



"""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


lowercase__			   =  get_logger(__name__)





def       __magic_name__    (   _lowerCamelCase				: List[Any]  ,       _lowerCamelCase				: List[str]  ,       _lowerCamelCase				: List[str]  ,       _lowerCamelCase				: List[Any]  ,       _lowerCamelCase				: Dict=0					):
							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		:		Optional[Any]									=       model.state_dict()
														if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
																					__a		:		List[Any]									=       F'''{MODEL_NAME}.bin''' if model_index == 0 else F'''{MODEL_NAME}_{model_index}.bin'''
																					__a		:		Tuple									=       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		:		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		:		Union[str, Any]									=       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		:		Optional[int]									=       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		:		Optional[int]									=       {"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       __magic_name__    (   _lowerCamelCase				: str  ,       _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: Dict  ,       _lowerCamelCase				: List[Any]  ,       _lowerCamelCase				: Tuple=0					):
							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		:		Optional[Any]									=       F'''{MODEL_NAME}.bin''' if model_index == 0 else F'''{MODEL_NAME}_{model_index}.bin'''
																					__a		:		int									=       os.path.join(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_					)
																					logger.info(F'''Loading model from {input_model_file}'''					)
																					__a		:		int									=       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		:		List[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		:		str									=       os.path.join(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_					)
																					logger.info(F'''Loading model from {input_model_file}'''					)
																					__a		:		Optional[Any]									=       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		:		Dict									=       (
																					    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		:		Union[str, Any]									=       {"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		:		Dict									=       state_dict["model"]
																					logger.info(F'''Model loaded from {ckpt_dir}'''					)
														model.load_state_dict(SCREAMING_SNAKE_CASE_					)





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: List[Any]  ,       _lowerCamelCase				: Any  ,       _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: List[str]  ,       _lowerCamelCase				: Optional[int]=0					):
							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		:		List[Any]									=       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		:		Union[str, Any]									=       (
																												    F'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else F'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
																												)
																												__a		:		Any									=       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		:		Any									=       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       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: Any  ,       _lowerCamelCase				: List[str]  ,       _lowerCamelCase				: Optional[Any]=0					):
							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		:		int									=       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		:		Dict									=       os.path.join(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_					)
																					logger.info(F'''Loading Optimizer state from {input_optimizer_file}'''					)
																					__a		:		Optional[Any]									=       torch.load(SCREAMING_SNAKE_CASE_					)
																					logger.info(F'''Optimizer state loaded from {input_optimizer_file}'''					)
														else:
																					__a		:		Tuple									=       (
																					    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		:		Dict									=       load_sharded_optimizer_state_dict(
																					    model_state_dict=model.state_dict()  ,       optimizer_key="""optimizer"""  ,       storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE_					)  ,       )
																					__a		:		Union[str, Any]									=       optim_state["optimizer"]
																					logger.info(F'''Optimizer loaded from {ckpt_dir}'''					)
														__a		:		int									=       FSDP.optim_state_dict_to_load(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_					)
														optimizer.load_state_dict(SCREAMING_SNAKE_CASE_					)








"""simple docstring"""

def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: float					):
							return round(float(moles / volume					) * nfactor					)





def       __magic_name__    (   _lowerCamelCase				: float  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: float					):
							return round(float((moles * 0.08_21 * temperature) / (volume)					)					)





def       __magic_name__    (   _lowerCamelCase				: float  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: float					):
							return round(float((moles * 0.08_21 * temperature) / (pressure)					)					)





def       __magic_name__    (   _lowerCamelCase				: float  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: float					):
							return round(float((pressure * volume) / (0.08_21 * moles)					)					)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

