






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      	=  get_logger(__name__)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase=0	)->  Dict:





						"""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	):
												_UpperCAmelCase									=			model.state_dict()
												if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
																		_UpperCAmelCase									=			F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
																		_UpperCAmelCase									=			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:
																		_UpperCAmelCase									=			(
																		    F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
																		    if model_index == 0
																		    else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
																		)
																		_UpperCAmelCase									=			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:
																		_UpperCAmelCase									=			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}"""	)
																		_UpperCAmelCase									=			{'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 __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase=0	)->  Union[str, Any]:





						"""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
																		_UpperCAmelCase									=			F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
																		_UpperCAmelCase									=			os.path.join(SCREAMING_SNAKE_CASE__ ,		SCREAMING_SNAKE_CASE__	)
																		logger.info(F"""Loading model from {input_model_file}"""	)
																		_UpperCAmelCase									=			torch.load(SCREAMING_SNAKE_CASE__	)
																		logger.info(F"""Model loaded from {input_model_file}"""	)
												elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
																		_UpperCAmelCase									=			(
																		    F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
																		    if model_index == 0
																		    else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
																		)
																		_UpperCAmelCase									=			os.path.join(SCREAMING_SNAKE_CASE__ ,		SCREAMING_SNAKE_CASE__	)
																		logger.info(F"""Loading model from {input_model_file}"""	)
																		_UpperCAmelCase									=			torch.load(SCREAMING_SNAKE_CASE__	)
																		logger.info(F"""Model loaded from {input_model_file}"""	)
												elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
																		_UpperCAmelCase									=			(
																		    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}"""	)
																		_UpperCAmelCase									=			{'model': model.state_dict()}
																		dist_cp.load_state_dict(
																		    state_dict=SCREAMING_SNAKE_CASE__ ,		storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE__	) ,		planner=DefaultLoadPlanner() ,		)
																		_UpperCAmelCase									=			state_dict['model']
																		logger.info(F"""Model loaded from {ckpt_dir}"""	)
												model.load_state_dict(SCREAMING_SNAKE_CASE__	)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase=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	):
												_UpperCAmelCase									=			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:
																								_UpperCAmelCase									=			(
																								    F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
																								)
																								_UpperCAmelCase									=			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:
																		_UpperCAmelCase									=			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 __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase=0	)->  Optional[Any]:





						"""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:
																		_UpperCAmelCase									=			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:
																		_UpperCAmelCase									=			(
																		    F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
																		)
																		_UpperCAmelCase									=			os.path.join(SCREAMING_SNAKE_CASE__ ,		SCREAMING_SNAKE_CASE__	)
																		logger.info(F"""Loading Optimizer state from {input_optimizer_file}"""	)
																		_UpperCAmelCase									=			torch.load(SCREAMING_SNAKE_CASE__	)
																		logger.info(F"""Optimizer state loaded from {input_optimizer_file}"""	)
												else:
																		_UpperCAmelCase									=			(
																		    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}"""	)
																		_UpperCAmelCase									=			load_sharded_optimizer_state_dict(
																		    model_state_dict=model.state_dict() ,		optimizer_key='optimizer' ,		storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE__	) ,		)
																		_UpperCAmelCase									=			optim_state['optimizer']
																		logger.info(F"""Optimizer loaded from {ckpt_dir}"""	)
												_UpperCAmelCase									=			FSDP.optim_state_dict_to_load(SCREAMING_SNAKE_CASE__ ,		SCREAMING_SNAKE_CASE__ ,		SCREAMING_SNAKE_CASE__	)
												optimizer.load_state_dict(SCREAMING_SNAKE_CASE__	)



def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	[]
						if len(SCREAMING_SNAKE_CASE__       ) == 1:
												return [nums.copy()]
						for _ in range(len(SCREAMING_SNAKE_CASE__       )       ):
												snake_case_   =	nums.pop(0       )
												snake_case_   =	permute(SCREAMING_SNAKE_CASE__       )
												for perm in permutations:
																		perm.append(SCREAMING_SNAKE_CASE__       )
												result.extend(SCREAMING_SNAKE_CASE__       )
												nums.append(SCREAMING_SNAKE_CASE__       )
						return result


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):

						def backtrack(SCREAMING_SNAKE_CASE__       ):
												if start == len(SCREAMING_SNAKE_CASE__       ) - 1:
																		output.append(nums[:]       )
												else:
																		for i in range(SCREAMING_SNAKE_CASE__  ,		len(SCREAMING_SNAKE_CASE__       )       ):
																								snake_case_,    snake_case_   =	nums[i], nums[start]
																								backtrack(start + 1       )
																								snake_case_,    snake_case_   =	nums[i], nums[start]  # backtrack

						snake_case_   =	[]
						backtrack(0       )
						return output


if __name__ == "__main__":
					import doctest

					# use res to print the data in permute2 function
					lowerCAmelCase_       	=	permutea([1, 2, 3])
					print(res)
					doctest.testmod()