





"""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					,      snake_case_					,      snake_case_					,      snake_case_ = True					,      snake_case_ = False  ):

        """simple docstring"""

        A_  :       List[str]										=  scheduler
        A_  :       str										=  optimizers if isinstance(snake_case_					,      (list, tuple)  ) else [optimizers]
        A_  :       Optional[Any]										=  split_batches
        A_  :       Union[str, Any]										=  step_with_optimizer
        A_  :       Dict										=  GradientState()

      def 					lowerCamelCase_			(     self					,      *snake_case_					,      **snake_case_  ):

        """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
          A_  :       Union[str, Any]										=  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 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        return self.scheduler.get_last_lr()

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        return self.scheduler.state_dict()

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        self.scheduler.load_state_dict(snake_case_  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        return self.scheduler.get_lr()

      def 					lowerCamelCase_			(     self					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

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






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


lowerCamelCase_     :						int						=					{}


try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase_     :						Tuple						=					['MLukeTokenizer']

if TYPE_CHECKING:
   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_mluke import MLukeTokenizer


else:
   import sys

   lowerCamelCase_     :						List[str]						=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)