




'''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


UpperCAmelCase_ 		=      get_logger(__name__)


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

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


def 						SCREAMING_SNAKE_CASE       (      a_	:							Optional[Any]				,					a_	:							Any				,					a_	:							Tuple				,					a_	:							Any				,					a_	:							Dict=0			):
		accelerator.wait_for_everyone()
		with FSDP.state_dict_type(
		    SCREAMING_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(SCREAMING_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
						__a			=					f"{MODEL_NAME}.bin" if model_index == 0 else f"{MODEL_NAME}_{model_index}.bin"
						__a			=					os.path.join(SCREAMING_SNAKE_CASE_				,					SCREAMING_SNAKE_CASE_			)
						logger.info(f"Loading model from {input_model_file}"			)
						__a			=					torch.load(SCREAMING_SNAKE_CASE_			)
						logger.info(f"Model loaded from {input_model_file}"			)
				elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
						__a			=					(
						    f"{MODEL_NAME}_rank{accelerator.process_index}.bin"
						    if model_index == 0
						    else f"{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"
						)
						__a			=					os.path.join(SCREAMING_SNAKE_CASE_				,					SCREAMING_SNAKE_CASE_			)
						logger.info(f"Loading model from {input_model_file}"			)
						__a			=					torch.load(SCREAMING_SNAKE_CASE_			)
						logger.info(f"Model loaded from {input_model_file}"			)
				elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
						__a			=					(
						    os.path.join(SCREAMING_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}"			)
						__a			=					{'model': model.state_dict()}
						dist_cp.load_state_dict(
						    state_dict=SCREAMING_SNAKE_CASE_				,					storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE_			)				,					planner=DefaultLoadPlanner()				,					)
						__a			=					state_dict['model']
						logger.info(f"Model loaded from {ckpt_dir}"			)
				model.load_state_dict(SCREAMING_SNAKE_CASE_			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							Dict				,					a_	:							List[str]				,					a_	:							Optional[int]				,					a_	:							List[Any]				,					a_	:							Optional[int]				,					a_	:							Any=0			):
		os.makedirs(SCREAMING_SNAKE_CASE_				,					exist_ok=SCREAMING_SNAKE_CASE_			)
		with FSDP.state_dict_type(
		    SCREAMING_SNAKE_CASE_				,					fsdp_plugin.state_dict_type				,					fsdp_plugin.state_dict_config				,					fsdp_plugin.optim_state_dict_config			):
				__a			=					FSDP.optim_state_dict(SCREAMING_SNAKE_CASE_				,					SCREAMING_SNAKE_CASE_			)
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						if accelerator.process_index == 0:
								__a			=					(
								    f"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else f"{OPTIMIZER_NAME}_{optimizer_index}.bin"
								)
								__a			=					os.path.join(SCREAMING_SNAKE_CASE_				,					SCREAMING_SNAKE_CASE_			)
								logger.info(f"Saving Optimizer state to {output_optimizer_file}"			)
								torch.save(SCREAMING_SNAKE_CASE_				,					SCREAMING_SNAKE_CASE_			)
								logger.info(f"Optimizer state saved in {output_optimizer_file}"			)
				else:
						__a			=					os.path.join(SCREAMING_SNAKE_CASE_				,					f"{OPTIMIZER_NAME}_{optimizer_index}"			)
						os.makedirs(SCREAMING_SNAKE_CASE_				,					exist_ok=SCREAMING_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(SCREAMING_SNAKE_CASE_			)				,					planner=DefaultSavePlanner()				,					)
						logger.info(f"Optimizer state saved in {ckpt_dir}"			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							Dict				,					a_	:							Optional[int]				,					a_	:							str				,					a_	:							str				,					a_	:							Any				,					a_	:							List[str]=0			):
		accelerator.wait_for_everyone()
		with FSDP.state_dict_type(
		    SCREAMING_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:
						__a			=					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:
						__a			=					(
						    f"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else f"{OPTIMIZER_NAME}_{optimizer_index}.bin"
						)
						__a			=					os.path.join(SCREAMING_SNAKE_CASE_				,					SCREAMING_SNAKE_CASE_			)
						logger.info(f"Loading Optimizer state from {input_optimizer_file}"			)
						__a			=					torch.load(SCREAMING_SNAKE_CASE_			)
						logger.info(f"Optimizer state loaded from {input_optimizer_file}"			)
				else:
						__a			=					(
						    os.path.join(SCREAMING_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}"			)
						__a			=					load_sharded_optimizer_state_dict(
						    model_state_dict=model.state_dict()				,					optimizer_key='optimizer'				,					storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE_			)				,					)
						__a			=					optim_state['optimizer']
						logger.info(f"Optimizer loaded from {ckpt_dir}"			)
				__a			=					FSDP.optim_state_dict_to_load(SCREAMING_SNAKE_CASE_				,					SCREAMING_SNAKE_CASE_				,					SCREAMING_SNAKE_CASE_			)
				optimizer.load_state_dict(SCREAMING_SNAKE_CASE_			)









'''simple docstring'''


from ..utils import DummyObject, requires_backends





class    __lowercase						(  metaclass=__magic_name__ ):
   _a							=      ["""onnx"""]







   def __init__(	self      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    str:
     requires_backends(self      ,							['onnx']      )







   @classmethod
   def 		UpperCamelCase__			(	cls      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    Tuple:
     requires_backends(cls      ,							['onnx']      )







   @classmethod
   def 		UpperCamelCase__			(	cls      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    Tuple:
     requires_backends(cls      ,							['onnx']      )



