
'''simple docstring'''
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_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCAmelCase												=	{
    'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'ResNetForImageClassification',
				    'ResNetModel',
				    'ResNetPreTrainedModel',
				    'ResNetBackbone',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFResNetForImageClassification',
				    'TFResNetModel',
				    'TFResNetPreTrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'FlaxResNetForImageClassification',
				    'FlaxResNetModel',
				    'FlaxResNetPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_resnet import (
								    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ResNetBackbone,
								    ResNetForImageClassification,
								    ResNetModel,
								    ResNetPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_resnet import (
								    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFResNetForImageClassification,
								    TFResNetModel,
								    TFResNetPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
				import sys

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