



"""simple docstring"""


import torch

from accelerate import Accelerator, DistributedType







class  A_     :



						"""simple docstring"""






						def __enter__(   self					:str   )							->     int:
												if self.enabled:
																		UpperCAmelCase			 =					self.model.no_sync()
																		self.model_sync_obj.__enter__()

												return self






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






						def __init__(   self					:Optional[int]  ,    lowercase_					:Accelerator  ,    lowercase_					:torch.nn.Module  ,    lowercase_					:int  ,    lowercase_					:bool = True   )							->     Optional[int]:
												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			 =					enabled and accelerator.distributed_type != DistributedType.NO
												UpperCAmelCase			 =					0
												if self.enabled:
																		UpperCAmelCase			 =					accelerator
																		UpperCAmelCase			 =					model
																		UpperCAmelCase			 =					local_sgd_steps






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









"""simple docstring"""






from typing import TYPE_CHECKING

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


_lowerCamelCase  =	{
    'configuration_open_llama': ['OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenLlamaConfig'],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	['LlamaTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	['LlamaTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	[
	    'OpenLlamaForCausalLM',
	    'OpenLlamaModel',
	    'OpenLlamaPreTrainedModel',
	    'OpenLlamaForSequenceClassification',
	]


if TYPE_CHECKING:
	from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from transformers import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from transformers import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_open_llama import (
		    OpenLlamaForCausalLM,
		    OpenLlamaForSequenceClassification,
		    OpenLlamaModel,
		    OpenLlamaPreTrainedModel,
		)


else:
	import sys

	_lowerCamelCase  =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
