
'''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       _lowerCAmelCase   :







							'''simple docstring'''

							def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = True  ,  UpperCAmelCase = False  )					->       List[str]:
														_snake_case       =  scheduler
														_snake_case       =  optimizers if isinstance(UpperCAmelCase  ,  (list, tuple)  ) else [optimizers]
														_snake_case       =  split_batches
														_snake_case       =  step_with_optimizer
														_snake_case       =  GradientState()

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

							def 	lowercase							(self  )					->       List[str]:
														return self.scheduler.get_last_lr()

							def 	lowercase							(self  )					->       str:
														return self.scheduler.state_dict()

							def 	lowercase							(self  ,  UpperCAmelCase  )					->       Any:
														self.scheduler.load_state_dict(UpperCAmelCase  )

							def 	lowercase							(self  )					->       Dict:
														return self.scheduler.get_lr()





							def 	lowercase							(self  ,  *UpperCAmelCase  ,  **UpperCAmelCase  )					->       Any:
														return self.scheduler.print_lr(*UpperCAmelCase  ,  **UpperCAmelCase  )

'''simple docstring'''
from typing import TYPE_CHECKING

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


__lowerCAmelCase												=	{
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'EncodecModel',
				    'EncodecPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_encodec import (
				    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    EncodecConfig,
				)
				from .feature_extraction_encodec import EncodecFeatureExtractor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_encodec import (
								    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
								    EncodecModel,
								    EncodecPreTrainedModel,
								)

else:
				import sys

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