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

			dist_cp.save_state_dict(
			    state_dict=_UpperCamelCase ,						storage_writer=dist_cp.FileSystemWriter(_UpperCamelCase	) ,						planner=DefaultSavePlanner() ,						)
			logger.info(F"Model saved to {ckpt_dir}"	)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase=0	):
	accelerator.wait_for_everyone()
	with FSDP.state_dict_type(
	    _UpperCamelCase ,						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(_UpperCamelCase	) != 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
			__lowerCAmelCase    :		Tuple		    =   F"{MODEL_NAME}.bin" if model_index == 0 else F"{MODEL_NAME}_{model_index}.bin"
			__lowerCAmelCase    :		Union[str, Any]		    =   os.path.join(_UpperCamelCase ,						_UpperCamelCase	)
			logger.info(F"Loading model from {input_model_file}"	)
			__lowerCAmelCase    :		Tuple		    =   torch.load(_UpperCamelCase	)
			logger.info(F"Model loaded from {input_model_file}"	)
		elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
			__lowerCAmelCase    :		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"
			)
			__lowerCAmelCase    :		Optional[Any]		    =   os.path.join(_UpperCamelCase ,						_UpperCamelCase	)
			logger.info(F"Loading model from {input_model_file}"	)
			__lowerCAmelCase    :		Any		    =   torch.load(_UpperCamelCase	)
			logger.info(F"Model loaded from {input_model_file}"	)
		elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
			__lowerCAmelCase    :		Optional[int]		    =   (
			    os.path.join(_UpperCamelCase ,						F"{MODEL_NAME}_{model_index}"	)
			    if F"{MODEL_NAME}" not in input_dir
			    else input_dir
			)
			logger.info(F"Loading model from {ckpt_dir}"	)
			__lowerCAmelCase    :		Optional[int]		    =   {'model': model.state_dict()}
			dist_cp.load_state_dict(
			    state_dict=_UpperCamelCase ,						storage_reader=dist_cp.FileSystemReader(_UpperCamelCase	) ,						planner=DefaultLoadPlanner() ,						)
			__lowerCAmelCase    :		int		    =   state_dict['model']
			logger.info(F"Model loaded from {ckpt_dir}"	)
		model.load_state_dict(_UpperCamelCase	)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase=0	):
	os.makedirs(_UpperCamelCase ,						exist_ok=_UpperCamelCase	)
	with FSDP.state_dict_type(
	    _UpperCamelCase ,						fsdp_plugin.state_dict_type ,						fsdp_plugin.state_dict_config ,						fsdp_plugin.optim_state_dict_config	):
		__lowerCAmelCase    :		str		    =   FSDP.optim_state_dict(_UpperCamelCase ,						_UpperCamelCase	)
		if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
			if accelerator.process_index == 0:
				__lowerCAmelCase    :		List[str]		    =   (
				    F"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else F"{OPTIMIZER_NAME}_{optimizer_index}.bin"
				)
				__lowerCAmelCase    :		Union[str, Any]		    =   os.path.join(_UpperCamelCase ,						_UpperCamelCase	)
				logger.info(F"Saving Optimizer state to {output_optimizer_file}"	)
				torch.save(_UpperCamelCase ,						_UpperCamelCase	)
				logger.info(F"Optimizer state saved in {output_optimizer_file}"	)
		else:
			__lowerCAmelCase    :		str		    =   os.path.join(_UpperCamelCase ,						F"{OPTIMIZER_NAME}_{optimizer_index}"	)
			os.makedirs(_UpperCamelCase ,						exist_ok=_UpperCamelCase	)
			logger.info(F"Saving Optimizer state to {ckpt_dir}"	)
			dist_cp.save_state_dict(
			    state_dict={'optimizer': optim_state} ,						storage_writer=dist_cp.FileSystemWriter(_UpperCamelCase	) ,						planner=DefaultSavePlanner() ,						)
			logger.info(F"Optimizer state saved in {ckpt_dir}"	)
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase=0	):
	accelerator.wait_for_everyone()
	with FSDP.state_dict_type(
	    _UpperCamelCase ,						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:
			__lowerCAmelCase    :		Optional[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:
			__lowerCAmelCase    :		Tuple		    =   (
			    F"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else F"{OPTIMIZER_NAME}_{optimizer_index}.bin"
			)
			__lowerCAmelCase    :		int		    =   os.path.join(_UpperCamelCase ,						_UpperCamelCase	)
			logger.info(F"Loading Optimizer state from {input_optimizer_file}"	)
			__lowerCAmelCase    :		Tuple		    =   torch.load(_UpperCamelCase	)
			logger.info(F"Optimizer state loaded from {input_optimizer_file}"	)
		else:
			__lowerCAmelCase    :		Dict		    =   (
			    os.path.join(_UpperCamelCase ,						F"{OPTIMIZER_NAME}_{optimizer_index}"	)
			    if F"{OPTIMIZER_NAME}" not in input_dir
			    else input_dir
			)
			logger.info(F"Loading Optimizer from {ckpt_dir}"	)
			__lowerCAmelCase    :		str		    =   load_sharded_optimizer_state_dict(
			    model_state_dict=model.state_dict() ,						optimizer_key='optimizer' ,						storage_reader=dist_cp.FileSystemReader(_UpperCamelCase	) ,						)
			__lowerCAmelCase    :		List[str]		    =   optim_state['optimizer']
			logger.info(F"Optimizer loaded from {ckpt_dir}"	)
		__lowerCAmelCase    :		str		    =   FSDP.optim_state_dict_to_load(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	)
		optimizer.load_state_dict(_UpperCamelCase	)

"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		List[str]		    =   len(_UpperCamelCase	)
	__lowerCAmelCase    :		Tuple		    =   [[0] * n for i in range(_UpperCamelCase	)]
	for i in range(_UpperCamelCase	):
		__lowerCAmelCase    :		Any		    =   y_points[i]

	for i in range(2 ,						_UpperCamelCase	):
		for j in range(_UpperCamelCase ,						_UpperCamelCase	):
			__lowerCAmelCase    :		int		    =   (
			    (xa - x_points[j - i + 1]) * q[j][i - 1]
			    - (xa - x_points[j]) * q[j - 1][i - 1]
			) / (x_points[j] - x_points[j - i + 1])

	return [q[n - 1][n - 1], q]


if __name__ == "__main__":
							import doctest

							doctest.testmod()