



'''simple docstring'''




import warnings

from .state import AcceleratorState, GradientState


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



class    lowerCamelCase__				:






      """simple docstring"""




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




      def lowerCAmelCase_					(       self							:     Any			,*a__							:     Dict			,**a__							:     List[str]   ):
             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__								= 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							:     str   ):
             return self.scheduler.state_dict()




      def lowerCAmelCase_					(       self							:     Tuple			,a__							:     Optional[Any]   ):
             self.scheduler.load_state_dict(a__   )




      def lowerCAmelCase_					(       self							:     Tuple   ):
             return self.scheduler.get_lr()




      def lowerCAmelCase_					(       self							:     Dict			,*a__							:     Any			,**a__							:     Optional[int]   ):
             return self.scheduler.print_lr(*a__			,**a__   )






"""simple docstring"""






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
			if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			from ...utils.dummy_torch_and_transformers_objects import (
			    VersatileDiffusionDualGuidedPipeline,
			    VersatileDiffusionImageVariationPipeline,
			    VersatileDiffusionPipeline,
			    VersatileDiffusionTextToImagePipeline,
			)
else:
			from .modeling_text_unet import UNetFlatConditionModel
			from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
			from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
			from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
			from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline