



# 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 				snake_case__:

     """simple docstring"""






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







     def 			snake_case					(							self :     int				,       *SCREAMING_SNAKE_CASE :     Union[str, Any]				,       **SCREAMING_SNAKE_CASE :     Dict					):
         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
             lowercase__					:   Optional[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 			snake_case					(							self :     int					):
         return self.scheduler.get_last_lr()







     def 			snake_case					(							self :     Any					):
         return self.scheduler.state_dict()







     def 			snake_case					(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Any					):
         self.scheduler.load_state_dict(SCREAMING_SNAKE_CASE					)







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







     def 			snake_case					(							self :     Optional[int]				,       *SCREAMING_SNAKE_CASE :     int				,       **SCREAMING_SNAKE_CASE :     Any					):
         return self.scheduler.print_lr(*SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__					  =  {}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  ['''NllbTokenizer''']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  ['''NllbTokenizerFast''']


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

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_nllb_fast import NllbTokenizerFast

else:
				import sys

				lowerCAmelCase__					  =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




