
import torch

from accelerate import Accelerator, DistributedType







class 	A__				:






				def __enter__(		self   : Optional[Any]  ):






							'''simple docstring'''







							if self.enabled:
										lowerCAmelCase__ : Any   =						self.model.no_sync()
										self.model_sync_obj.__enter__()

							return self






				def __exit__(		self   : int       ,	a   : int       ,	a   : Optional[Any]       ,	a   : str  ):






							'''simple docstring'''







							if self.enabled:
										# Average all models on exit
										self._sync_and_avg_model_params()
										self.model_sync_obj.__exit__(a       ,	a       ,	a  )






				def __init__(		self   : List[str]       ,	a   : Accelerator       ,	a   : torch.nn.Module       ,	a   : int       ,	a   : bool = True  ):






							'''simple docstring'''







							if accelerator.distributed_type not in [
							    DistributedType.NO,
							    DistributedType.MULTI_CPU,
							    DistributedType.MULTI_GPU,
							]:
										raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'  )
							lowerCAmelCase__ : Union[str, Any]   =						enabled and accelerator.distributed_type != DistributedType.NO
							lowerCAmelCase__ : str   =						0
							if self.enabled:
										lowerCAmelCase__ : Dict   =						accelerator
										lowerCAmelCase__ : Dict   =						model
										lowerCAmelCase__ : Optional[int]   =						local_sgd_steps






				def 				_lowerCamelCase						(		self   : Optional[Any]  ):






							'''simple docstring'''







							self.num_steps += 1
							if not self.enabled:
										return

							if self.num_steps % self.local_sgd_steps == 0:
										self._sync_and_avg_model_params()






				def 				_lowerCamelCase						(		self   : int  ):






							'''simple docstring'''







							self.accelerator.wait_for_everyone()
							with self.accelerator.autocast():
										for param in self.model.parameters():
													lowerCAmelCase__ : Dict   =						self.accelerator.reduce(param.data       ,	reduction='mean'  )

from typing import TYPE_CHECKING

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


lowerCamelCase__        =						{"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__        =						[
					    """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RegNetForImageClassification""",
					    """RegNetModel""",
					    """RegNetPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__        =						[
					    """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRegNetForImageClassification""",
					    """TFRegNetModel""",
					    """TFRegNetPreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__        =						[
					    """FlaxRegNetForImageClassification""",
					    """FlaxRegNetModel""",
					    """FlaxRegNetPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_regnet import (
										    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RegNetForImageClassification,
										    RegNetModel,
										    RegNetPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_regnet import (
										    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRegNetForImageClassification,
										    TFRegNetModel,
										    TFRegNetPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_regnet import (
										    FlaxRegNetForImageClassification,
										    FlaxRegNetModel,
										    FlaxRegNetPreTrainedModel,
										)


else:
					import sys

					lowerCamelCase__        =						_LazyModule(__name__, globals()["""__file__"""], _import_structure)