






'''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 	_UpperCAmelCase    (  a		:	List[Any]						,			a		:	str						,			a		:	Dict						,			a		:	int						,			a		:	List[Any]=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							):
												lowercase_      :   Optional[int]        =			model.state_dict()
												if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
																		lowercase_      :   List[Any]        =			f"{MODEL_NAME}.bin" if model_index == 0 else f"{MODEL_NAME}_{model_index}.bin"
																		lowercase_      :   Dict        =			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:
																		lowercase_      :   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_      :   str        =			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:
																		lowercase_      :   Union[str, Any]        =			os.path.join(a						,			f"{MODEL_NAME}_{model_index}"							)
																		os.makedirs(a						,			exist_ok=a							)
																		logger.info(f"Saving model to {ckpt_dir}"							)
																		lowercase_      :   Optional[Any]        =			{'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[int]						,			a		:	List[str]						,			a		:	Union[str, Any]						,			a		:	Dict						,			a		:	List[Any]=0							)			->			List[Any]:





						"""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
																		lowercase_      :   str        =			f"{MODEL_NAME}.bin" if model_index == 0 else f"{MODEL_NAME}_{model_index}.bin"
																		lowercase_      :   List[Any]        =			os.path.join(a						,			a							)
																		logger.info(f"Loading model from {input_model_file}"							)
																		lowercase_      :   Any        =			torch.load(a							)
																		logger.info(f"Model loaded from {input_model_file}"							)
												elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
																		lowercase_      :   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_      :   int        =			os.path.join(a						,			a							)
																		logger.info(f"Loading model from {input_model_file}"							)
																		lowercase_      :   Any        =			torch.load(a							)
																		logger.info(f"Model loaded from {input_model_file}"							)
												elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
																		lowercase_      :   List[str]        =			(
																		    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}"							)
																		lowercase_      :   str        =			{'model': model.state_dict()}
																		dist_cp.load_state_dict(
																		    state_dict=a						,			storage_reader=dist_cp.FileSystemReader(a							)						,			planner=DefaultLoadPlanner()						,			)
																		lowercase_      :   Tuple        =			state_dict['model']
																		logger.info(f"Model loaded from {ckpt_dir}"							)
												model.load_state_dict(a							)






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








'''simple docstring'''

import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


A:      Any  				=				get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
						A:      List[Any]  				=				json.load(f)






@require_torch
class 							__magic_name__     (	unittest.TestCase				):







	"""simple docstring"""



	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  Tuple:
							return FSMTTokenizer.from_pretrained(_lowercase       )
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  Optional[int]:
							lowercase_      :   str        =			FSMTForConditionalGeneration.from_pretrained(_lowercase       ).to(_lowercase       )
							if torch_device == "cuda":
													model.half()
							return model







	@parameterized.expand(
	    [
	        ['en-ru', 26.0],
	        ['ru-en', 22.0],
	        ['en-de', 22.0],
	        ['de-en', 29.0],
	    ]       )
	@slow
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase       )						->  Optional[int]:
							# note: this test is not testing the best performance since it only evals a small batch
							# but it should be enough to detect a regression in the output quality
							lowercase_      :   Optional[Any]        =			f"facebook/wmt19-{pair}"
							lowercase_      :   str        =			self.get_tokenizer(_lowercase       )
							lowercase_      :   Any        =			self.get_model(_lowercase       )

							lowercase_      :   Any        =			bleu_data[pair]['src']
							lowercase_      :   Any        =			bleu_data[pair]['tgt']

							lowercase_      :   Dict        =			tokenizer(_lowercase  ,      return_tensors='pt'  ,      truncation=_lowercase  ,      padding='longest'       ).to(_lowercase       )
							lowercase_      :   str        =			model.generate(
							    input_ids=batch.input_ids  ,      num_beams=8  ,      )
							lowercase_      :   Any        =			tokenizer.batch_decode(
							    _lowercase  ,      skip_special_tokens=_lowercase  ,      clean_up_tokenization_spaces=_lowercase       )
							lowercase_      :   Union[str, Any]        =			calculate_bleu(_lowercase  ,      _lowercase       )
							print(_lowercase       )
							self.assertGreaterEqual(scores['bleu']  ,      _lowercase       )
