






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			:	Optional[Any]								=get_logger(__name__)



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

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



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



def  a__ (__lowercase							:Optional[int]					,  __lowercase							:int					,  __lowercase							:Any					,  __lowercase							:str					,  __lowercase							:List[str]					,  __lowercase							:Dict=0   )		-> List[Any]:
				os.makedirs(__lowercase					,  exist_ok=__lowercase   )
				with FSDP.state_dict_type(
				    __lowercase					,  fsdp_plugin.state_dict_type					,  fsdp_plugin.state_dict_config					,  fsdp_plugin.optim_state_dict_config   ):
								_A	:				List[Any]   =      FSDP.optim_state_dict(__lowercase					,  __lowercase   )
								if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
												if accelerator.process_index == 0:
																_A	:				Optional[int]   =      (
																    f"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else f"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
																)
																_A	:				Dict   =      os.path.join(__lowercase					,  __lowercase   )
																logger.info(f"""Saving Optimizer state to {output_optimizer_file}"""   )
																torch.save(__lowercase					,  __lowercase   )
																logger.info(f"""Optimizer state saved in {output_optimizer_file}"""   )
								else:
												_A	:				Dict   =      os.path.join(__lowercase					,  f"""{OPTIMIZER_NAME}_{optimizer_index}"""   )
												os.makedirs(__lowercase					,  exist_ok=__lowercase   )
												logger.info(f"""Saving Optimizer state to {ckpt_dir}"""   )
												dist_cp.save_state_dict(
												    state_dict={'''optimizer''': optim_state}					,  storage_writer=dist_cp.FileSystemWriter(__lowercase   )					,  planner=DefaultSavePlanner()					,  )
												logger.info(f"""Optimizer state saved in {ckpt_dir}"""   )






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











from __future__ import annotations



def  a__ (__lowercase							:list					,  __lowercase							:int | None = None					,  __lowercase							:int | None = None   )		-> None:
				if start is None:
								_A	:				Optional[int]   =      0

				if end is None:
								_A	:				Union[str, Any]   =      len(__lowercase   ) - 1

				if start >= end:
								return

				_A	:				Union[str, Any]   =      (start + end) // 2

				slowsort(__lowercase					,  __lowercase					,  __lowercase   )
				slowsort(__lowercase					,  mid + 1					,  __lowercase   )

				if sequence[end] < sequence[mid]:
								_A  ,				_A	:				Optional[int]   =      sequence[mid], sequence[end]

				slowsort(__lowercase					,  __lowercase					,  end - 1   )


if __name__ == "__main__":
	from doctest import testmod

	testmod()



