'''simple docstring'''






import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


_UpperCAmelCase						:    str     					=					logging.get_logger(__name__)

_UpperCAmelCase						:    Optional[Any]     					=					{
    '''google/vit-hybrid-base-bit-384''': '''https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json''',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}







class lowercase_					(		_UpperCamelCase ):







   """simple docstring"""

   __lowerCAmelCase    						=      "vit-hybrid"
   def __init__(			self			: List[str],   UpperCamelCase__			: Optional[Any]=None,   UpperCamelCase__			: Dict=7_68,   UpperCamelCase__			: Any=12,   UpperCamelCase__			: List[str]=12,   UpperCamelCase__			: Tuple=30_72,   UpperCamelCase__			: List[str]="gelu",   UpperCamelCase__			: Union[str, Any]=0.0,   UpperCamelCase__			: Union[str, Any]=0.0,   UpperCamelCase__			: Optional[int]=0.02,   UpperCamelCase__			: Tuple=1e-12,   UpperCamelCase__			: Optional[Any]=2_24,   UpperCamelCase__			: str=1,   UpperCamelCase__			: Optional[int]=3,   UpperCamelCase__			: Optional[int]=[1, 10_24, 24, 24],   UpperCamelCase__			: Tuple=True,   **UpperCamelCase__			: Dict,   )     -> Union[str, Any]:
        super().__init__(**UpperCamelCase__    )

        if backbone_config is None:
             logger.info('`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'    )
             _A								=     {
                 'global_padding': 'same',
                 'layer_type': 'bottleneck',
                 'depths': [3, 4, 9],
                 'out_features': ['stage3'],
                 'embedding_dynamic_padding': True,
             }

        if isinstance(UpperCamelCase__,   UpperCamelCase__    ):
             if "model_type" in backbone_config:
                  _A								=     CONFIG_MAPPING[backbone_config['model_type']]
             else:
                  logger.info(
                      '`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'    )
                  _A								=     BitConfig
             _A								=     backbone_config_class(**UpperCamelCase__    )

        _A								=     backbone_featmap_shape
        _A								=     backbone_config
        _A								=     hidden_size
        _A								=     num_hidden_layers
        _A								=     num_attention_heads
        _A								=     intermediate_size
        _A								=     hidden_act
        _A								=     hidden_dropout_prob
        _A								=     attention_probs_dropout_prob
        _A								=     initializer_range
        _A								=     layer_norm_eps
        _A								=     image_size
        _A								=     patch_size
        _A								=     num_channels
        _A								=     qkv_bias

   def   __UpperCAmelCase      (			self			: Optional[int]    )     -> Dict[str, any]:
        _A								=     copy.deepcopy(self.__dict__    )
        _A								=     self.backbone_config.to_dict()
        _A								=     self.__class__.model_type
        return output




'''simple docstring'''






import torch

from accelerate import Accelerator, DistributedType







class lowercase_					:







   """simple docstring"""

   def __enter__(			self			: Optional[int]    )     -> Union[str, Any]:
        if self.enabled:
             _A								=     self.model.no_sync()
             self.model_sync_obj.__enter__()

        return self
   def __exit__(			self			: Dict,   UpperCamelCase__			: Any,   UpperCamelCase__			: int,   UpperCamelCase__			: Any    )     -> str:
        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    )     -> str:
        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   __UpperCAmelCase      (			self			: Any    )     -> int:
        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   __UpperCAmelCase      (			self			: Dict    )     -> int:
        self.accelerator.wait_for_everyone()
        with self.accelerator.autocast():
             for param in self.model.parameters():
                  _A								=     self.accelerator.reduce(param.data,   reduction='mean'    )



