




# 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       a__	:







     def __init__(     self  ,		A  ,		A  ,		A = True  ,		A = False			)  ->   List[Any]:



          '''simple docstring'''



          a     =				scheduler
          a     =				optimizers if isinstance(A  ,		(list, tuple)			) else [optimizers]
          a     =				split_batches
          a     =				step_with_optimizer
          a     =				GradientState()







     def       lowerCAmelCase_    (     self  ,		*A  ,		**A			)  ->   Optional[int]:



          '''simple docstring'''



          if not self.step_with_optimizer:
               # No link between scheduler and optimizer -> just step
               self.scheduler.step(*A  ,		**A			)
               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(*A  ,		**A			)
          else:
               # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
               # num_processes steps per training step
               a     =				AcceleratorState().num_processes
               for _ in range(A			):
                    # 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(*A  ,		**A			)
                    else:
                         self.scheduler.step(*A  ,		**A			)







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          return self.scheduler.get_last_lr()







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          return self.scheduler.state_dict()







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Optional[Any]:



          '''simple docstring'''



          self.scheduler.load_state_dict(A			)







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          return self.scheduler.get_lr()







     def       lowerCAmelCase_    (     self  ,		*A  ,		**A			)  ->   Optional[Any]:



          '''simple docstring'''



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







import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property




@unittest.skipUnless(os.path.exists(UpperCamelCase__				)       , """Tatoeba directory does not exist."""				)
class       a__	( unittest.TestCase				):







     @cached_property
     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				tempfile.mkdtemp()
          return TatoebaConverter(save_dir=A			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          self.resolver.convert_models(["heb-eng"]			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a  ,				a     =				self.resolver.write_model_card("opus-mt-he-en"  ,		dry_run=A			)
          assert mmeta["long_pair"] == "heb-eng"

