


'''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    :    Dict			     =  get_logger(__name__)


def    lowerCamelCase__   ( A     :    List[Any]   ,     A     :    str   ,     A     :    Dict   ,     A     :    int   ,     A     :    List[Any]=0  ):




						'''simple docstring'''






						os.makedirs(A   ,     exist_ok=A  )
						with FSDP.state_dict_type(
						    A   ,     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(A   ,     A  )
																		if accelerator.process_index == 0:
																								logger.info(f"""Saving model to {output_model_file}"""  )
																								torch.save(A   ,     A  )
																								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(A   ,     A  )
																		logger.info(f"""Saving model to {output_model_file}"""  )
																		torch.save(A   ,     A  )
																		logger.info(f"""Model saved to {output_model_file}"""  )
												elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
																		UpperCAmelCase         =			os.path.join(A   ,     f"""{MODEL_NAME}_{model_index}"""  )
																		os.makedirs(A   ,     exist_ok=A  )
																		logger.info(f"""Saving model to {ckpt_dir}"""  )
																		UpperCAmelCase         =			{'''model''': state_dict}

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


def    lowerCamelCase__   ( A     :    Optional[int]   ,     A     :    List[str]   ,     A     :    Union[str, Any]   ,     A     :    Dict   ,     A     :    List[Any]=0  ):




						'''simple docstring'''






						accelerator.wait_for_everyone()
						with FSDP.state_dict_type(
						    A   ,     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(A  ) != 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(A   ,     A  )
																		logger.info(f"""Loading model from {input_model_file}"""  )
																		UpperCAmelCase         =			torch.load(A  )
																		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(A   ,     A  )
																		logger.info(f"""Loading model from {input_model_file}"""  )
																		UpperCAmelCase         =			torch.load(A  )
																		logger.info(f"""Model loaded from {input_model_file}"""  )
												elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
																		UpperCAmelCase         =			(
																		    os.path.join(A   ,     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=A   ,     storage_reader=dist_cp.FileSystemReader(A  )   ,     planner=DefaultLoadPlanner()   ,     )
																		UpperCAmelCase         =			state_dict['''model''']
																		logger.info(f"""Model loaded from {ckpt_dir}"""  )
												model.load_state_dict(A  )


def    lowerCamelCase__   ( A     :    str   ,     A     :    Any   ,     A     :    List[Any]   ,     A     :    Dict   ,     A     :    int   ,     A     :    Optional[Any]=0  ):




						'''simple docstring'''






						os.makedirs(A   ,     exist_ok=A  )
						with FSDP.state_dict_type(
						    A   ,     fsdp_plugin.state_dict_type   ,     fsdp_plugin.state_dict_config   ,     fsdp_plugin.optim_state_dict_config  ):
												UpperCAmelCase         =			FSDP.optim_state_dict(A   ,     A  )
												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(A   ,     A  )
																								logger.info(f"""Saving Optimizer state to {output_optimizer_file}"""  )
																								torch.save(A   ,     A  )
																								logger.info(f"""Optimizer state saved in {output_optimizer_file}"""  )
												else:
																		UpperCAmelCase         =			os.path.join(A   ,     f"""{OPTIMIZER_NAME}_{optimizer_index}"""  )
																		os.makedirs(A   ,     exist_ok=A  )
																		logger.info(f"""Saving Optimizer state to {ckpt_dir}"""  )
																		dist_cp.save_state_dict(
																		    state_dict={'''optimizer''': optim_state}   ,     storage_writer=dist_cp.FileSystemWriter(A  )   ,     planner=DefaultSavePlanner()   ,     )
																		logger.info(f"""Optimizer state saved in {ckpt_dir}"""  )




def    lowerCamelCase__   ( A     :    Union[str, Any]   ,     A     :    Dict   ,     A     :    Union[str, Any]   ,     A     :    Any   ,     A     :    Tuple   ,     A     :    int=0  ):




						'''simple docstring'''






						accelerator.wait_for_everyone()
						with FSDP.state_dict_type(
						    A   ,     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(A   ,     A  )
																		logger.info(f"""Loading Optimizer state from {input_optimizer_file}"""  )
																		UpperCAmelCase         =			torch.load(A  )
																		logger.info(f"""Optimizer state loaded from {input_optimizer_file}"""  )
												else:
																		UpperCAmelCase         =			(
																		    os.path.join(A   ,     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(A  )   ,     )
																		UpperCAmelCase         =			optim_state['''optimizer''']
																		logger.info(f"""Optimizer loaded from {ckpt_dir}"""  )
												UpperCAmelCase         =			FSDP.optim_state_dict_to_load(A   ,     A   ,     A  )
												optimizer.load_state_dict(A  )









'''simple docstring'''



from ..utils import DummyObject, requires_backends






class  UpperCamelCase__(     metaclass=lowerCAmelCase ):
				__magic_name__       :   List[str]							= ["note_seq"]




				def __init__(	self    :				Any   ,					*lowerCAmelCase    :				List[str]   ,					**lowerCAmelCase    :				int   )->  Optional[int]:

										"""simple docstring"""




										requires_backends(self   ,					['''note_seq''']   )




				@classmethod
				def   a__(	cls    :				Dict   ,					*lowerCAmelCase    :				int   ,					**lowerCAmelCase    :				Optional[int]   )->  Dict:

										"""simple docstring"""




										requires_backends(cls   ,					['''note_seq''']   )





				@classmethod
				def   a__(	cls    :				int   ,					*lowerCAmelCase    :				Tuple   ,					**lowerCAmelCase    :				Optional[Any]   )->  Union[str, Any]:

										"""simple docstring"""




										requires_backends(cls   ,					['''note_seq''']   )





