



'''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 							lowercase__     :





	def __init__(			self :    Any			,lowerCamelCase__ :    Tuple			,lowerCamelCase__ :    List[str]			,lowerCamelCase__ :    bool = True			,lowerCamelCase__ :    bool = False			):







							'''simple docstring'''
							_UpperCamelCase :		str			    =       scheduler
							_UpperCamelCase :		int			    =       optimizers if isinstance(lowerCamelCase__			,(list, tuple)			) else [optimizers]
							_UpperCamelCase :		Any			    =       split_batches
							_UpperCamelCase :		Optional[Any]			    =       step_with_optimizer
							_UpperCamelCase :		Any			    =       GradientState()





	def      UpperCamelCase_  (			self :    Union[str, Any]			,*lowerCamelCase__ :    Optional[int]			,**lowerCamelCase__ :    Union[str, Any]			):







							'''simple docstring'''
							if not self.step_with_optimizer:
													# No link between scheduler and optimizer -> just step
													self.scheduler.step(*lowerCamelCase__			,**lowerCamelCase__			)
													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(*lowerCamelCase__			,**lowerCamelCase__			)
							else:
													# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
													# num_processes steps per training step
													_UpperCamelCase :		int			    =       AcceleratorState().num_processes
													for _ in range(lowerCamelCase__			):
																			# 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(*lowerCamelCase__			,**lowerCamelCase__			)
																			else:
																									self.scheduler.step(*lowerCamelCase__			,**lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    Optional[Any]			):







							'''simple docstring'''
							return self.scheduler.get_last_lr()





	def      UpperCamelCase_  (			self :    Union[str, Any]			):







							'''simple docstring'''
							return self.scheduler.state_dict()





	def      UpperCamelCase_  (			self :    Tuple			,lowerCamelCase__ :    int			):







							'''simple docstring'''
							self.scheduler.load_state_dict(lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    Optional[Any]			):







							'''simple docstring'''
							return self.scheduler.get_lr()





	def      UpperCamelCase_  (			self :    Optional[int]			,*lowerCamelCase__ :    Dict			,**lowerCamelCase__ :    Any			):







							'''simple docstring'''
							return self.scheduler.print_lr(*lowerCamelCase__			,**lowerCamelCase__			)







'''simple docstring'''
from typing import TYPE_CHECKING

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


snake_case_			:     Optional[int]  	=  {
    'configuration_mask2former': [
        'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Mask2FormerConfig',
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     int  	=  ['Mask2FormerImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     List[Any]  	=  [
					    'MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'Mask2FormerForUniversalSegmentation',
					    'Mask2FormerModel',
					    'Mask2FormerPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

					snake_case_			:     Dict  	=  _LazyModule(__name__, globals()['__file__'], _import_structure)


