

import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version(""">=""", FSDP_PYTORCH_VERSION):
		import torch.distributed.checkpoint as dist_cp
		from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
		from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
		from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
		from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


__A						:							Union[str, Any]							=  get_logger(__name__)
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=0   ):
			'''simple docstring'''
			os.makedirs(_lowerCamelCase					,	exist_ok=_lowerCamelCase   )
			with FSDP.state_dict_type(
			    _lowerCamelCase					,	fsdp_plugin.state_dict_type					,	fsdp_plugin.state_dict_config					,	fsdp_plugin.optim_state_dict_config   ):
						SCREAMING_SNAKE_CASE           =					model.state_dict()
						if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
									SCREAMING_SNAKE_CASE           =					f"""{MODEL_NAME}.bin""" if model_index == 0 else f"""{MODEL_NAME}_{model_index}.bin"""
									SCREAMING_SNAKE_CASE           =					os.path.join(_lowerCamelCase					,	_lowerCamelCase   )
									if accelerator.process_index == 0:
												logger.info(f"""Saving model to {output_model_file}"""   )
												torch.save(_lowerCamelCase					,	_lowerCamelCase   )
												logger.info(f"""Model saved to {output_model_file}"""   )
						elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
									SCREAMING_SNAKE_CASE           =					(
									    f"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
									    if model_index == 0
									    else f"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
									)
									SCREAMING_SNAKE_CASE           =					os.path.join(_lowerCamelCase					,	_lowerCamelCase   )
									logger.info(f"""Saving model to {output_model_file}"""   )
									torch.save(_lowerCamelCase					,	_lowerCamelCase   )
									logger.info(f"""Model saved to {output_model_file}"""   )
						elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
									SCREAMING_SNAKE_CASE           =					os.path.join(_lowerCamelCase					,	f"""{MODEL_NAME}_{model_index}"""   )
									os.makedirs(_lowerCamelCase					,	exist_ok=_lowerCamelCase   )
									logger.info(f"""Saving model to {ckpt_dir}"""   )
									SCREAMING_SNAKE_CASE           =					{'model': state_dict}

									dist_cp.save_state_dict(
									    state_dict=_lowerCamelCase					,	storage_writer=dist_cp.FileSystemWriter(_lowerCamelCase   )					,	planner=DefaultSavePlanner()					,	)
									logger.info(f"""Model saved to {ckpt_dir}"""   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=0   ):
			'''simple docstring'''
			accelerator.wait_for_everyone()
			with FSDP.state_dict_type(
			    _lowerCamelCase					,	fsdp_plugin.state_dict_type					,	fsdp_plugin.state_dict_config					,	fsdp_plugin.optim_state_dict_config   ):
						if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
									if type(_lowerCamelCase   ) != FSDP and accelerator.process_index != 0:
												if not fsdp_plugin.sync_module_states:
															raise ValueError(
															    """Set the `sync_module_states` flag to `True` so that model states are synced across processes when """
															    """initializing FSDP object"""   )
												return
									SCREAMING_SNAKE_CASE           =					f"""{MODEL_NAME}.bin""" if model_index == 0 else f"""{MODEL_NAME}_{model_index}.bin"""
									SCREAMING_SNAKE_CASE           =					os.path.join(_lowerCamelCase					,	_lowerCamelCase   )
									logger.info(f"""Loading model from {input_model_file}"""   )
									SCREAMING_SNAKE_CASE           =					torch.load(_lowerCamelCase   )
									logger.info(f"""Model loaded from {input_model_file}"""   )
						elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
									SCREAMING_SNAKE_CASE           =					(
									    f"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
									    if model_index == 0
									    else f"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
									)
									SCREAMING_SNAKE_CASE           =					os.path.join(_lowerCamelCase					,	_lowerCamelCase   )
									logger.info(f"""Loading model from {input_model_file}"""   )
									SCREAMING_SNAKE_CASE           =					torch.load(_lowerCamelCase   )
									logger.info(f"""Model loaded from {input_model_file}"""   )
						elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
									SCREAMING_SNAKE_CASE           =					(
									    os.path.join(_lowerCamelCase					,	f"""{MODEL_NAME}_{model_index}"""   )
									    if f"""{MODEL_NAME}""" not in input_dir
									    else input_dir
									)
									logger.info(f"""Loading model from {ckpt_dir}"""   )
									SCREAMING_SNAKE_CASE           =					{'model': model.state_dict()}
									dist_cp.load_state_dict(
									    state_dict=_lowerCamelCase					,	storage_reader=dist_cp.FileSystemReader(_lowerCamelCase   )					,	planner=DefaultLoadPlanner()					,	)
									SCREAMING_SNAKE_CASE           =					state_dict['model']
									logger.info(f"""Model loaded from {ckpt_dir}"""   )
						model.load_state_dict(_lowerCamelCase   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=0   ):
			'''simple docstring'''
			os.makedirs(_lowerCamelCase					,	exist_ok=_lowerCamelCase   )
			with FSDP.state_dict_type(
			    _lowerCamelCase					,	fsdp_plugin.state_dict_type					,	fsdp_plugin.state_dict_config					,	fsdp_plugin.optim_state_dict_config   ):
						SCREAMING_SNAKE_CASE           =					FSDP.optim_state_dict(_lowerCamelCase					,	_lowerCamelCase   )
						if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
									if accelerator.process_index == 0:
												SCREAMING_SNAKE_CASE           =					(
												    f"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else f"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
												)
												SCREAMING_SNAKE_CASE           =					os.path.join(_lowerCamelCase					,	_lowerCamelCase   )
												logger.info(f"""Saving Optimizer state to {output_optimizer_file}"""   )
												torch.save(_lowerCamelCase					,	_lowerCamelCase   )
												logger.info(f"""Optimizer state saved in {output_optimizer_file}"""   )
						else:
									SCREAMING_SNAKE_CASE           =					os.path.join(_lowerCamelCase					,	f"""{OPTIMIZER_NAME}_{optimizer_index}"""   )
									os.makedirs(_lowerCamelCase					,	exist_ok=_lowerCamelCase   )
									logger.info(f"""Saving Optimizer state to {ckpt_dir}"""   )
									dist_cp.save_state_dict(
									    state_dict={"""optimizer""": optim_state}					,	storage_writer=dist_cp.FileSystemWriter(_lowerCamelCase   )					,	planner=DefaultSavePlanner()					,	)
									logger.info(f"""Optimizer state saved in {ckpt_dir}"""   )




def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=0   ):
			'''simple docstring'''
			accelerator.wait_for_everyone()
			with FSDP.state_dict_type(
			    _lowerCamelCase					,	fsdp_plugin.state_dict_type					,	fsdp_plugin.state_dict_config					,	fsdp_plugin.optim_state_dict_config   ):
						if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
									SCREAMING_SNAKE_CASE           =					None
									# below check should work but currently it isn't working (mostly opytorch issue),
									# in the meantime disabling it at the cost of excess memory usage
									# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
									SCREAMING_SNAKE_CASE           =					(
									    f"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else f"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
									)
									SCREAMING_SNAKE_CASE           =					os.path.join(_lowerCamelCase					,	_lowerCamelCase   )
									logger.info(f"""Loading Optimizer state from {input_optimizer_file}"""   )
									SCREAMING_SNAKE_CASE           =					torch.load(_lowerCamelCase   )
									logger.info(f"""Optimizer state loaded from {input_optimizer_file}"""   )
						else:
									SCREAMING_SNAKE_CASE           =					(
									    os.path.join(_lowerCamelCase					,	f"""{OPTIMIZER_NAME}_{optimizer_index}"""   )
									    if f"""{OPTIMIZER_NAME}""" not in input_dir
									    else input_dir
									)
									logger.info(f"""Loading Optimizer from {ckpt_dir}"""   )
									SCREAMING_SNAKE_CASE           =					load_sharded_optimizer_state_dict(
									    model_state_dict=model.state_dict()					,	optimizer_key="""optimizer"""					,	storage_reader=dist_cp.FileSystemReader(_lowerCamelCase   )					,	)
									SCREAMING_SNAKE_CASE           =					optim_state['optimizer']
									logger.info(f"""Optimizer loaded from {ckpt_dir}"""   )
						SCREAMING_SNAKE_CASE           =					FSDP.optim_state_dict_to_load(_lowerCamelCase					,	_lowerCamelCase					,	_lowerCamelCase   )
						optimizer.load_state_dict(_lowerCamelCase   )












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


__A						:							Any							=  logging.get_logger(__name__)

__A						:							int							=  {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     '''megatron-bert'''





							def __init__(					self       :	str       ,     a       :	Union[str, Any]=29_056       ,     a       :	Optional[int]=1_024       ,     a       :	str=24       ,     a       :	str=16       ,     a       :	str=4_096       ,     a       :	Any="gelu"       ,     a       :	Any=0.1       ,     a       :	Optional[int]=0.1       ,     a       :	Optional[int]=512       ,     a       :	Optional[Any]=2       ,     a       :	List[str]=0.02       ,     a       :	List[Any]=1E-1_2       ,     a       :	Any=0       ,     a       :	List[Any]="absolute"       ,     a       :	List[str]=True       ,     **a       :	Optional[Any]       ,     )			->					List[str]:
										super().__init__(pad_token_id=a       ,     **a			)

										SCREAMING_SNAKE_CASE           =					vocab_size
										SCREAMING_SNAKE_CASE           =					hidden_size
										SCREAMING_SNAKE_CASE           =					num_hidden_layers
										SCREAMING_SNAKE_CASE           =					num_attention_heads
										SCREAMING_SNAKE_CASE           =					hidden_act
										SCREAMING_SNAKE_CASE           =					intermediate_size
										SCREAMING_SNAKE_CASE           =					hidden_dropout_prob
										SCREAMING_SNAKE_CASE           =					attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE           =					max_position_embeddings
										SCREAMING_SNAKE_CASE           =					type_vocab_size
										SCREAMING_SNAKE_CASE           =					initializer_range
										SCREAMING_SNAKE_CASE           =					layer_norm_eps
										SCREAMING_SNAKE_CASE           =					position_embedding_type
										SCREAMING_SNAKE_CASE           =					use_cache






