





import torch

from accelerate import Accelerator, DistributedType





class A_   :




   def __enter__(self      :List[Any]					)->   int:
         if self.enabled:
               __A			  =							self.model.no_sync()
               self.model_sync_obj.__enter__()

         return self




   def __exit__(self      :str       ,  _UpperCamelCase      :Tuple       ,  _UpperCamelCase      :Optional[int]       ,  _UpperCamelCase      :int					)->   Dict:
         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      :Optional[int]       ,  _UpperCamelCase      :Accelerator       ,  _UpperCamelCase      :torch.nn.Module       ,  _UpperCamelCase      :int       ,  _UpperCamelCase      :bool = True					)->   Optional[Any]:
         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)'''					)
         __A			  =							enabled and accelerator.distributed_type != DistributedType.NO
         __A			  =							0
         if self.enabled:
               __A			  =							accelerator
               __A			  =							model
               __A			  =							local_sgd_steps




   def _lowerCAmelCase (self      :Optional[Any]					)->   Optional[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 _lowerCAmelCase (self      :Union[str, Any]					)->   List[Any]:
         self.accelerator.wait_for_everyone()
         with self.accelerator.autocast():
               for param in self.model.parameters():
                     __A			  =							self.accelerator.reduce(param.data       ,  reduction='''mean'''					)












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case__   :				Any						=	{
    'configuration_camembert': ['CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CamembertConfig', 'CamembertOnnxConfig'],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				List[Any]						=	['CamembertTokenizer']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Dict						=	['CamembertTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Any						=	[
           'CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'CamembertForCausalLM',
           'CamembertForMaskedLM',
           'CamembertForMultipleChoice',
           'CamembertForQuestionAnswering',
           'CamembertForSequenceClassification',
           'CamembertForTokenClassification',
           'CamembertModel',
           'CamembertPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__   :				Any						=	[
           'TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFCamembertForCausalLM',
           'TFCamembertForMaskedLM',
           'TFCamembertForMultipleChoice',
           'TFCamembertForQuestionAnswering',
           'TFCamembertForSequenceClassification',
           'TFCamembertForTokenClassification',
           'TFCamembertModel',
           'TFCamembertPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert import CamembertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_camembert_fast import CamembertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_camembert import (
                  CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CamembertForCausalLM,
                  CamembertForMaskedLM,
                  CamembertForMultipleChoice,
                  CamembertForQuestionAnswering,
                  CamembertForSequenceClassification,
                  CamembertForTokenClassification,
                  CamembertModel,
                  CamembertPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_camembert import (
                  TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFCamembertForCausalLM,
                  TFCamembertForMaskedLM,
                  TFCamembertForMultipleChoice,
                  TFCamembertForQuestionAnswering,
                  TFCamembertForSequenceClassification,
                  TFCamembertForTokenClassification,
                  TFCamembertModel,
                  TFCamembertPreTrainedModel,
              )

else:
       import sys

       snake_case__   :				Any						=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





