
# 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						:   int       ,		snake_case__						:   Any       ,		snake_case__						:   List[str]       ,		snake_case__						:   bool = True       ,		snake_case__						:   bool = False					)						->      List[str]:


          '''simple docstring'''


          snake_case			:  Tuple     							= scheduler
          snake_case			:  Optional[int]     							= optimizers if isinstance(snake_case__       ,		(list, tuple)					) else [optimizers]
          snake_case			:  str     							= split_batches
          snake_case			:  Optional[int]     							= step_with_optimizer
          snake_case			:  str     							= GradientState()



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]       ,		*snake_case__						:   List[Any]       ,		**snake_case__						:   List[Any]					)						->      str:


          '''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
                 snake_case			:  Optional[int]     							= 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 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]					)						->      str:


          '''simple docstring'''


          return self.scheduler.get_last_lr()



   def 							_SCREAMING_SNAKE_CASE  (self						:   Any					)						->      int:


          '''simple docstring'''


          return self.scheduler.state_dict()



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]       ,		snake_case__						:   List[Any]					)						->      Optional[int]:


          '''simple docstring'''


          self.scheduler.load_state_dict(snake_case__					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict					)						->      Optional[int]:


          '''simple docstring'''


          return self.scheduler.get_lr()



   def 							_SCREAMING_SNAKE_CASE  (self						:   List[Any]       ,		*snake_case__						:   Any       ,		**snake_case__						:   Tuple					)						->      Dict:


          '''simple docstring'''


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







from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
)


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_pt_objects import *  # noqa F403
else:
   from .scheduling_consistency_models import CMStochasticIterativeScheduler
   from .scheduling_ddim import DDIMScheduler
   from .scheduling_ddim_inverse import DDIMInverseScheduler
   from .scheduling_ddim_parallel import DDIMParallelScheduler
   from .scheduling_ddpm import DDPMScheduler
   from .scheduling_ddpm_parallel import DDPMParallelScheduler
   from .scheduling_deis_multistep import DEISMultistepScheduler
   from .scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
   from .scheduling_dpmsolver_multistep_inverse import DPMSolverMultistepInverseScheduler
   from .scheduling_dpmsolver_singlestep import DPMSolverSinglestepScheduler
   from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
   from .scheduling_euler_discrete import EulerDiscreteScheduler
   from .scheduling_heun_discrete import HeunDiscreteScheduler
   from .scheduling_ipndm import IPNDMScheduler
   from .scheduling_k_dpm_2_ancestral_discrete import KDPMaAncestralDiscreteScheduler
   from .scheduling_k_dpm_2_discrete import KDPMaDiscreteScheduler
   from .scheduling_karras_ve import KarrasVeScheduler
   from .scheduling_pndm import PNDMScheduler
   from .scheduling_repaint import RePaintScheduler
   from .scheduling_sde_ve import ScoreSdeVeScheduler
   from .scheduling_sde_vp import ScoreSdeVpScheduler
   from .scheduling_unclip import UnCLIPScheduler
   from .scheduling_unipc_multistep import UniPCMultistepScheduler
   from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
   from .scheduling_vq_diffusion import VQDiffusionScheduler

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_flax_objects import *  # noqa F403
else:
   from .scheduling_ddim_flax import FlaxDDIMScheduler
   from .scheduling_ddpm_flax import FlaxDDPMScheduler
   from .scheduling_dpmsolver_multistep_flax import FlaxDPMSolverMultistepScheduler
   from .scheduling_karras_ve_flax import FlaxKarrasVeScheduler
   from .scheduling_lms_discrete_flax import FlaxLMSDiscreteScheduler
   from .scheduling_pndm_flax import FlaxPNDMScheduler
   from .scheduling_sde_ve_flax import FlaxScoreSdeVeScheduler
   from .scheduling_utils_flax import (
       FlaxKarrasDiffusionSchedulers,
       FlaxSchedulerMixin,
       FlaxSchedulerOutput,
       broadcast_to_shape_from_left,
   )


try:
   if not (is_torch_available() and is_scipy_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
   from .scheduling_lms_discrete import LMSDiscreteScheduler

try:
   if not (is_torch_available() and is_torchsde_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
   from .scheduling_dpmsolver_sde import DPMSolverSDEScheduler





