



import json
import os
import tempfile
from unittest.mock import patch

import torch
from torch.utils.data import DataLoader, TensorDataset

from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase    :     List[Any]													=	torch.nn.Linear(2 ,   4						)
		__UpperCAmelCase    :     Optional[Any]													=	torch.optim.AdamW(model.parameters() ,   lr=1.0						)
		__UpperCAmelCase    :     List[Any]													=	torch.optim.lr_scheduler.OneCycleLR(_UpperCAmelCase ,   max_lr=0.01 ,   steps_per_epoch=2 ,   epochs=1						)
		__UpperCAmelCase    :     Union[str, Any]													=	DataLoader(TensorDataset(torch.tensor([1, 2, 3]						)						)						)
		__UpperCAmelCase    :     Union[str, Any]													=	DataLoader(TensorDataset(torch.tensor([4, 5, 6]						)						)						)

		return model, optimizer, scheduler, train_dl, valid_dl

def        a						(      _UpperCAmelCase							:					Optional[Any]						):







		'''simple docstring'''





		return (model.weight.abs().sum() + model.bias.abs().sum()).item()

def        a						(      _UpperCAmelCase							:					Union[str, Any]						):







		'''simple docstring'''





		__UpperCAmelCase    :     Dict													=	torch.nn.Linear(*tuple(model.weight.T.shape						)						).state_dict()
		model.load_state_dict(_UpperCAmelCase						)

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			@require_cuda
			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[Any]													=	Accelerator()
					assert PartialState._shared_state["_cpu"] is False
					assert PartialState._shared_state["device"].type == "cuda"
					with self.assertRaises(a_ ):
							__UpperCAmelCase    :     str													=	Accelerator(cpu=a_ )




			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[Any]													=	Accelerator()
					__UpperCAmelCase    :     List[str]													=	GradientState()
					assert state.num_steps == 1
					__UpperCAmelCase    :     Optional[int]													=	4
					assert state.num_steps == 4

					assert state.sync_gradients is True
					__UpperCAmelCase    :     Dict													=	False
					assert state.sync_gradients is False
					GradientState._reset_state()




			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     int													=	Accelerator()
					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     Union[str, Any]													=	create_components()

					(
					    (
					    __UpperCAmelCase
					)							,					(
					    __UpperCAmelCase
					)							,					(
					    __UpperCAmelCase
					)							,					(
					    __UpperCAmelCase
					)							,					(
					    __UpperCAmelCase
					)							,					
					)    :     Optional[int]													=	accelerator.prepare(a_       ,       a_       ,       a_       ,       a_       ,       a_ )

					self.assertTrue(prepared_model in accelerator._models )
					self.assertTrue(prepared_optimizer in accelerator._optimizers )
					self.assertTrue(prepared_scheduler in accelerator._schedulers )
					self.assertTrue(prepared_train_dl in accelerator._dataloaders )
					self.assertTrue(prepared_valid_dl in accelerator._dataloaders )




			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[int]													=	Accelerator()
					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     List[Any]													=	create_components()
					accelerator.prepare(a_       ,       a_       ,       a_       ,       a_       ,       a_ )
					accelerator.free_memory()

					self.assertTrue(len(accelerator._models ) == 0 )
					self.assertTrue(len(accelerator._optimizers ) == 0 )
					self.assertTrue(len(accelerator._schedulers ) == 0 )
					self.assertTrue(len(accelerator._dataloaders ) == 0 )




			def  snake_case__     (   self							: Dict ):
					'''simple docstring'''





					PartialState._reset_state()

					# Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
					def noop(*a_							: Tuple       ,       **a_							: Optional[Any] ):
							pass

					with patch('''torch.cuda.set_device'''       ,       a_ ), patch_environment(ACCELERATE_TORCH_DEVICE='''cuda:64''' ):
							__UpperCAmelCase    :     int													=	Accelerator()
							self.assertEqual(str(accelerator.state.device )       ,       '''cuda:64''' )




			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[Any]													=	Accelerator()
					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     Any													=	create_components()
					accelerator.prepare(a_       ,       a_       ,       a_       ,       a_       ,       a_ )

					__UpperCAmelCase    :     Dict													=	get_signature(a_ )

					with tempfile.TemporaryDirectory() as tmpdirname:
							accelerator.save_state(a_ )

							# make sure random weights don't match
							load_random_weights(a_ )
							self.assertTrue(abs(model_signature - get_signature(a_ ) ) > 1e-3 )

							# make sure loaded weights match
							accelerator.load_state(a_ )
							self.assertTrue(abs(model_signature - get_signature(a_ ) ) < 1e-3 )




			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	Accelerator()
					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     Tuple													=	create_components()
					accelerator.prepare(a_       ,       a_       ,       a_       ,       a_       ,       a_ )

					__UpperCAmelCase    :     Tuple													=	get_signature(a_ )

					# saving hook
					def save_config(a_							: Dict       ,       a_							: Dict       ,       a_							: List[str] ):
							__UpperCAmelCase    :     List[Any]													=	{'''class_name''': models[0].__class__.__name__}

							with open(os.path.join(a_       ,       '''data.json''' )       ,       '''w''' ) as f:
									json.dump(a_       ,       a_ )

        # loading hook
					def load_config(a_							: List[Any]       ,       a_							: Optional[Any] ):
							with open(os.path.join(a_       ,       '''data.json''' )       ,       '''r''' ) as f:
									__UpperCAmelCase    :     int													=	json.load(a_ )

							__UpperCAmelCase    :     int													=	config['''class_name''']

					__UpperCAmelCase    :     Optional[Any]													=	accelerator.register_save_state_pre_hook(a_ )
					__UpperCAmelCase    :     List[Any]													=	accelerator.register_load_state_pre_hook(a_ )

					with tempfile.TemporaryDirectory() as tmpdirname:
							accelerator.save_state(a_ )

							# make sure random weights don't match with hooks
							load_random_weights(a_ )
							self.assertTrue(abs(model_signature - get_signature(a_ ) ) > 1e-3 )

							# random class name to verify correct one is loaded
							__UpperCAmelCase    :     int													=	'''random'''

							# make sure loaded weights match with hooks
							accelerator.load_state(a_ )
							self.assertTrue(abs(model_signature - get_signature(a_ ) ) < 1e-3 )

							# mode.class_name is loaded from config
							self.assertTrue(model.class_name == model.__class__.__name__ )

					# remove hooks
					save_hook.remove()
					load_hook.remove()

					with tempfile.TemporaryDirectory() as tmpdirname:
							accelerator.save_state(a_ )

							# make sure random weights don't match with hooks removed
							load_random_weights(a_ )
							self.assertTrue(abs(model_signature - get_signature(a_ ) ) > 1e-3 )

							# random class name to verify correct one is loaded
							__UpperCAmelCase    :     List[Any]													=	'''random'''

							# make sure loaded weights match with hooks removed
							accelerator.load_state(a_ )
							self.assertTrue(abs(model_signature - get_signature(a_ ) ) < 1e-3 )

							# mode.class_name is NOT loaded from config
							self.assertTrue(model.class_name != model.__class__.__name__ )




			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     int													=	Accelerator()
					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	create_components()
					__UpperCAmelCase    :     Optional[Any]													=	None

					# This should work
					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     str													=	accelerator.prepare(
					    a_       ,       a_       ,       a_       ,       a_       ,       a_       ,       a_ )
					self.assertTrue(dummy_obj is None )




			def  snake_case__     (   self							: Any ):
					'''simple docstring'''





					__UpperCAmelCase    :     Tuple													=	Accelerator()
					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     Dict													=	create_components()
					__UpperCAmelCase    :     Tuple													=	[1, 2, 3]

					# This should work
					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     Dict													=	accelerator.prepare(
					    a_       ,       a_       ,       a_       ,       a_       ,       a_       ,       a_ )
					self.assertEqual(
					    getattr(a_       ,       '''_is_accelerate_prepared'''       ,       a_ )       ,       a_       ,       '''Dummy object should have `_is_accelerate_prepared` set to `True`'''       ,       )
					self.assertEqual(
					    getattr(a_       ,       '''_is_accelerate_prepared'''       ,       a_ )       ,       a_       ,       '''Model is missing `_is_accelerator_prepared` or is set to `False`'''       ,       )
					self.assertEqual(
					    getattr(a_       ,       '''_is_accelerate_prepared'''       ,       a_ )       ,       a_       ,       '''Optimizer is missing `_is_accelerator_prepared` or is set to `False`'''       ,       )
					self.assertEqual(
					    getattr(a_       ,       '''_is_accelerate_prepared'''       ,       a_ )       ,       a_       ,       '''Scheduler is missing `_is_accelerator_prepared` or is set to `False`'''       ,       )
					self.assertEqual(
					    getattr(a_       ,       '''_is_accelerate_prepared'''       ,       a_ )       ,       a_       ,       '''Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`'''       ,       )
					self.assertEqual(
					    getattr(a_       ,       '''_is_accelerate_prepared'''       ,       a_ )       ,       a_       ,       '''Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`'''       ,       )




			@slow
			@require_bnb
			def  snake_case__     (   self							: str ):
					'''simple docstring'''





					from transformers import AutoModelForCausalLM

					__UpperCAmelCase    :     Optional[int]													=	AutoModelForCausalLM.from_pretrained(
					    '''EleutherAI/gpt-neo-125m'''       ,       load_in_abit=a_       ,       device_map={'''''': 0}       ,       )
					__UpperCAmelCase    :     Tuple													=	Accelerator()

					# This should work
					__UpperCAmelCase    :     Optional[Any]													=	accelerator.prepare(a_ )




			@slow
			@require_bnb
			def  snake_case__     (   self							: Tuple ):
					'''simple docstring'''





					from transformers import AutoModelForCausalLM

					__UpperCAmelCase    :     Optional[Any]													=	Accelerator()

					with init_empty_weights():
							__UpperCAmelCase    :     Optional[Any]													=	AutoModelForCausalLM.from_pretrained(
							    '''EleutherAI/gpt-neo-125m'''       ,       )
							model.tie_weights()
							__UpperCAmelCase    :     Optional[Any]													=	infer_auto_device_map(a_ )
							__UpperCAmelCase    :     str													=	'''cpu'''

					__UpperCAmelCase    :     int													=	AutoModelForCausalLM.from_pretrained(
					    '''EleutherAI/gpt-neo-125m'''       ,       device_map=a_       ,       load_in_abit=a_       ,       llm_inta_enable_fpaa_cpu_offload=a_ )

					# This should not work and get value error
					with self.assertRaises(a_ ):
							__UpperCAmelCase    :     Any													=	accelerator.prepare(a_ )




			@slow
			@require_bnb
			@require_multi_gpu
			def  snake_case__     (   self							: str ):
					'''simple docstring'''





					from transformers import AutoModelForCausalLM

					__UpperCAmelCase    :     str													=	{'''distributed_type''': DistributedType.MULTI_GPU}

					with init_empty_weights():
							__UpperCAmelCase    :     Union[str, Any]													=	AutoModelForCausalLM.from_pretrained(
							    '''EleutherAI/gpt-neo-125m'''       ,       )
							model.tie_weights()
							__UpperCAmelCase    :     Tuple													=	infer_auto_device_map(a_ )
							__UpperCAmelCase    :     str													=	1

					__UpperCAmelCase    :     Dict													=	AutoModelForCausalLM.from_pretrained(
					    '''EleutherAI/gpt-neo-125m'''       ,       load_in_abit=a_       ,       device_map=a_       ,       )
					__UpperCAmelCase    :     int													=	Accelerator()

					# This should not work and get value error
					with self.assertRaises(a_ ):
							__UpperCAmelCase    :     Tuple													=	accelerator.prepare(a_ )

					PartialState._reset_state()




			@slow
			@require_bnb
			@require_multi_gpu
			def  snake_case__     (   self							: Dict ):
					'''simple docstring'''





					from transformers import AutoModelForCausalLM

					with init_empty_weights():
							__UpperCAmelCase    :     List[str]													=	AutoModelForCausalLM.from_pretrained(
							    '''EleutherAI/gpt-neo-125m'''       ,       )
							__UpperCAmelCase    :     int													=	infer_auto_device_map(a_ )
							__UpperCAmelCase    :     Any													=	1

					__UpperCAmelCase    :     List[str]													=	AutoModelForCausalLM.from_pretrained(
					    '''EleutherAI/gpt-neo-125m'''       ,       load_in_abit=a_       ,       device_map=a_       ,       )
					__UpperCAmelCase    :     Union[str, Any]													=	Accelerator()

					# This should work
					__UpperCAmelCase    :     Any													=	accelerator.prepare(a_ )




			@require_cuda
			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[Any]													=	torch.nn.Linear(10       ,       10 )
					__UpperCAmelCase    :     Dict													=	torch.optim.SGD(model.parameters()       ,       lr=0.0_1 )
					__UpperCAmelCase    :     Dict													=	Accelerator(cpu=a_ )
					__UpperCAmelCase    :     List[Any]													=	accelerator.prepare(a_ )










import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__A   			=getLogger(__name__)

def        a						(      _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					int = 8 ,   _UpperCAmelCase							:					int = 10_24 ,   _UpperCAmelCase							:					Any="val" ,   _UpperCAmelCase							:					Dict=None ,   _UpperCAmelCase							:					Any=False ,   _UpperCAmelCase							:					Union[str, Any]="summarization" ,   _UpperCAmelCase							:					Optional[Any]=None ,   _UpperCAmelCase							:					Union[str, Any]=1 ,   _UpperCAmelCase							:					Dict = None ,   _UpperCAmelCase							:					Dict="" ,   **_UpperCAmelCase							:					List[str] ,   ):







		'''simple docstring'''





		__UpperCAmelCase    :     Any													=	str(_UpperCAmelCase						)
		assert local_rank is not None
		torch.distributed.init_process_group(backend='''nccl''' ,   rank=_UpperCAmelCase						)

		__UpperCAmelCase    :     Union[str, Any]													=	Path(_UpperCAmelCase						)
		__UpperCAmelCase    :     Union[str, Any]													=	save_dir.joinpath(f'rank_{local_rank}_output.json'						)
		torch.cuda.set_device(_UpperCAmelCase						)
		__UpperCAmelCase    :     int													=	AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase						).cuda()
		if fpaa:
				__UpperCAmelCase    :     Any													=	model.half()
		# determine if we need to increase num_beams
		use_task_specific_params(_UpperCAmelCase ,   _UpperCAmelCase						)  # update config with task specific params
		__UpperCAmelCase    :     List[str]													=	generate_kwargs.pop('''num_beams''' ,   model.config.num_beams						)  # AttributeError risk?
		if num_return_sequences > num_beams:
				__UpperCAmelCase    :     Any													=	num_return_sequences

		__UpperCAmelCase    :     Tuple													=	AutoTokenizer.from_pretrained(_UpperCAmelCase						)
		logger.info(f'Inferred tokenizer type: {tokenizer.__class__}'						)  # if this is wrong, check config.model_type.

		if max_source_length is None:
				__UpperCAmelCase    :     Optional[Any]													=	tokenizer.model_max_length
		if prefix is None:
				__UpperCAmelCase    :     str													=	prefix or getattr(model.config ,   '''prefix''' ,   ''''''						) or ''''''
		__UpperCAmelCase    :     Union[str, Any]													=	SeqaSeqDataset(
		    _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   max_target_length=10_24 ,   type_path=_UpperCAmelCase ,   n_obs=_UpperCAmelCase ,   prefix=_UpperCAmelCase ,   **_UpperCAmelCase ,   )
		# I set shuffle=True for a more accurate progress bar.
		# If all the longest samples are first, the prog bar estimate is too high at the beginning.
		__UpperCAmelCase    :     str													=	ds.make_sortish_sampler(_UpperCAmelCase ,   distributed=_UpperCAmelCase ,   add_extra_examples=_UpperCAmelCase ,   shuffle=_UpperCAmelCase						)
		__UpperCAmelCase    :     List[Any]													=	DataLoader(_UpperCAmelCase ,   sampler=_UpperCAmelCase ,   batch_size=_UpperCAmelCase ,   collate_fn=ds.collate_fn						)
		__UpperCAmelCase    :     List[Any]													=	[]
		for batch in tqdm(_UpperCAmelCase						):
				__UpperCAmelCase    :     str													=	model.generate(
				    input_ids=batch['''input_ids'''].to(model.device						) ,   attention_mask=batch['''attention_mask'''].to(model.device						) ,   num_return_sequences=_UpperCAmelCase ,   num_beams=_UpperCAmelCase ,   **_UpperCAmelCase ,   )
				__UpperCAmelCase    :     Optional[int]													=	tokenizer.batch_decode(_UpperCAmelCase ,   skip_special_tokens=_UpperCAmelCase ,   clean_up_tokenization_spaces=_UpperCAmelCase						)
				__UpperCAmelCase    :     List[str]													=	batch['''ids''']
				if num_return_sequences > 1:
						__UpperCAmelCase    :     Any													=	chunks(_UpperCAmelCase ,   _UpperCAmelCase						)  # batch size chunks, each of size num_return_seq
				for i, pred in enumerate(_UpperCAmelCase						):
						results.append({'''pred''': pred, '''id''': ids[i].item()}						)
		save_json(_UpperCAmelCase ,   _UpperCAmelCase						)
		return results, sampler.num_replicas

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	argparse.ArgumentParser(
		    epilog='''Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate'''						)
		parser.add_argument('''--data_dir''' ,   type=_UpperCAmelCase ,   help='''like cnn_dm/test.source'''						)
		parser.add_argument(
		    '''--model_name''' ,   type=_UpperCAmelCase ,   help='''like facebook/bart-large-cnn,t5-base, etc.''' ,   default='''sshleifer/distilbart-xsum-12-3''' ,   )
		parser.add_argument('''--save_dir''' ,   type=_UpperCAmelCase ,   help='''where to save''' ,   default='''tmp_gen'''						)
		parser.add_argument('''--max_source_length''' ,   type=_UpperCAmelCase ,   default=_UpperCAmelCase						)
		parser.add_argument(
		    '''--type_path''' ,   type=_UpperCAmelCase ,   default='''test''' ,   help='''which subset to evaluate typically train/val/test'''						)
		parser.add_argument('''--task''' ,   type=_UpperCAmelCase ,   default='''summarization''' ,   help='''used for task_specific_params + metrics'''						)
		parser.add_argument('''--bs''' ,   type=_UpperCAmelCase ,   default=8 ,   required=_UpperCAmelCase ,   help='''batch size'''						)
		parser.add_argument(
		    '''--local_rank''' ,   type=_UpperCAmelCase ,   default=-1 ,   required=_UpperCAmelCase ,   help='''should be passed by distributed.launch'''						)

		parser.add_argument(
		    '''--n_obs''' ,   type=_UpperCAmelCase ,   default=_UpperCAmelCase ,   required=_UpperCAmelCase ,   help='''How many observations. Defaults to all.'''						)
		parser.add_argument(
		    '''--num_return_sequences''' ,   type=_UpperCAmelCase ,   default=1 ,   required=_UpperCAmelCase ,   help='''How many sequences to return'''						)
		parser.add_argument(
		    '''--sync_timeout''' ,   type=_UpperCAmelCase ,   default=6_00 ,   required=_UpperCAmelCase ,   help='''How long should master process wait for other processes to finish.''' ,   )
		parser.add_argument('''--src_lang''' ,   type=_UpperCAmelCase ,   default=_UpperCAmelCase ,   required=_UpperCAmelCase						)
		parser.add_argument('''--tgt_lang''' ,   type=_UpperCAmelCase ,   default=_UpperCAmelCase ,   required=_UpperCAmelCase						)
		parser.add_argument(
		    '''--prefix''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   default=_UpperCAmelCase ,   help='''will be added to the begininng of src examples'''						)
		parser.add_argument('''--fp16''' ,   action='''store_true'''						)
		parser.add_argument('''--debug''' ,   action='''store_true'''						)
		__UpperCAmelCase    :     Any													=	time.time()
		__UpperCAmelCase							,					__UpperCAmelCase    :     Any													=	parser.parse_known_args()
		__UpperCAmelCase    :     List[Any]													=	parse_numeric_n_bool_cl_kwargs(_UpperCAmelCase						)
		if generate_kwargs and args.local_rank <= 0:
				print(f'parsed the following generate kwargs: {generate_kwargs}'						)
		__UpperCAmelCase    :     Union[str, Any]													=	Path(args.save_dir + '''_tmp'''						)
		Path(_UpperCAmelCase						).mkdir(exist_ok=_UpperCAmelCase						)  # this handles locking.
		__UpperCAmelCase    :     int													=	list(json_save_dir.glob('''rank_*.json'''						)						)
		if intermediate_files:
				raise ValueError(f'Found files at {json_save_dir} please move or remove them.'						)
				# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
		__UpperCAmelCase    :     List[Any]													=	{}
		if args.src_lang is not None:
				__UpperCAmelCase    :     List[str]													=	args.src_lang
		if args.tgt_lang is not None:
				__UpperCAmelCase    :     List[Any]													=	args.tgt_lang

		Path(args.save_dir						).mkdir(exist_ok=_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	eval_data_dir(
		    args.data_dir ,   _UpperCAmelCase ,   args.model_name ,   type_path=args.type_path ,   bs=args.bs ,   fpaa=args.fpaa ,   task=args.task ,   local_rank=args.local_rank ,   n_obs=args.n_obs ,   max_source_length=args.max_source_length ,   num_return_sequences=args.num_return_sequences ,   prefix=args.prefix ,   dataset_kwargs=_UpperCAmelCase ,   **_UpperCAmelCase ,   )

		if args.local_rank <= 0:
				__UpperCAmelCase    :     int													=	Path(args.save_dir						)
				save_dir.mkdir(exist_ok=_UpperCAmelCase						)
				__UpperCAmelCase    :     List[str]													=	gather_results_from_each_node(_UpperCAmelCase ,   _UpperCAmelCase ,   args.sync_timeout						)
				__UpperCAmelCase    :     List[Any]													=	combine_partial_results(_UpperCAmelCase						)
				if args.num_return_sequences > 1:
						__UpperCAmelCase    :     int													=	save_dir.joinpath('''pseudolabel_results.json'''						)
						print(f'Saving aggregated results at {save_path}, intermediate in {json_save_dir}/'						)
						save_json(_UpperCAmelCase ,   _UpperCAmelCase						)
						return
				__UpperCAmelCase    :     str													=	Path(args.data_dir						).joinpath(args.type_path + '''.target'''						)
				with open(_UpperCAmelCase						) as f:
						__UpperCAmelCase    :     int													=	[x.rstrip() for x in f.readlines()][: len(_UpperCAmelCase						)]

				# Calculate metrics, save metrics,  and save _generations.txt
				__UpperCAmelCase    :     Optional[Any]													=	'''translation''' in args.task
				__UpperCAmelCase    :     Optional[int]													=	calculate_bleu if calc_bleu else calculate_rouge
				__UpperCAmelCase    :     Union[str, Any]													=	'''bleu''' if calc_bleu else '''rouge'''
				__UpperCAmelCase    :     Dict													=	score_fn(_UpperCAmelCase ,   _UpperCAmelCase						)
				__UpperCAmelCase    :     Union[str, Any]													=	len(_UpperCAmelCase						)
				__UpperCAmelCase    :     Union[str, Any]													=	time.time() - start_time
				__UpperCAmelCase    :     List[str]													=	round(runtime / metrics['''n_obs'''] ,   4						)
				__UpperCAmelCase    :     List[str]													=	num_replicas
				# TODO(@stas00): add whatever metadata to metrics
				__UpperCAmelCase    :     List[Any]													=	save_dir.joinpath(f'{args.type_path}_{metric_name}.json'						)
				save_json(_UpperCAmelCase ,   _UpperCAmelCase ,   indent=_UpperCAmelCase						)
				print(_UpperCAmelCase						)
				write_txt_file(_UpperCAmelCase ,   save_dir.joinpath(f'{args.type_path}_generations.txt'						)						)
				if args.debug:
						write_txt_file(_UpperCAmelCase ,   save_dir.joinpath(f'{args.type_path}.target'						)						)
				else:
						shutil.rmtree(_UpperCAmelCase						)

def        a						(      _UpperCAmelCase							:					int						):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[Any]													=	[]
		for partial_result in partial_results:
				records.extend(_UpperCAmelCase						)
		__UpperCAmelCase    :     List[str]													=	sorted(_UpperCAmelCase ,   key=lambda _UpperCAmelCase						: x["id"]						)
		__UpperCAmelCase    :     Union[str, Any]													=	[x['''pred'''] for x in records]
		return preds

def        a						(      _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					Union[str, Any]						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[str]													=	time.time()
		logger.info('''waiting for all nodes to finish'''						)
		__UpperCAmelCase    :     Any													=	None
		while (time.time() - start_wait) < timeout:
				__UpperCAmelCase    :     List[Any]													=	list(save_dir.glob('''rank_*.json'''						)						)
				if len(_UpperCAmelCase						) < num_replicas:
						continue
				try:
						# make sure all json files are fully saved
						__UpperCAmelCase    :     Union[str, Any]													=	lmap(_UpperCAmelCase ,   _UpperCAmelCase						)
						return json_data
				except JSONDecodeError:
						continue
		else:
				raise TimeoutError('''Rank 0 gave up on waiting for other processes'''						)
		# Unreachable


if __name__ == "__main__":
				# Usage for MT:
				run_generate()





