




import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')





class     lowerCamelCase__:
      def __init__(				self: Optional[int]    ,       UpperCamelCase_: Optional[int]    ,       UpperCamelCase_: Union[str, Any]    ,       UpperCamelCase_: bool = True    ,       UpperCamelCase_: bool = False   ):
             __lowerCamelCase   =						scheduler
             __lowerCamelCase   =						optimizers if isinstance(__A    ,       (list, tuple)   ) else [optimizers]
             __lowerCamelCase   =						split_batches
             __lowerCamelCase   =						step_with_optimizer
             __lowerCamelCase   =						GradientState()
      def 			lowerCAmelCase__						(				self: List[str]    ,       *UpperCamelCase_: Dict    ,       **UpperCamelCase_: List[Any]   ):
             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
                    __lowerCamelCase   =						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]   ):
             return self.scheduler.get_last_lr()
      def 			lowerCAmelCase__						(				self: List[str]   ):
             return self.scheduler.state_dict()
      def 			lowerCAmelCase__						(				self: Optional[int]    ,       UpperCamelCase_: Dict   ):
             self.scheduler.load_state_dict(__A   )
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             return self.scheduler.get_lr()




      def 			lowerCAmelCase__						(				self: Tuple    ,       *UpperCamelCase_: Optional[int]    ,       **UpperCamelCase_: str   ):
             return self.scheduler.print_lr(*__A    ,       **__A   )








'''simple docstring'''
import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
    'split_dict'				,  [
        SplitDict(),
        SplitDict({'train': SplitInfo(name='train'				,  num_bytes=1337				,  num_examples=42				,  dataset_name='my_dataset'     )}     ),
        SplitDict({'train': SplitInfo(name='train'				,  num_bytes=1337				,  num_examples=42     )}     ),
        SplitDict({'train': SplitInfo()}     ),
    ]				,  )
def 				lowercase__   (   __lowercase						:  SplitDict     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       =  split_dict._to_yaml_list()
       assert len(__lowercase     ) == len(__lowercase     )
       __UpperCamelCase       =  SplitDict._from_yaml_list(__lowercase     )
       for split_name, split_info in split_dict.items():
              # dataset_name field is deprecated, and is therefore not part of the YAML dump
              __UpperCamelCase       =  None
              # the split name of split_dict takes over the name of the split info object
              __UpperCamelCase       =  split_name
       assert split_dict == reloaded


@pytest.mark.parametrize(
    'split_info'				,  [SplitInfo(), SplitInfo(dataset_name=__lowercase     ), SplitInfo(dataset_name='my_dataset'     )]     )
def 				lowercase__   (   __lowercase						:  Dict     ) ->      Any:



       """simple docstring"""






       __UpperCamelCase       =  asdict(SplitDict({'train': split_info}     )     )
       assert "dataset_name" in split_dict_asdict["train"]
       assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
