




'''simple docstring'''





import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer


def 					UpperCamelCase_(     snake_case							:	str							,    snake_case							:	str							,    **snake_case							:	int					):

       '''simple docstring'''






       snake_case_      =				AutoConfig.from_pretrained(snake_case__							,    **snake_case__					)
       snake_case_      =				AutoModelForSeqaSeqLM.from_config(snake_case__					)
       model.save_pretrained(snake_case__					)
       AutoTokenizer.from_pretrained(snake_case__					).save_pretrained(snake_case__					)
       return model


if __name__ == "__main__":
  fire.Fire(save_randomly_initialized_version)










'''simple docstring'''





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


_SCREAMING_SNAKE_CASE							:       Tuple						=			get_logger(__name__)


def 					UpperCamelCase_(     snake_case							:	Optional[Any]							,    snake_case							:	Optional[Any]							,    snake_case							:	Optional[Any]							,    snake_case							:	int							,    snake_case							:	List[Any]=0					):

							'''simple docstring'''






							os.makedirs(snake_case							,    exist_ok=snake_case					)
							with FSDP.state_dict_type(
							    snake_case							,    fsdp_plugin.state_dict_type							,    fsdp_plugin.state_dict_config							,    fsdp_plugin.optim_state_dict_config					):
														snake_case_      =				model.state_dict()
														if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
																					snake_case_      =				f'{MODEL_NAME}.bin' if model_index == 0 else f'{MODEL_NAME}_{model_index}.bin'
																					snake_case_      =				os.path.join(snake_case							,    snake_case					)
																					if accelerator.process_index == 0:
																												logger.info(f'Saving model to {output_model_file}'					)
																												torch.save(snake_case							,    snake_case					)
																												logger.info(f'Model saved to {output_model_file}'					)
														elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
																					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'
																					)
																					snake_case_      =				os.path.join(snake_case							,    snake_case					)
																					logger.info(f'Saving model to {output_model_file}'					)
																					torch.save(snake_case							,    snake_case					)
																					logger.info(f'Model saved to {output_model_file}'					)
														elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
																					snake_case_      =				os.path.join(snake_case							,    f'{MODEL_NAME}_{model_index}'					)
																					os.makedirs(snake_case							,    exist_ok=snake_case					)
																					logger.info(f'Saving model to {ckpt_dir}'					)
																					snake_case_      =				{"model": state_dict}

																					dist_cp.save_state_dict(
																					    state_dict=snake_case							,    storage_writer=dist_cp.FileSystemWriter(snake_case					)							,    planner=DefaultSavePlanner()							,    )
																					logger.info(f'Model saved to {ckpt_dir}'					)


def 					UpperCamelCase_(     snake_case							:	Optional[int]							,    snake_case							:	Optional[Any]							,    snake_case							:	int							,    snake_case							:	Union[str, Any]							,    snake_case							:	Any=0					):

							'''simple docstring'''






							accelerator.wait_for_everyone()
							with FSDP.state_dict_type(
							    snake_case							,    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(snake_case					) != 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
																					snake_case_      =				f'{MODEL_NAME}.bin' if model_index == 0 else f'{MODEL_NAME}_{model_index}.bin'
																					snake_case_      =				os.path.join(snake_case							,    snake_case					)
																					logger.info(f'Loading model from {input_model_file}'					)
																					snake_case_      =				torch.load(snake_case					)
																					logger.info(f'Model loaded from {input_model_file}'					)
														elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
																					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'
																					)
																					snake_case_      =				os.path.join(snake_case							,    snake_case					)
																					logger.info(f'Loading model from {input_model_file}'					)
																					snake_case_      =				torch.load(snake_case					)
																					logger.info(f'Model loaded from {input_model_file}'					)
														elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
																					snake_case_      =				(
																					    os.path.join(snake_case							,    f'{MODEL_NAME}_{model_index}'					)
																					    if f'{MODEL_NAME}' not in input_dir
																					    else input_dir
																					)
																					logger.info(f'Loading model from {ckpt_dir}'					)
																					snake_case_      =				{"model": model.state_dict()}
																					dist_cp.load_state_dict(
																					    state_dict=snake_case							,    storage_reader=dist_cp.FileSystemReader(snake_case					)							,    planner=DefaultLoadPlanner()							,    )
																					snake_case_      =				state_dict["model"]
																					logger.info(f'Model loaded from {ckpt_dir}'					)
														model.load_state_dict(snake_case					)


def 					UpperCamelCase_(     snake_case							:	str							,    snake_case							:	List[str]							,    snake_case							:	Any							,    snake_case							:	Tuple							,    snake_case							:	Optional[Any]							,    snake_case							:	Tuple=0					):

							'''simple docstring'''






							os.makedirs(snake_case							,    exist_ok=snake_case					)
							with FSDP.state_dict_type(
							    snake_case							,    fsdp_plugin.state_dict_type							,    fsdp_plugin.state_dict_config							,    fsdp_plugin.optim_state_dict_config					):
														snake_case_      =				FSDP.optim_state_dict(snake_case							,    snake_case					)
														if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
																					if accelerator.process_index == 0:
																												snake_case_      =				(
																												    f'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else f'{OPTIMIZER_NAME}_{optimizer_index}.bin'
																												)
																												snake_case_      =				os.path.join(snake_case							,    snake_case					)
																												logger.info(f'Saving Optimizer state to {output_optimizer_file}'					)
																												torch.save(snake_case							,    snake_case					)
																												logger.info(f'Optimizer state saved in {output_optimizer_file}'					)
														else:
																					snake_case_      =				os.path.join(snake_case							,    f'{OPTIMIZER_NAME}_{optimizer_index}'					)
																					os.makedirs(snake_case							,    exist_ok=snake_case					)
																					logger.info(f'Saving Optimizer state to {ckpt_dir}'					)
																					dist_cp.save_state_dict(
																					    state_dict={"optimizer": optim_state}							,    storage_writer=dist_cp.FileSystemWriter(snake_case					)							,    planner=DefaultSavePlanner()							,    )
																					logger.info(f'Optimizer state saved in {ckpt_dir}'					)




def 					UpperCamelCase_(     snake_case							:	Optional[Any]							,    snake_case							:	List[str]							,    snake_case							:	Union[str, Any]							,    snake_case							:	int							,    snake_case							:	Optional[int]							,    snake_case							:	Union[str, Any]=0					):

							'''simple docstring'''






							accelerator.wait_for_everyone()
							with FSDP.state_dict_type(
							    snake_case							,    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:
																					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:
																					snake_case_      =				(
																					    f'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else f'{OPTIMIZER_NAME}_{optimizer_index}.bin'
																					)
																					snake_case_      =				os.path.join(snake_case							,    snake_case					)
																					logger.info(f'Loading Optimizer state from {input_optimizer_file}'					)
																					snake_case_      =				torch.load(snake_case					)
																					logger.info(f'Optimizer state loaded from {input_optimizer_file}'					)
														else:
																					snake_case_      =				(
																					    os.path.join(snake_case							,    f'{OPTIMIZER_NAME}_{optimizer_index}'					)
																					    if f'{OPTIMIZER_NAME}' not in input_dir
																					    else input_dir
																					)
																					logger.info(f'Loading Optimizer from {ckpt_dir}'					)
																					snake_case_      =				load_sharded_optimizer_state_dict(
																					    model_state_dict=model.state_dict()							,    optimizer_key="optimizer"							,    storage_reader=dist_cp.FileSystemReader(snake_case					)							,    )
																					snake_case_      =				optim_state["optimizer"]
																					logger.info(f'Optimizer loaded from {ckpt_dir}'					)
														snake_case_      =				FSDP.optim_state_dict_to_load(snake_case							,    snake_case							,    snake_case					)
														optimizer.load_state_dict(snake_case					)




