


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_   :				Union[str, Any]						      = get_logger(__name__)







def  SCREAMING_SNAKE_CASE_		(    __A				:    Dict						,					__A				:    List[Any]						,					__A				:    Any						,					__A				:    List[Any]						,					__A				:    Optional[Any]=0       )			->	Optional[int]:

       """simple docstring"""

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

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







def  SCREAMING_SNAKE_CASE_		(    __A				:    List[str]						,					__A				:    Tuple						,					__A				:    Dict						,					__A				:    List[str]						,					__A				:    Tuple=0       )			->	Optional[int]:

       """simple docstring"""

       accelerator.wait_for_everyone()
       with FSDP.state_dict_type(
           __A						,					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(__A       ) != 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_      :	List[str]							=						F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
                     a_      :	Optional[Any]							=						os.path.join(__A						,					__A       )
                     logger.info(F"""Loading model from {input_model_file}"""       )
                     a_      :	Union[str, Any]							=						torch.load(__A       )
                     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_      :	str							=						os.path.join(__A						,					__A       )
                     logger.info(F"""Loading model from {input_model_file}"""       )
                     a_      :	Union[str, Any]							=						torch.load(__A       )
                     logger.info(F"""Model loaded from {input_model_file}"""       )
              elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
                     a_      :	Dict							=						(
                         os.path.join(__A						,					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[Any]							=						{'model': model.state_dict()}
                     dist_cp.load_state_dict(
                         state_dict=__A						,					storage_reader=dist_cp.FileSystemReader(__A       )						,					planner=DefaultLoadPlanner()						,					)
                     a_      :	Dict							=						state_dict['model']
                     logger.info(F"""Model loaded from {ckpt_dir}"""       )
              model.load_state_dict(__A       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    List[Any]						,					__A				:    List[str]						,					__A				:    List[Any]						,					__A				:    Dict						,					__A				:    Any						,					__A				:    List[Any]=0       )			->	Tuple:

       """simple docstring"""

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







def  SCREAMING_SNAKE_CASE_		(    __A				:    str						,					__A				:    List[Any]						,					__A				:    List[Any]						,					__A				:    List[Any]						,					__A				:    Optional[Any]						,					__A				:    Dict=0       )			->	Dict:

       """simple docstring"""

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





def  SCREAMING_SNAKE_CASE_		(    __A				:    int       )			->	bool:

       """simple docstring"""

       a_      :	List[Any]							=						n ** (1 / 3)
       return (val * val * val) == n


if __name__ == "__main__":
   print(perfect_cube(27))
   print(perfect_cube(4))


