






"""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


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

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

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

def  _snake_case				(     lowerCamelCase__	:    str				,						lowerCamelCase__	:    Any				,						lowerCamelCase__	:    Tuple				,						lowerCamelCase__	:    List[str]				,						lowerCamelCase__	:    Tuple=0  )				->	Tuple:
							accelerator.wait_for_everyone()
							with FSDP.state_dict_type(
							    lowerCamelCase__				,						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(lowerCamelCase__  ) != 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
																					lowerCamelCase_       :							Any									=F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
																					lowerCamelCase_       :							Optional[int]									=os.path.join(lowerCamelCase__				,						lowerCamelCase__  )
																					logger.info(F"""Loading model from {input_model_file}"""  )
																					lowerCamelCase_       :							int									=torch.load(lowerCamelCase__  )
																					logger.info(F"""Model loaded from {input_model_file}"""  )
														elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
																					lowerCamelCase_       :							Dict									=(
																					    F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
																					    if model_index == 0
																					    else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
																					)
																					lowerCamelCase_       :							Tuple									=os.path.join(lowerCamelCase__				,						lowerCamelCase__  )
																					logger.info(F"""Loading model from {input_model_file}"""  )
																					lowerCamelCase_       :							str									=torch.load(lowerCamelCase__  )
																					logger.info(F"""Model loaded from {input_model_file}"""  )
														elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
																					lowerCamelCase_       :							Optional[int]									=(
																					    os.path.join(lowerCamelCase__				,						F"""{MODEL_NAME}_{model_index}"""  )
																					    if F"""{MODEL_NAME}""" not in input_dir
																					    else input_dir
																					)
																					logger.info(F"""Loading model from {ckpt_dir}"""  )
																					lowerCamelCase_       :							int									={"model": model.state_dict()}
																					dist_cp.load_state_dict(
																					    state_dict=lowerCamelCase__				,						storage_reader=dist_cp.FileSystemReader(lowerCamelCase__  )				,						planner=DefaultLoadPlanner()				,						)
																					lowerCamelCase_       :							int									=state_dict["model"]
																					logger.info(F"""Model loaded from {ckpt_dir}"""  )
														model.load_state_dict(lowerCamelCase__  )

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

def  _snake_case				(     lowerCamelCase__	:    List[str]				,						lowerCamelCase__	:    Dict				,						lowerCamelCase__	:    int				,						lowerCamelCase__	:    Dict				,						lowerCamelCase__	:    Optional[int]				,						lowerCamelCase__	:    List[Any]=0  )				->	str:
							accelerator.wait_for_everyone()
							with FSDP.state_dict_type(
							    lowerCamelCase__				,						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:
																					lowerCamelCase_       :							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:
																					lowerCamelCase_       :							Union[str, Any]									=(
																					    F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
																					)
																					lowerCamelCase_       :							List[str]									=os.path.join(lowerCamelCase__				,						lowerCamelCase__  )
																					logger.info(F"""Loading Optimizer state from {input_optimizer_file}"""  )
																					lowerCamelCase_       :							List[Any]									=torch.load(lowerCamelCase__  )
																					logger.info(F"""Optimizer state loaded from {input_optimizer_file}"""  )
														else:
																					lowerCamelCase_       :							List[Any]									=(
																					    os.path.join(lowerCamelCase__				,						F"""{OPTIMIZER_NAME}_{optimizer_index}"""  )
																					    if F"""{OPTIMIZER_NAME}""" not in input_dir
																					    else input_dir
																					)
																					logger.info(F"""Loading Optimizer from {ckpt_dir}"""  )
																					lowerCamelCase_       :							int									=load_sharded_optimizer_state_dict(
																					    model_state_dict=model.state_dict()				,						optimizer_key="optimizer"				,						storage_reader=dist_cp.FileSystemReader(lowerCamelCase__  )				,						)
																					lowerCamelCase_       :							Dict									=optim_state["optimizer"]
																					logger.info(F"""Optimizer loaded from {ckpt_dir}"""  )
														lowerCamelCase_       :							str									=FSDP.optim_state_dict_to_load(lowerCamelCase__				,						lowerCamelCase__				,						lowerCamelCase__  )
														optimizer.load_state_dict(lowerCamelCase__  )








import random






def     a(      A       :    Optional[Any] ,     A       :    Optional[Any] ,     A       :    str       )		->       List[Any]:
					"""simple docstring"""





					a					     =   a[left_index]
					a					     =   left_index + 1
					for j in range(left_index + 1 ,     A       ):
										if a[j] < pivot:
															a     ,				a					     =   a[i], a[j]
															i += 1
					a     ,				a					     =   a[i - 1], a[left_index]
					return i - 1






def     a(      A       :    List[Any] ,     A       :    List[Any] ,     A       :    Union[str, Any]       )		->       List[Any]:
					"""simple docstring"""





					if left < right:
										a					     =   random.randint(A ,     right - 1       )
										a     ,				a					     =   (
										    a[left],
										    a[pivot],
										)  # switches the pivot with the left most bound
										a					     =   partition(A ,     A ,     A       )
										quick_sort_random(
										    A ,     A ,     A       )  # recursive quicksort to the left of the pivot point
										quick_sort_random(
										    A ,     pivot_index + 1 ,     A       )  # recursive quicksort to the right of the pivot point






def     a(      )		->       Any:
					"""simple docstring"""





					a					     =   input("Enter numbers separated by a comma:\n"       ).strip()
					a					     =   [int(A       ) for item in user_input.split(","       )]

					quick_sort_random(A ,     0 ,     len(A       )       )

					print(A       )


if __name__ == "__main__":
				main()
