


'''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    UpperCAmelCase_  :





    """simple docstring"""






    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_ = True			,     SCREAMING_SNAKE_CASE_ = False	)				->     Dict:
         __lowerCamelCase   :	Tuple								=		scheduler
         __lowerCamelCase   :	str								=		optimizers if isinstance(SCREAMING_SNAKE_CASE_			,     (list, tuple)	) else [optimizers]
         __lowerCamelCase   :	int								=		split_batches
         __lowerCamelCase   :	Union[str, Any]								=		step_with_optimizer
         __lowerCamelCase   :	List[str]								=		GradientState()







    def       lowercase_				(							self			,     *SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	)				->     List[str]:
         if not self.step_with_optimizer:
              # No link between scheduler and optimizer -> just step
              self.scheduler.step(*SCREAMING_SNAKE_CASE_			,     **SCREAMING_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(*SCREAMING_SNAKE_CASE_			,     **SCREAMING_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
              __lowerCamelCase   :	Any								=		AcceleratorState().num_processes
              for _ in range(SCREAMING_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(*SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	)
                   else:
                        self.scheduler.step(*SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     List[str]:
         return self.scheduler.get_last_lr()







    def       lowercase_				(							self	)				->     int:
         return self.scheduler.state_dict()







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     List[Any]:
         self.scheduler.load_state_dict(SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     int:
         return self.scheduler.get_lr()







    def       lowercase_				(							self			,     *SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	)				->     List[Any]:
         return self.scheduler.print_lr(*SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_	)










'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_torch_available,
    is_vision_available,
)


A__		:					List[str]												=  {"""configuration_beit""": ["""BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BeitConfig""", """BeitOnnxConfig"""]}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					str												=  ["""BeitFeatureExtractor"""]
      A__		:					List[str]												=  ["""BeitImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					Optional[int]												=  [
          """BEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BeitForImageClassification""",
          """BeitForMaskedImageModeling""",
          """BeitForSemanticSegmentation""",
          """BeitModel""",
          """BeitPreTrainedModel""",
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		:					Any												=  [
          """FlaxBeitForImageClassification""",
          """FlaxBeitForMaskedImageModeling""",
          """FlaxBeitModel""",
          """FlaxBeitPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_beit import BeitFeatureExtractor
            from .image_processing_beit import BeitImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_beit import (
                BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BeitForImageClassification,
                BeitForMaskedImageModeling,
                BeitForSemanticSegmentation,
                BeitModel,
                BeitPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_beit import (
                FlaxBeitForImageClassification,
                FlaxBeitForMaskedImageModeling,
                FlaxBeitModel,
                FlaxBeitPreTrainedModel,
            )


else:
      import sys

      A__		:					Optional[Any]												=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






