





"""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      =							get_logger(__name__)





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





def 							lowercase					(snake_case__   : Dict      ,       snake_case__   : str      ,       snake_case__   : List[Any]      ,       snake_case__   : List[str]      ,       snake_case__   : List[Any]      ,       snake_case__   : Tuple=0 )     ->  Optional[Any]:


				'''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 ):
								lowerCAmelCase  =				FSDP.optim_state_dict(_A      ,       _A )
								if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
												if accelerator.process_index == 0:
																lowerCAmelCase  =				(
																    f'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else f'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
																)
																lowerCAmelCase  =				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:
												lowerCAmelCase  =				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					(snake_case__   : int      ,       snake_case__   : Tuple      ,       snake_case__   : Optional[Any]      ,       snake_case__   : int      ,       snake_case__   : Any      ,       snake_case__   : Dict=0 )     ->  Tuple:


				'''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:
												lowerCAmelCase  =				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  =				(
												    f'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else f'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
												)
												lowerCAmelCase  =				os.path.join(_A      ,       _A )
												logger.info(f'''Loading Optimizer state from {input_optimizer_file}''' )
												lowerCAmelCase  =				torch.load(_A )
												logger.info(f'''Optimizer state loaded from {input_optimizer_file}''' )
								else:
												lowerCAmelCase  =				(
												    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}''' )
												lowerCAmelCase  =				load_sharded_optimizer_state_dict(
												    model_state_dict=model.state_dict()      ,       optimizer_key="""optimizer"""      ,       storage_reader=dist_cp.FileSystemReader(_A )      ,       )
												lowerCAmelCase  =				optim_state["optimizer"]
												logger.info(f'''Optimizer loaded from {ckpt_dir}''' )
								lowerCAmelCase  =				FSDP.optim_state_dict_to_load(_A      ,       _A      ,       _A )
								optimizer.load_state_dict(_A )











"""simple docstring"""







import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 							lowercase					(snake_case__   : int      ,       snake_case__   : Union[str, Any] )     ->  List[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				torch.load(snake_case__      ,       map_location="""cpu""" )

				lowerCAmelCase  =				chkpt["""model"""]

				# We have the base model one level deeper than the original XLM repository
				lowerCAmelCase  =				{}
				for k, v in state_dict.items():
								if "pred_layer" in k:
												lowerCAmelCase  =				v
								else:
												lowerCAmelCase  =				v

				lowerCAmelCase  =				chkpt["""params"""]
				lowerCAmelCase  =				{n: v for n, v in config.items() if not isinstance(snake_case__      ,       (torch.FloatTensor, numpy.ndarray) )}

				lowerCAmelCase  =				chkpt["""dico_word2id"""]
				lowerCAmelCase  =				{s + """</w>""" if s.find("""@@""" ) == -1 and i > 13 else s.replace("""@@"""      ,       """""" ): i for s, i in vocab.items()}

				# Save pytorch-model
				lowerCAmelCase  =				pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
				lowerCAmelCase  =				pytorch_dump_folder_path + """/""" + CONFIG_NAME
				lowerCAmelCase  =				pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""vocab_file"""]

				print(f'''Save PyTorch model to {pytorch_weights_dump_path}''' )
				torch.save(snake_case__      ,       snake_case__ )

				print(f'''Save configuration file to {pytorch_config_dump_path}''' )
				with open(snake_case__      ,       """w"""      ,       encoding="""utf-8""" ) as f:
								f.write(json.dumps(snake_case__      ,       indent=2 ) + """\n""" )

				print(f'''Save vocab file to {pytorch_config_dump_path}''' )
				with open(snake_case__      ,       """w"""      ,       encoding="""utf-8""" ) as f:
								f.write(json.dumps(snake_case__      ,       indent=2 ) + """\n""" )


if __name__ == "__main__":
		a      =							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--xlm_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		a      =							parser.parse_args()
		convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)





