



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


snake_case							: Tuple      =   get_logger(__name__)






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






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








from math import pi






def 							__lowercase     (				__lowerCAmelCase							:       int      ,					__lowerCAmelCase							:       int     ):
							return 2 * pi * radius * (angle / 3_6_0)


if __name__ == "__main__":
						print(arc_length(90, 10))




