

"""simple docstring"""




import torch

from accelerate import Accelerator, DistributedType


class 		_A							:





      def __enter__(	self		) ->       Optional[Any]:
         if self.enabled:
            lowerCamelCase__						       =      self.model.no_sync()
            self.model_sync_obj.__enter__()

         return self





      def __exit__(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		) ->       List[str]:
         if self.enabled:
            # Average all models on exit
            self._sync_and_avg_model_params()
            self.model_sync_obj.__exit__(SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__		)





      def __init__(	self     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__     ,    SCREAMING_SNAKE_CASE__ = True		) ->       Dict:
         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__						       =      enabled and accelerator.distributed_type != DistributedType.NO
         lowerCamelCase__						       =      0
         if self.enabled:
            lowerCamelCase__						       =      accelerator
            lowerCamelCase__						       =      model
            lowerCamelCase__						       =      local_sgd_steps





      def        _lowerCamelCase	(	self		) ->       Union[str, 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		) ->       Optional[int]:
         self.accelerator.wait_for_everyone()
         with self.accelerator.autocast():
            for param in self.model.parameters():
               lowerCamelCase__						       =      self.accelerator.reduce(param.data     ,    reduction="mean"		)







"""simple docstring"""




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_  :      Tuple							=      {
    '''configuration_convnextv2''': [
        '''CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ConvNextV2Config''',
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_  :      int							=      [
         '''CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''ConvNextV2ForImageClassification''',
         '''ConvNextV2Model''',
         '''ConvNextV2PreTrainedModel''',
         '''ConvNextV2Backbone''',
     ]


if TYPE_CHECKING:
     from .configuration_convnextva import (
         CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
         ConvNextVaConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_convnextva import (
              CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
              ConvNextVaBackbone,
              ConvNextVaForImageClassification,
              ConvNextVaModel,
              ConvNextVaPreTrainedModel,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE_  :      Dict							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure)




