"""simple docstring"""






import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


lowerCAmelCase__  :  List[Any]						     =	logging.getLogger(__name__)

lowerCAmelCase__  :  Any						     =	'pytorch_model.bin'
@dataclasses.dataclass
class 				snake_case       :




					"""simple docstring"""

					snake_case__				    = dataclasses.field(
					    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."}				)
					snake_case__				    = dataclasses.field(
					    default=__UpperCAmelCase      ,    metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."}      ,    )






@dataclasses.dataclass
class 				snake_case       :




					"""simple docstring"""

					snake_case__				    = dataclasses.field(metadata={"help": "A csv or a json file containing the training data."}				)
					snake_case__				    = dataclasses.field(metadata={"help": "A csv or a json file containing the data to predict on."}				)
					snake_case__				    = dataclasses.field(
					    default=__UpperCAmelCase      ,    metadata={"help": "A csv or a json file containing the validation data."}				)
					snake_case__				    = dataclasses.field(
					    default=__UpperCAmelCase      ,    metadata={"help": "The name of the task to train on."}      ,    )
					snake_case__				    = dataclasses.field(
					    default=__UpperCAmelCase      ,    metadata={"help": "The list of labels for the task."}				)






@dataclasses.dataclass
class 				snake_case       :




					"""simple docstring"""

					snake_case__				    = dataclasses.field(
					    metadata={"help": "The output directory where the model predictions and checkpoints will be written."}				)
					snake_case__				    = dataclasses.field(
					    default="accuracy"      ,    metadata={"help": "The evaluation metric used for the task."}				)
					snake_case__				    = dataclasses.field(
					    default="no"      ,    metadata={
					        "help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
					    }      ,    )
					snake_case__				    = dataclasses.field(
					    default=10      ,    metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}      ,    )
					snake_case__				    = dataclasses.field(
					    default=0.0      ,    metadata={
					        "help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
					    }      ,    )
					snake_case__				    = dataclasses.field(
					    default=__UpperCAmelCase      ,    metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."}      ,    )
					snake_case__				    = dataclasses.field(
					    default=__UpperCAmelCase      ,    metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."}      ,    )
					snake_case__				    = dataclasses.field(
					    default=__UpperCAmelCase      ,    metadata={"help": "Whether to fine-tune on labeled data after pseudo training."}      ,    )
					snake_case__				    = dataclasses.field(
					    default=0.0      ,    metadata={"help": "Confidence threshold for pseudo-labeled data filtering."}      ,    )
					snake_case__				    = dataclasses.field(
					    default=1_00      ,    metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."}      ,    )
					snake_case__				    = dataclasses.field(
					    default=__UpperCAmelCase      ,    metadata={"help": "Random seed for initialization."}      ,    )






def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							datasets.concatenate_datasets([infer_input, infer_output]					,			axis=1       )

				if args.do_filter_by_confidence:
								UpperCAmelCase__             =							dataset.filter(lambda lowerCamelCase       : example["probability"] > args.confidence_threshold       )

				if args.do_filter_by_val_performance:
								assert eval_result >= 0.0 and eval_result <= 1.0
								UpperCAmelCase__             =							int(eval_result * len(lowerCamelCase       )       )
								print(lowerCamelCase       )
								UpperCAmelCase__             =							dataset.sort('probability'					,			reverse=lowerCamelCase       )
								UpperCAmelCase__             =							dataset.select(range(lowerCamelCase       )       )

				UpperCAmelCase__             =							dataset.remove_columns(['label', 'probability']       )
				UpperCAmelCase__             =							dataset.rename_column('prediction'					,			'label'       )
				UpperCAmelCase__             =							dataset.map(lambda lowerCamelCase       : {"label": idalabel[example["label"]]}       )
				UpperCAmelCase__             =							dataset.shuffle(seed=args.seed       )

				UpperCAmelCase__             =							os.path.join(lowerCamelCase					,			f'''train_pseudo.{args.data_file_extension}'''       )
				if args.data_file_extension == "csv":
								dataset.to_csv(lowerCamelCase					,			index=lowerCamelCase       )
				else:
								dataset.to_json(lowerCamelCase       )







def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			**lowerCamelCase       ):
				UpperCAmelCase__             =							Accelerator()
				# Make one log on every process with the configuration for debugging.
				logging.basicConfig(
				    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'					,			datefmt='%m/%d/%Y %H:%M:%S'					,			level=logging.INFO					,			)
				logger.info(accelerator.state       )

				# Setup logging, we only want one process per machine to log things on the
				# screen. accelerator.is_local_main_process is only True for one process per
				# machine.
				logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR       )

				if accelerator.is_local_main_process:
								datasets.utils.logging.set_verbosity_warning()
								transformers.utils.logging.set_verbosity_info()
				else:
								datasets.utils.logging.set_verbosity_error()
								transformers.utils.logging.set_verbosity_error()

				UpperCAmelCase__             =							STModelArguments(model_name_or_path=lowerCamelCase       )
				UpperCAmelCase__             =							STDataArguments(train_file=lowerCamelCase					,			infer_file=lowerCamelCase       )
				UpperCAmelCase__             =							STTrainingArguments(output_dir=lowerCamelCase       )
				UpperCAmelCase__             =							argparse.Namespace()

				for arg_class in (model_args, data_args, training_args):
								for key, value in vars(lowerCamelCase       ).items():
												setattr(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )

				for key, value in kwargs.items():
								if hasattr(lowerCamelCase					,			lowerCamelCase       ):
												setattr(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )

    # Sanity checks
				UpperCAmelCase__             =							{}
				UpperCAmelCase__             =							None

				# You need to provide the training data and the data to predict on
				assert args.train_file is not None
				assert args.infer_file is not None
				UpperCAmelCase__             =							args.train_file
				UpperCAmelCase__             =							args.infer_file

				if args.evaluation_strategy != IntervalStrategy.NO.value:
								assert args.eval_file is not None
								UpperCAmelCase__             =							args.eval_file

				for key in data_files:
								UpperCAmelCase__             =							data_files[key].split('.'       )[-1]
								assert extension in ["csv", "json"], f'''`{key}_file` should be a csv or a json file.'''
								if args.data_file_extension is None:
												UpperCAmelCase__             =							extension
								else:
												assert extension == args.data_file_extension, f'''`{key}_file` should be a {args.data_file_extension} file`.'''

				assert (
				    args.eval_metric in datasets.list_metrics()
				), f'''{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'''

				# If passed along, set the training seed now.
				if args.seed is not None:
								set_seed(args.seed       )

				logger.info('Creating the initial data directory for self-training...'       )
				UpperCAmelCase__             =							f'''{args.output_dir}/self-train_iter-{{}}'''.format
				UpperCAmelCase__             =							data_dir_format(0       )

				if accelerator.is_main_process:
								if args.output_dir is not None:
												os.makedirs(args.output_dir					,			exist_ok=lowerCamelCase       )
												os.makedirs(lowerCamelCase					,			exist_ok=lowerCamelCase       )
				accelerator.wait_for_everyone()

				UpperCAmelCase__             =							None
				UpperCAmelCase__             =							None
				UpperCAmelCase__             =							0
				UpperCAmelCase__             =							False
				# Show the progress bar
				UpperCAmelCase__             =							tqdm(range(args.max_selftrain_iterations       )					,			disable=not accelerator.is_local_main_process       )

				# Self-train
				for iteration in range(0					,			int(args.max_selftrain_iterations       )       ):
								UpperCAmelCase__             =							data_dir_format(lowerCamelCase       )
								assert os.path.exists(lowerCamelCase       )

								# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
								# iteration > 0
								UpperCAmelCase__             =							os.path.join(lowerCamelCase					,			'stage-1'       )
								UpperCAmelCase__             =							{
								    'accelerator': accelerator,
								    'model_name_or_path': args.model_name_or_path,
								    'cache_dir': args.cache_dir,
								    'do_train': True,
								    'train_file': data_files['train'] if iteration == 0 else data_files['train_pseudo'],
								    'do_eval': True if args.eval_file is not None else False,
								    'eval_file': data_files['eval'],
								    'do_predict': True,
								    'infer_file': data_files['infer'],
								    'task_name': args.task_name,
								    'label_list': args.label_list,
								    'output_dir': current_output_dir,
								    'eval_metric': args.eval_metric,
								    'evaluation_strategy': args.evaluation_strategy,
								    'early_stopping_patience': args.early_stopping_patience,
								    'early_stopping_threshold': args.early_stopping_threshold,
								    'seed': args.seed,
								}
								# Add additional training arguments
								for key, value in kwargs.items():
												if key not in arguments_dict and not hasattr(lowerCamelCase					,			lowerCamelCase       ):
																arguments_dict.update({key: value}       )

								UpperCAmelCase__             =							os.path.join(lowerCamelCase					,			'best-checkpoint'					,			lowerCamelCase       )
								if os.path.exists(lowerCamelCase       ):
												logger.info(
												    'Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.'					,			lowerCamelCase					,			lowerCamelCase					,			)
								else:
												logger.info('***** Running self-training: iteration: %d, stage: 1 *****'					,			lowerCamelCase       )
												finetune(**lowerCamelCase       )
												accelerator.wait_for_everyone()
												assert os.path.exists(lowerCamelCase       )
												logger.info('Self-training job completed: iteration: %d, stage: 1.'					,			lowerCamelCase       )

								if iteration > 0 and args.finetune_on_labeled_data:
												# Stage 2 (optional): fine-tuning on the original labeled data
												UpperCAmelCase__             =							os.path.join(lowerCamelCase					,			'best-checkpoint'       )
												UpperCAmelCase__             =							os.path.join(lowerCamelCase					,			'stage-2'       )
												# Update arguments_dict
												UpperCAmelCase__             =							model_path
												UpperCAmelCase__             =							data_files['train']
												UpperCAmelCase__             =							current_output_dir

												UpperCAmelCase__             =							os.path.join(lowerCamelCase					,			'best-checkpoint'					,			lowerCamelCase       )
												if os.path.exists(lowerCamelCase       ):
																logger.info(
																    'Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.'					,			lowerCamelCase					,			lowerCamelCase					,			)
												else:
																logger.info('***** Running self-training: iteration: %d, stage: 2 *****'					,			lowerCamelCase       )
																finetune(**lowerCamelCase       )
																accelerator.wait_for_everyone()
																assert os.path.exists(lowerCamelCase       )
																logger.info('Self-training job completed: iteration: %d, stage: 2.'					,			lowerCamelCase       )

								UpperCAmelCase__             =							iteration
								UpperCAmelCase__             =							data_dir_format(iteration + 1       )

								UpperCAmelCase__             =							AutoConfig.from_pretrained(os.path.join(lowerCamelCase					,			'best-checkpoint'       )       )
								UpperCAmelCase__             =							config.idalabel
								UpperCAmelCase__             =							os.path.join(lowerCamelCase					,			'eval_results_best-checkpoint.json'       )
								UpperCAmelCase__             =							os.path.join(lowerCamelCase					,			'test_results_best-checkpoint.json'       )
								assert os.path.exists(lowerCamelCase       )

								with open(lowerCamelCase					,			'r'       ) as f:
												UpperCAmelCase__             =							float(json.load(lowerCamelCase       )[args.eval_metric]       )
								UpperCAmelCase__             =							os.path.join(lowerCamelCase					,			'infer_output_best-checkpoint.csv'       )
								assert os.path.exists(lowerCamelCase       )
								# Loading the dataset from local csv or json files.
								UpperCAmelCase__             =							load_dataset(args.data_file_extension					,			data_files={'data': data_files['infer']}       )['data']
								UpperCAmelCase__             =							load_dataset('csv'					,			data_files={'data': infer_output_file}       )['data']

								if accelerator.is_main_process:
												os.makedirs(lowerCamelCase					,			exist_ok=lowerCamelCase       )
												shutil.copy(lowerCamelCase					,			os.path.join(lowerCamelCase					,			f'''eval_results_iter-{iteration}.json'''       )       )
												if os.path.exists(lowerCamelCase       ):
																shutil.copy(lowerCamelCase					,			os.path.join(lowerCamelCase					,			f'''test_results_iter-{iteration}.json'''       )       )
												create_pseudo_labeled_data(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )
								accelerator.wait_for_everyone()

								UpperCAmelCase__             =							os.path.join(lowerCamelCase					,			f'''train_pseudo.{args.data_file_extension}'''       )

								if args.evaluation_strategy != IntervalStrategy.NO.value:
												UpperCAmelCase__             =							eval_result

												if best_iteration is None:
																UpperCAmelCase__             =							new_iteration
																UpperCAmelCase__             =							new_eval_result
												else:
																if new_eval_result - best_eval_result > args.early_stopping_threshold:
																				UpperCAmelCase__             =							new_iteration
																				UpperCAmelCase__             =							new_eval_result
																				UpperCAmelCase__             =							0
																else:
																				if new_eval_result == best_eval_result:
																								UpperCAmelCase__             =							new_iteration
																								UpperCAmelCase__             =							new_eval_result
																				early_stopping_patience_counter += 1

																if early_stopping_patience_counter >= args.early_stopping_patience:
																				UpperCAmelCase__             =							True

								progress_bar.update(1       )

								if should_training_stop:
												break

				if best_iteration is not None:
								# Save the best iteration
								logger.info('Best iteration: %d'					,			lowerCamelCase       )
								logger.info('Best evaluation result: %s = %f'					,			args.eval_metric					,			lowerCamelCase       )
								accelerator.wait_for_everyone()
								if accelerator.is_main_process:
												shutil.copy(
												    os.path.join(lowerCamelCase					,			f'''eval_results_iter-{iteration}.json'''       )					,			os.path.join(lowerCamelCase					,			'eval_results_best-iteration.json'       )					,			)
				else:
								# Assume that the last iteration is the best
								logger.info('Best iteration: %d'					,			args.max_selftrain_iterations - 1       )
								logger.info('Best evaluation result: %s = %f'					,			args.eval_metric					,			lowerCamelCase       )
								accelerator.wait_for_everyone()
								if accelerator.is_main_process:
												shutil.copy(
												    os.path.join(lowerCamelCase					,			f'''eval_results_iter-{args.max_selftrain_iterations - 1}.json'''       )					,			os.path.join(lowerCamelCase					,			'eval_results_best-iteration.json'       )					,			)








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()





