
'''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 			lowerCAmelCase :




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase = True					,						UpperCamelCase = False      ):
								_SCREAMING_SNAKE_CASE				= scheduler
								_SCREAMING_SNAKE_CASE				= optimizers if isinstance(UpperCamelCase					,						(list, tuple)      ) else [optimizers]
								_SCREAMING_SNAKE_CASE				= split_batches
								_SCREAMING_SNAKE_CASE				= step_with_optimizer
								_SCREAMING_SNAKE_CASE				= GradientState()




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




		def     lowercase       (				self      ):
								return self.scheduler.get_last_lr()




		def     lowercase       (				self      ):
								return self.scheduler.state_dict()




		def     lowercase       (				self					,						UpperCamelCase      ):
								self.scheduler.load_state_dict(UpperCamelCase      )




		def     lowercase       (				self      ):
								return self.scheduler.get_lr()




		def     lowercase       (				self					,						*UpperCamelCase					,						**UpperCamelCase      ):
								return self.scheduler.print_lr(*UpperCamelCase					,						**UpperCamelCase      )

'''simple docstring'''







import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


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


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __init__(				self					,						*UpperCamelCase					,						**UpperCamelCase      ):
								warnings.warn(
								    "The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
								    " use DPTImageProcessor instead."					,						UpperCamelCase					,						)
								super().__init__(*UpperCamelCase					,						**UpperCamelCase      )