
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase								=					logging.get_logger(__name__)

__lowerCamelCase								=					{
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
}
class    _UpperCamelCase(   SCREAMING_SNAKE_CASE				):
						__A:						Optional[Any]				   =      """qdqbert"""

						def __init__(					self		:			Any	,						_lowerCamelCase		:			Any=3_05_22	,						_lowerCamelCase		:			Dict=7_68	,						_lowerCamelCase		:			str=12	,						_lowerCamelCase		:			Dict=12	,						_lowerCamelCase		:			Tuple=30_72	,						_lowerCamelCase		:			int="gelu"	,						_lowerCamelCase		:			List[str]=0.1	,						_lowerCamelCase		:			Tuple=0.1	,						_lowerCamelCase		:			Optional[Any]=5_12	,						_lowerCamelCase		:			Any=2	,						_lowerCamelCase		:			Optional[int]=0.02	,						_lowerCamelCase		:			str=1E-12	,						_lowerCamelCase		:			Union[str, Any]=True	,						_lowerCamelCase		:			Optional[int]=1	,						_lowerCamelCase		:			Optional[Any]=0	,						_lowerCamelCase		:			Any=2	,						**_lowerCamelCase		:			str	,						):
													super().__init__(pad_token_id=_lowerCamelCase	,						bos_token_id=_lowerCamelCase	,						eos_token_id=_lowerCamelCase	,						**_lowerCamelCase			)

													_UpperCAmelCase  :			str	       =		vocab_size
													_UpperCAmelCase  :			Any	       =		max_position_embeddings
													_UpperCAmelCase  :			List[Any]	       =		hidden_size
													_UpperCAmelCase  :			Optional[int]	       =		num_hidden_layers
													_UpperCAmelCase  :			Union[str, Any]	       =		num_attention_heads
													_UpperCAmelCase  :			List[str]	       =		intermediate_size
													_UpperCAmelCase  :			Union[str, Any]	       =		hidden_act
													_UpperCAmelCase  :			List[Any]	       =		hidden_dropout_prob
													_UpperCAmelCase  :			Optional[Any]	       =		attention_probs_dropout_prob
													_UpperCAmelCase  :			Optional[int]	       =		initializer_range
													_UpperCAmelCase  :			List[Any]	       =		type_vocab_size
													_UpperCAmelCase  :			Any	       =		layer_norm_eps
													_UpperCAmelCase  :			Optional[int]	       =		use_cache






import torch

from accelerate import Accelerator, DistributedType
class    _UpperCamelCase:

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

													return self

						def __exit__(					self		:			str	,						_lowerCamelCase		:			Union[str, Any]	,						_lowerCamelCase		:			Tuple	,						_lowerCamelCase		:			Optional[Any]			):
													if self.enabled:
																				# Average all models on exit
																				self._sync_and_avg_model_params()
																				self.model_sync_obj.__exit__(_lowerCamelCase	,						_lowerCamelCase	,						_lowerCamelCase			)

						def __init__(					self		:			str	,						_lowerCamelCase		:			Accelerator	,						_lowerCamelCase		:			torch.nn.Module	,						_lowerCamelCase		:			int	,						_lowerCamelCase		:			bool = 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  :			Any	       =		enabled and accelerator.distributed_type != DistributedType.NO
													_UpperCAmelCase  :			str	       =		0
													if self.enabled:
																				_UpperCAmelCase  :			Tuple	       =		accelerator
																				_UpperCAmelCase  :			List[str]	       =		model
																				_UpperCAmelCase  :			Dict	       =		local_sgd_steps

						def 						a__ (					self		:			Optional[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 						a__ (					self		:			Tuple			):
													self.accelerator.wait_for_everyone()
													with self.accelerator.autocast():
																				for param in self.model.parameters():
																											_UpperCAmelCase  :			Optional[Any]	       =		self.accelerator.reduce(param.data	,						reduction="mean"			)




