from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase      :				int     			={
    "configuration_fsmt": ["FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP", "FSMTConfig"],
    "tokenization_fsmt": ["FSMTTokenizer"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase      :				int     			=["FSMTForConditionalGeneration", "FSMTModel", "PretrainedFSMTModel"]


if TYPE_CHECKING:
    from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
    from .tokenization_fsmt import FSMTTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_fsmt import FSMTForConditionalGeneration, FSMTModel, PretrainedFSMTModel

else:
    import sys

    lowerCAmelCase      :				Dict     			=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





import torch

from accelerate import Accelerator, DistributedType




class 						__snake_case						:





      '''simple docstring'''






      def __enter__(       self  :					Union[str, Any])					->Dict:


          """simple docstring"""


          if self.enabled:
              _lowerCamelCase  :		str			    =    self.model.no_sync()
              self.model_sync_obj.__enter__()

          return self



      def __exit__(       self  :					List[str]     ,	_UpperCamelCase  :					Any     ,	_UpperCamelCase  :					Union[str, Any]     ,	_UpperCamelCase  :					int)					->Optional[Any]:


          """simple docstring"""


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



      def __init__(       self  :					Union[str, Any]     ,	_UpperCamelCase  :					Accelerator     ,	_UpperCamelCase  :					torch.nn.Module     ,	_UpperCamelCase  :					int     ,	_UpperCamelCase  :					bool = True)					->int:


          """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  :		str			    =    enabled and accelerator.distributed_type != DistributedType.NO
          _lowerCamelCase  :		Optional[int]			    =    0
          if self.enabled:
              _lowerCamelCase  :		Union[str, Any]			    =    accelerator
              _lowerCamelCase  :		int			    =    model
              _lowerCamelCase  :		int			    =    local_sgd_steps



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any])					->Union[str, 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 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any])					->List[str]:


          """simple docstring"""


          self.accelerator.wait_for_everyone()
          with self.accelerator.autocast():
              for param in self.model.parameters():
                  _lowerCamelCase  :		Union[str, Any]			    =    self.accelerator.reduce(param.data     ,	reduction="""mean""")




