



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


UpperCAmelCase_							:      int      =  get_logger(__name__)
def  UpperCamelCase						(       _A			:				int			,							_A			:				Tuple			,							_A			:				Optional[Any]			,							_A			:				Any			,							_A			:				Union[str, Any]=0    )->       Dict:





						"""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    ):
												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  UpperCamelCase						(       _A			:				Optional[Any]			,							_A			:				Union[str, Any]			,							_A			:				List[str]			,							_A			:				Optional[int]			,							_A			:				List[Any]=0    )->       Optional[int]:





						"""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
																		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  UpperCamelCase						(       _A			:				str			,							_A			:				List[str]			,							_A			:				str			,							_A			:				Optional[int]			,							_A			:				int			,							_A			:				int=0    )->       int:





						"""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    ):
												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  UpperCamelCase						(       _A			:				Dict			,							_A			:				Optional[Any]			,							_A			:				Optional[Any]			,							_A			:				Tuple			,							_A			:				Union[str, Any]			,							_A			:				Any=0    )->       List[str]:





						"""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:
																		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    )










import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_							:      Union[str, Any]      =  logging.get_logger(__name__)
def  UpperCamelCase						(       _A			:				str			,							_A			:				str    )->       Any:





						"""simple docstring"""





						A__							=       RobertaPreLayerNormConfig.from_pretrained(
						    _A			,							architectures=["RobertaPreLayerNormForMaskedLM"]    )

						# convert state_dict
						A__							=       torch.load(hf_hub_download(repo_id=_A			,							filename="pytorch_model.bin"    )    )
						A__							=       {}
						for tensor_key, tensor_value in original_state_dict.items():
												# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
												if tensor_key.startswith("roberta."    ):
																		A__							=       "roberta_prelayernorm." + tensor_key[len("roberta."    ) :]

												# The original implementation contains weights which are not used, remove them from the state_dict
												if tensor_key.endswith(".self.LayerNorm.weight"    ) or tensor_key.endswith(".self.LayerNorm.bias"    ):
																		continue

												A__							=       tensor_value

						A__							=       RobertaPreLayerNormForMaskedLM.from_pretrained(
						    pretrained_model_name_or_path=_A			,							config=_A			,							state_dict=_A    )
						model.save_pretrained(_A    )

						# convert tokenizer
						A__							=       AutoTokenizer.from_pretrained(_A    )
						tokenizer.save_pretrained(_A    )


if __name__ == "__main__":
						UpperCAmelCase_							:      str      =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--checkpoint-repo",
						    default=None,
						    type=str,
						    required=True,
						    help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						UpperCAmelCase_							:      List[Any]      =  parser.parse_args()
						convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)





