






"""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		:       Dict  				=		get_logger(__name__)



def 						lowercase    (				__snake_case				: Any		,					__snake_case				: int		,					__snake_case				: Union[str, Any]		,					__snake_case				: Dict		,					__snake_case				: str=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	):
				lowercase_ : str							       =   model.state_dict()
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						lowercase_ : Any							       =   F'''{MODEL_NAME}.bin''' if model_index == 0 else F'''{MODEL_NAME}_{model_index}.bin'''
						lowercase_ : str							       =   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:
						lowercase_ : str							       =   (
						    F'''{MODEL_NAME}_rank{accelerator.process_index}.bin'''
						    if model_index == 0
						    else F'''{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'''
						)
						lowercase_ : int							       =   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:
						lowercase_ : int							       =   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}'''	)
						lowercase_ : 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 						lowercase    (				__snake_case				: Tuple		,					__snake_case				: Union[str, Any]		,					__snake_case				: Union[str, Any]		,					__snake_case				: List[Any]		,					__snake_case				: Tuple=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
						lowercase_ : int							       =   F'''{MODEL_NAME}.bin''' if model_index == 0 else F'''{MODEL_NAME}_{model_index}.bin'''
						lowercase_ : Dict							       =   os.path.join(__snake_case		,					__snake_case	)
						logger.info(F'''Loading model from {input_model_file}'''	)
						lowercase_ : Any							       =   torch.load(__snake_case	)
						logger.info(F'''Model loaded from {input_model_file}'''	)
				elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
						lowercase_ : 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'''
						)
						lowercase_ : Any							       =   os.path.join(__snake_case		,					__snake_case	)
						logger.info(F'''Loading model from {input_model_file}'''	)
						lowercase_ : int							       =   torch.load(__snake_case	)
						logger.info(F'''Model loaded from {input_model_file}'''	)
				elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
						lowercase_ : Union[str, 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}'''	)
						lowercase_ : List[Any]							       =   {'''model''': model.state_dict()}
						dist_cp.load_state_dict(
						    state_dict=__snake_case		,					storage_reader=dist_cp.FileSystemReader(__snake_case	)		,					planner=DefaultLoadPlanner()		,					)
						lowercase_ : Optional[Any]							       =   state_dict['''model''']
						logger.info(F'''Model loaded from {ckpt_dir}'''	)
				model.load_state_dict(__snake_case	)



def 						lowercase    (				__snake_case				: Any		,					__snake_case				: List[Any]		,					__snake_case				: Dict		,					__snake_case				: Union[str, Any]		,					__snake_case				: Tuple		,					__snake_case				: Tuple=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	):
				lowercase_ : Tuple							       =   FSDP.optim_state_dict(__snake_case		,					__snake_case	)
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						if accelerator.process_index == 0:
								lowercase_ : Any							       =   (
								    F'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else F'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
								)
								lowercase_ : Optional[Any]							       =   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:
						lowercase_ : List[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 						lowercase    (				__snake_case				: Tuple		,					__snake_case				: Union[str, Any]		,					__snake_case				: List[str]		,					__snake_case				: Any		,					__snake_case				: Tuple		,					__snake_case				: Optional[int]=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:
						lowercase_ : 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:
						lowercase_ : List[str]							       =   (
						    F'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else F'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
						)
						lowercase_ : List[Any]							       =   os.path.join(__snake_case		,					__snake_case	)
						logger.info(F'''Loading Optimizer state from {input_optimizer_file}'''	)
						lowercase_ : str							       =   torch.load(__snake_case	)
						logger.info(F'''Optimizer state loaded from {input_optimizer_file}'''	)
				else:
						lowercase_ : Union[str, Any]							       =   (
						    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}'''	)
						lowercase_ : Tuple							       =   load_sharded_optimizer_state_dict(
						    model_state_dict=model.state_dict()		,					optimizer_key='''optimizer'''		,					storage_reader=dist_cp.FileSystemReader(__snake_case	)		,					)
						lowercase_ : Optional[int]							       =   optim_state['''optimizer''']
						logger.info(F'''Optimizer loaded from {ckpt_dir}'''	)
				lowercase_ : Optional[int]							       =   FSDP.optim_state_dict_to_load(__snake_case		,					__snake_case		,					__snake_case	)
				optimizer.load_state_dict(__snake_case	)











"""simple docstring"""



__A		:       Any  				=		{
    '''Pillow''': '''Pillow''',
    '''accelerate''': '''accelerate>=0.11.0''',
    '''compel''': '''compel==0.1.8''',
    '''black''': '''black~=23.1''',
    '''datasets''': '''datasets''',
    '''filelock''': '''filelock''',
    '''flax''': '''flax>=0.4.1''',
    '''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
    '''huggingface-hub''': '''huggingface-hub>=0.13.2''',
    '''requests-mock''': '''requests-mock==1.10.0''',
    '''importlib_metadata''': '''importlib_metadata''',
    '''invisible-watermark''': '''invisible-watermark''',
    '''isort''': '''isort>=5.5.4''',
    '''jax''': '''jax>=0.2.8,!=0.3.2''',
    '''jaxlib''': '''jaxlib>=0.1.65''',
    '''Jinja2''': '''Jinja2''',
    '''k-diffusion''': '''k-diffusion>=0.0.12''',
    '''torchsde''': '''torchsde''',
    '''note_seq''': '''note_seq''',
    '''librosa''': '''librosa''',
    '''numpy''': '''numpy''',
    '''omegaconf''': '''omegaconf''',
    '''parameterized''': '''parameterized''',
    '''protobuf''': '''protobuf>=3.20.3,<4''',
    '''pytest''': '''pytest''',
    '''pytest-timeout''': '''pytest-timeout''',
    '''pytest-xdist''': '''pytest-xdist''',
    '''ruff''': '''ruff>=0.0.241''',
    '''safetensors''': '''safetensors''',
    '''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
    '''scipy''': '''scipy''',
    '''onnx''': '''onnx''',
    '''regex''': '''regex!=2019.12.17''',
    '''requests''': '''requests''',
    '''tensorboard''': '''tensorboard''',
    '''torch''': '''torch>=1.4''',
    '''torchvision''': '''torchvision''',
    '''transformers''': '''transformers>=4.25.1''',
    '''urllib3''': '''urllib3<=2.0.0''',
}



