

# 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      UpperCamelCase :

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

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			scheduler
					SCREAMING_SNAKE_CASE								=			optimizers if isinstance(snake_case__							,    (list, tuple)    ) else [optimizers]
					SCREAMING_SNAKE_CASE								=			split_batches
					SCREAMING_SNAKE_CASE								=			step_with_optimizer
					SCREAMING_SNAKE_CASE								=			GradientState()

				def UpperCamelCase			(			self :      List[Any]							,    *snake_case__ :      List[str]							,    **snake_case__ :      Any    ):

					"""simple docstring"""






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

				def UpperCamelCase			(			self :      Dict    ):

					"""simple docstring"""






					return self.scheduler.get_last_lr()

				def UpperCamelCase			(			self :      Optional[Any]    ):

					"""simple docstring"""






					return self.scheduler.state_dict()

				def UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      Optional[int]    ):

					"""simple docstring"""






					self.scheduler.load_state_dict(snake_case__    )

				def UpperCamelCase			(			self :      Tuple    ):

					"""simple docstring"""






					return self.scheduler.get_lr()




				def UpperCamelCase			(			self :      Optional[Any]							,    *snake_case__ :      Union[str, Any]							,    **snake_case__ :      List[str]    ):

					"""simple docstring"""






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




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


a_    :						Any    		=      logging.get_logger(__name__)

a_    :						Dict    		=      {
    "Visual-Attention-Network/van-base": (
        "https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
    ),
}




class      UpperCamelCase (     SCREAMING_SNAKE_CASE   ):
    __UpperCamelCase         ="van"

    def __init__(			self :      Optional[Any]							,    snake_case__ :      Tuple=2_2_4							,    snake_case__ :      Dict=3							,    snake_case__ :      Union[str, Any]=[7, 3, 3, 3]							,    snake_case__ :      str=[4, 2, 2, 2]							,    snake_case__ :      Optional[Any]=[6_4, 1_2_8, 3_2_0, 5_1_2]							,    snake_case__ :      Optional[Any]=[3, 3, 1_2, 3]							,    snake_case__ :      Tuple=[8, 8, 4, 4]							,    snake_case__ :      Any="gelu"							,    snake_case__ :      Dict=0.02							,    snake_case__ :      List[str]=1E-6							,    snake_case__ :      int=1E-2							,    snake_case__ :      Any=0.0							,    snake_case__ :      Tuple=0.0							,    **snake_case__ :      Any							,    ):

     """simple docstring"""






     super().__init__(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			image_size
     SCREAMING_SNAKE_CASE								=			num_channels
     SCREAMING_SNAKE_CASE								=			patch_sizes
     SCREAMING_SNAKE_CASE								=			strides
     SCREAMING_SNAKE_CASE								=			hidden_sizes
     SCREAMING_SNAKE_CASE								=			depths
     SCREAMING_SNAKE_CASE								=			mlp_ratios
     SCREAMING_SNAKE_CASE								=			hidden_act
     SCREAMING_SNAKE_CASE								=			initializer_range
     SCREAMING_SNAKE_CASE								=			layer_norm_eps
     SCREAMING_SNAKE_CASE								=			layer_scale_init_value
     SCREAMING_SNAKE_CASE								=			drop_path_rate
     SCREAMING_SNAKE_CASE								=			dropout_rate

