[Any]							):
		return self.scheduler.state_dict()




	def        lowerCAmelCase_       (     self						:     int					,      __A						:     int							):
		self.scheduler.load_state_dict(__A							)




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		return self.scheduler.get_lr()




	def        lowerCAmelCase_       (     self						:     List[Any]					,      *__A						:     Optional[Any]					,      **__A						:     List[str]							):
		return self.scheduler.print_lr(*__A					,      **__A							)












from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase:	List[Any] = logging.get_logger(__name__)

__UpperCamelCase:	Dict = {
    """studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""",
    """studio-ousia/luke-large""": """https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json""",
}


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				"luke"

	def __init__(						self:						Union[str, Any],					lowerCamelCase_:						str=50267,					lowerCamelCase_:						Dict=500000,					lowerCamelCase_:						Union[str, Any]=768,					lowerCamelCase_:						Dict=256,					lowerCamelCase_:						int=12,					lowerCamelCase_:						Union[str, Any]=12,					lowerCamelCase_:						str=3072,					lowerCamelCase_:						List[Any]="gelu",					lowerCamelCase_:						int=0.1,					lowerCamelCase_:						Optional[Any]=0.1,					lowerCamelCase_:						Any=512,					lowerCamelCase_:						List[str]=2,					lowerCamelCase_:						str=0.0_2,					lowerCamelCase_:						Union[str, Any]=1E-12,					lowerCamelCase_:						List[Any]=True,					lowerCamelCase_:						List[Any]=None,					lowerCamelCase_:						Optional[Any]=1,					lowerCamelCase_:						Dict=0,					lowerCamelCase_:						List[Any]=2,					**lowerCamelCase_:						Optional[Any],					):
			super().__init__(pad_token_id=lowerCamelCase_,					bos_token_id=lowerCamelCase_,					eos_token_id=lowerCamelCase_,					**lowerCamelCase_			)

			lowercase__					:      Tuple      =		vocab_size
			lowercase__					:      Any      =		entity_vocab_size
			lowercase__					:      Tuple      =		hidden_size
			lowercase__					:      List[str]      =		entity_emb_size
			lowercase__					:      Tuple      =		num_hidden_layers
			lowercase__					:      Optional[Any]      =		num_attention_heads
			lowercase__					:      Dict      =		hidden_act
			lowercase__					:      Optional[int]      =		intermediate_size
			lowercase__					:      List[str]      =		hidden_dropout_prob
			lowercase__					:      Dict      =		attention_probs_dropout_prob
			lowercase__					:      Union[str, Any]      =		max_position_embeddings
			lowercase__					:      int      =		type_vocab_size
			lowercase__					:      Optional[Any]      =		initializer_range
			lowercase__					:      Dict      =		layer_norm_eps
			lowercase__					:      List[Any]      =		use_entity_aware_attention
			lowercase__					:      Optional[Any]      =		classifier_dropout


