

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


lowerCAmelCase__	   = get_logger(__name__)



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




				'''simple docstring'''





				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 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Optional[int]	,       SCREAMING_SNAKE_CASE_:							List[Any]	,       SCREAMING_SNAKE_CASE_:							Optional[int]	,       SCREAMING_SNAKE_CASE_:							List[Any]	,       SCREAMING_SNAKE_CASE_:							Union[str, Any]=0      )			->      Dict:




				'''simple docstring'''





				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 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Dict	,       SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							Any	,       SCREAMING_SNAKE_CASE_:							Union[str, Any]	,       SCREAMING_SNAKE_CASE_:							Any	,       SCREAMING_SNAKE_CASE_:							Optional[int]=0      )			->      List[Any]:




				'''simple docstring'''





				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 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Optional[Any]	,       SCREAMING_SNAKE_CASE_:							List[str]	,       SCREAMING_SNAKE_CASE_:							int	,       SCREAMING_SNAKE_CASE_:							Any	,       SCREAMING_SNAKE_CASE_:							List[Any]	,       SCREAMING_SNAKE_CASE_:							Optional[Any]=0      )			->      Tuple:




				'''simple docstring'''





				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_      )


from math import factorial



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int = 1_0_0      )			->      int:




				'''simple docstring'''





				return sum(map(SCREAMING_SNAKE_CASE_	,       str(factorial(SCREAMING_SNAKE_CASE_      )      )      )      )


if __name__ == "__main__":
	print(solution(int(input("""Enter the Number: """).strip())))

