





import torch

from accelerate import Accelerator, DistributedType


class 		_SCREAMING_SNAKE_CASE    :


     def __enter__(     self						):
       if self.enabled:
         _UpperCAmelCase		:       int       =    self.model.no_sync()
         self.model_sync_obj.__enter__()

       return self


     def __exit__(     self       ,				A_       ,				A_       ,				A_						):
       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       ,				A_       ,				A_       ,				A_       ,				A_ = 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		:       str       =    enabled and accelerator.distributed_type != DistributedType.NO
       _UpperCAmelCase		:       Optional[Any]       =    0
       if self.enabled:
         _UpperCAmelCase		:       Optional[Any]       =    accelerator
         _UpperCAmelCase		:       Dict       =    model
         _UpperCAmelCase		:       Any       =    local_sgd_steps


     def 					__snake_case(     self						):
       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						):
       self.accelerator.wait_for_everyone()
       with self.accelerator.autocast():
         for param in self.model.parameters():
           _UpperCAmelCase		:       List[str]       =    self.accelerator.reduce(param.data       ,				reduction="""mean"""						)









from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__				:    List[str]         =    {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__				:    int         =    [
     'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'FlaubertForMultipleChoice',
     'FlaubertForQuestionAnswering',
     'FlaubertForQuestionAnsweringSimple',
     'FlaubertForSequenceClassification',
     'FlaubertForTokenClassification',
     'FlaubertModel',
     'FlaubertWithLMHeadModel',
     'FlaubertPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE__				:    Dict         =    [
     'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFFlaubertForMultipleChoice',
     'TFFlaubertForQuestionAnsweringSimple',
     'TFFlaubertForSequenceClassification',
     'TFFlaubertForTokenClassification',
     'TFFlaubertModel',
     'TFFlaubertPreTrainedModel',
     'TFFlaubertWithLMHeadModel',
 ]


if TYPE_CHECKING:
 from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
 from .tokenization_flaubert import FlaubertTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flaubert import (
      FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      FlaubertForMultipleChoice,
      FlaubertForQuestionAnswering,
      FlaubertForQuestionAnsweringSimple,
      FlaubertForSequenceClassification,
      FlaubertForTokenClassification,
      FlaubertModel,
      FlaubertPreTrainedModel,
      FlaubertWithLMHeadModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_flaubert import (
      TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFFlaubertForMultipleChoice,
      TFFlaubertForQuestionAnsweringSimple,
      TFFlaubertForSequenceClassification,
      TFFlaubertForTokenClassification,
      TFFlaubertModel,
      TFFlaubertPreTrainedModel,
      TFFlaubertWithLMHeadModel,
  )

else:
 import sys

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





