


'''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'''

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







     def a	( 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
                   _lowerCAmelCase    :							Dict   			=       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 a	( self   ):

            '''simple docstring'''

            return self.scheduler.get_last_lr()







     def a	( self   ):

            '''simple docstring'''

            return self.scheduler.state_dict()







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            self.scheduler.load_state_dict(snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            return self.scheduler.get_lr()







     def a	( self      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

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





'''simple docstring'''







from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *

