






"""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


_lowerCAmelCase         =							get_logger(__name__)






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

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






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






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__=0					):
	os.makedirs(snake_case__   ,   exist_ok=snake_case__					)
	with FSDP.state_dict_type(
	    snake_case__   ,   fsdp_plugin.state_dict_type   ,   fsdp_plugin.state_dict_config   ,   fsdp_plugin.optim_state_dict_config					):
		A_		:						Optional[Any]	    =   FSDP.optim_state_dict(snake_case__   ,   snake_case__					)
		if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
			if accelerator.process_index == 0:
				A_		:						Any	    =   (
				    F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
				)
				A_		:						Optional[int]	    =   os.path.join(snake_case__   ,   snake_case__					)
				logger.info(F"""Saving Optimizer state to {output_optimizer_file}"""					)
				torch.save(snake_case__   ,   snake_case__					)
				logger.info(F"""Optimizer state saved in {output_optimizer_file}"""					)
		else:
			A_		:						Union[str, Any]	    =   os.path.join(snake_case__   ,   F"""{OPTIMIZER_NAME}_{optimizer_index}"""					)
			os.makedirs(snake_case__   ,   exist_ok=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(snake_case__					)   ,   planner=DefaultSavePlanner()   ,   )
			logger.info(F"""Optimizer state saved in {ckpt_dir}"""					)






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









"""simple docstring"""
import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
_lowerCAmelCase         =							"."


if __name__ == "__main__":
							_lowerCAmelCase         =							os.path.join(REPO_PATH, "utils/documentation_tests.txt")
							_lowerCAmelCase         =							[]
							_lowerCAmelCase         =							[]
							with open(doctest_file_path) as fp:
														for line in fp:
																					_lowerCAmelCase         =							line.strip()
																					_lowerCAmelCase         =							os.path.join(REPO_PATH, line)
																					if not (os.path.isfile(path) or os.path.isdir(path)):
																												non_existent_paths.append(line)
																					all_paths.append(path)
							if len(non_existent_paths) > 0:
														_lowerCAmelCase         =							"\n".join(non_existent_paths)
														raise ValueError(F'`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}')
							if all_paths != sorted(all_paths):
														raise ValueError("Files in `utils/documentation_tests.txt` are not in alphabetical order.")

