




import torch

from accelerate import Accelerator, DistributedType

class        _lowerCamelCase  :

      """simple docstring"""

      def __enter__(   self    :       Tuple ):
          if self.enabled:
              __UpperCamelCase     						=  self.model.no_sync()
              self.model_sync_obj.__enter__()

          return self







      def __exit__(   self    :       List[str]       ,				snake_case    :       Any       ,				snake_case    :       List[Any]       ,				snake_case    :       Optional[int] ):
          if self.enabled:
              # Average all models on exit
              self._sync_and_avg_model_params()
              self.model_sync_obj.__exit__(snake_case       ,				snake_case       ,				snake_case )







      def __init__(   self    :       Optional[int]       ,				snake_case    :       Accelerator       ,				snake_case    :       torch.nn.Module       ,				snake_case    :       int       ,				snake_case    :       bool = True ):
          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)''' )
          __UpperCamelCase     						=  enabled and accelerator.distributed_type != DistributedType.NO
          __UpperCamelCase     						=  0
          if self.enabled:
              __UpperCamelCase     						=  accelerator
              __UpperCamelCase     						=  model
              __UpperCamelCase     						=  local_sgd_steps







      def 					snake_case		(   self    :       List[Any] ):
          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 					snake_case		(   self    :       Dict ):
          self.accelerator.wait_for_everyone()
          with self.accelerator.autocast():
              for param in self.model.parameters():
                  __UpperCamelCase     						=  self.accelerator.reduce(param.data       ,				reduction='''mean''' )












'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCamelCase							:						Optional[Any]								=  {'configuration_bit': ['BIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BitConfig', 'BitOnnxConfig']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCamelCase							:						Dict								=  [
						    'BIT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'BitForImageClassification',
						    'BitModel',
						    'BitPreTrainedModel',
						    'BitBackbone',
						]


try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCamelCase							:						Union[str, Any]								=  ['BitImageProcessor']


if TYPE_CHECKING:
						from .configuration_bit import BIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BitConfig, BitOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bit import (
												    BIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BitBackbone,
												    BitForImageClassification,
												    BitModel,
												    BitPreTrainedModel,
												)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .image_processing_bit import BitImageProcessor

else:
						import sys

						_lowerCamelCase							:						Optional[Any]								=  _LazyModule(__name__, globals()['__file__'], _import_structure)





