




'''simple docstring'''



import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


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





@dataclass
class      lowerCAmelCase :
       __lowercase	:   str										=      field(
           metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''})
       __lowercase	:   Optional[str]										=      field(
           default=__lowerCAmelCase					, metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''})
       __lowercase	:   Optional[str]										=      field(
           default='''NER'''					, metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''})
       __lowercase	:   Optional[str]										=      field(
           default=__lowerCAmelCase					, metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''})
       __lowercase	:   bool										=      field(default=__lowerCAmelCase					, metadata={'''help''': '''Set this flag to use fast tokenization.'''})
       # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
       # or just modify its tokenizer_config.json.
       __lowercase	:   Optional[str]										=      field(
           default=__lowerCAmelCase					, metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}					, )





@dataclass
class      lowerCAmelCase :
       __lowercase	:   str										=      field(
           metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''})
       __lowercase	:   Optional[str]										=      field(
           default=__lowerCAmelCase					, metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}					, )
       __lowercase	:   int										=      field(
           default=128					, metadata={
               '''help''': (
                   '''The maximum total input sequence length after tokenization. Sequences longer '''
                   '''than this will be truncated, sequences shorter will be padded.'''
               )
           }					, )
       __lowercase	:   bool										=      field(
           default=__lowerCAmelCase					, metadata={'''help''': '''Overwrite the cached training and evaluation sets'''})







def       _UpperCamelCase			()-> Tuple:




     '''simple docstring'''





     __snake_case            =	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
     if len(sys.argv						) == 2 and sys.argv[1].endswith('''.json'''						):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          __snake_case       ,     __snake_case       ,     __snake_case            =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
     else:
          __snake_case       ,     __snake_case       ,     __snake_case            =	parser.parse_args_into_dataclasses()

     if (
         os.path.exists(training_args.output_dir						)
         and os.listdir(training_args.output_dir						)
         and training_args.do_train
         and not training_args.overwrite_output_dir
     ):
          raise ValueError(
              f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
              ''' --overwrite_output_dir to overcome.'''						)

     __snake_case            =	import_module('''tasks'''						)
     try:
          __snake_case            =	getattr(_lowerCamelCase ,						model_args.task_type						)
          __snake_case            =	token_classification_task_clazz()
     except AttributeError:
          raise ValueError(
              f'''Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '''
              f'''Available tasks classes are: {TokenClassificationTask.__subclasses__()}'''						)

     # Setup logging
     logging.basicConfig(
         format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' ,						datefmt='''%m/%d/%Y %H:%M:%S''' ,						level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN ,						)
     logger.warning(
         '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' ,						training_args.local_rank ,						training_args.device ,						training_args.n_gpu ,						bool(training_args.local_rank != -1						) ,						training_args.fpaa ,						)
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank						):
          transformers.utils.logging.set_verbosity_info()
          transformers.utils.logging.enable_default_handler()
          transformers.utils.logging.enable_explicit_format()
     logger.info('''Training/evaluation parameters %s''' ,						_lowerCamelCase						)

     # Set seed
     set_seed(training_args.seed						)

     # Prepare CONLL-2003 task
     __snake_case            =	token_classification_task.get_labels(data_args.labels						)
     __snake_case            =	dict(enumerate(_lowerCamelCase						)						)
     __snake_case            =	len(_lowerCamelCase						)

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.

     __snake_case            =	AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path ,						num_labels=_lowerCamelCase ,						idalabel=_lowerCamelCase ,						labelaid={label: i for i, label in enumerate(_lowerCamelCase						)} ,						cache_dir=model_args.cache_dir ,						)
     __snake_case            =	AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,						cache_dir=model_args.cache_dir ,						use_fast=model_args.use_fast ,						)
     __snake_case            =	AutoModelForTokenClassification.from_pretrained(
         model_args.model_name_or_path ,						from_tf=bool('''.ckpt''' in model_args.model_name_or_path						) ,						config=_lowerCamelCase ,						cache_dir=model_args.cache_dir ,						)

     # Get datasets
     __snake_case            =	(
         TokenClassificationDataset(
             token_classification_task=_lowerCamelCase ,						data_dir=data_args.data_dir ,						tokenizer=_lowerCamelCase ,						labels=_lowerCamelCase ,						model_type=config.model_type ,						max_seq_length=data_args.max_seq_length ,						overwrite_cache=data_args.overwrite_cache ,						mode=Split.train ,						)
         if training_args.do_train
         else None
     )
     __snake_case            =	(
         TokenClassificationDataset(
             token_classification_task=_lowerCamelCase ,						data_dir=data_args.data_dir ,						tokenizer=_lowerCamelCase ,						labels=_lowerCamelCase ,						model_type=config.model_type ,						max_seq_length=data_args.max_seq_length ,						overwrite_cache=data_args.overwrite_cache ,						mode=Split.dev ,						)
         if training_args.do_eval
         else None
     )

     def align_predictions(_lowerCamelCase				:  np.ndarray ,						_lowerCamelCase				:  np.ndarray						) -> Tuple[List[int], List[int]]:
          __snake_case            =	np.argmax(_lowerCamelCase ,						axis=2						)

          __snake_case       ,     __snake_case            =	preds.shape

          __snake_case            =	[[] for _ in range(_lowerCamelCase						)]
          __snake_case            =	[[] for _ in range(_lowerCamelCase						)]

          for i in range(_lowerCamelCase						):
               for j in range(_lowerCamelCase						):
                    if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
                         out_label_list[i].append(label_map[label_ids[i][j]]						)
                         preds_list[i].append(label_map[preds[i][j]]						)

          return preds_list, out_label_list

     def compute_metrics(_lowerCamelCase				:  EvalPrediction						) -> Dict:
          __snake_case       ,     __snake_case            =	align_predictions(p.predictions ,						p.label_ids						)
          return {
              "accuracy_score": accuracy_score(_lowerCamelCase ,						_lowerCamelCase						),
              "precision": precision_score(_lowerCamelCase ,						_lowerCamelCase						),
              "recall": recall_score(_lowerCamelCase ,						_lowerCamelCase						),
              "f1": fa_score(_lowerCamelCase ,						_lowerCamelCase						),
          }

     # Data collator
     __snake_case            =	DataCollatorWithPadding(_lowerCamelCase ,						pad_to_multiple_of=8						) if training_args.fpaa else None

     # Initialize our Trainer
     __snake_case            =	Trainer(
         model=_lowerCamelCase ,						args=_lowerCamelCase ,						train_dataset=_lowerCamelCase ,						eval_dataset=_lowerCamelCase ,						compute_metrics=_lowerCamelCase ,						data_collator=_lowerCamelCase ,						)

     # Training
     if training_args.do_train:
          trainer.train(
              model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
          trainer.save_model()
          # For convenience, we also re-save the tokenizer to the same directory,
          # so that you can share your model easily on huggingface.co/models =)
          if trainer.is_world_process_zero():
               tokenizer.save_pretrained(training_args.output_dir						)

    # Evaluation
     __snake_case            =	{}
     if training_args.do_eval:
          logger.info('''*** Evaluate ***'''						)

          __snake_case            =	trainer.evaluate()

          __snake_case            =	os.path.join(training_args.output_dir ,						'''eval_results.txt'''						)
          if trainer.is_world_process_zero():
               with open(_lowerCamelCase ,						'''w'''						) as writer:
                    logger.info('''***** Eval results *****'''						)
                    for key, value in result.items():
                         logger.info('''  %s = %s''' ,						_lowerCamelCase ,						_lowerCamelCase						)
                         writer.write('''%s = %s\n''' % (key, value)						)

               results.update(_lowerCamelCase						)

    # Predict
     if training_args.do_predict:
          __snake_case            =	TokenClassificationDataset(
              token_classification_task=_lowerCamelCase ,						data_dir=data_args.data_dir ,						tokenizer=_lowerCamelCase ,						labels=_lowerCamelCase ,						model_type=config.model_type ,						max_seq_length=data_args.max_seq_length ,						overwrite_cache=data_args.overwrite_cache ,						mode=Split.test ,						)

          __snake_case       ,     __snake_case       ,     __snake_case            =	trainer.predict(_lowerCamelCase						)
          __snake_case       ,     __snake_case            =	align_predictions(_lowerCamelCase ,						_lowerCamelCase						)

          __snake_case            =	os.path.join(training_args.output_dir ,						'''test_results.txt'''						)
          if trainer.is_world_process_zero():
               with open(_lowerCamelCase ,						'''w'''						) as writer:
                    for key, value in metrics.items():
                         logger.info('''  %s = %s''' ,						_lowerCamelCase ,						_lowerCamelCase						)
                         writer.write('''%s = %s\n''' % (key, value)						)

        # Save predictions
          __snake_case            =	os.path.join(training_args.output_dir ,						'''test_predictions.txt'''						)
          if trainer.is_world_process_zero():
               with open(_lowerCamelCase ,						'''w'''						) as writer:
                    with open(os.path.join(data_args.data_dir ,						'''test.txt'''						) ,						'''r'''						) as f:
                         token_classification_task.write_predictions_to_file(_lowerCamelCase ,						_lowerCamelCase ,						_lowerCamelCase						)

     return results







def       _UpperCamelCase			(_lowerCamelCase				:  Tuple						)-> int:




     '''simple docstring'''





     main()


if __name__ == "__main__":
     main()











import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


_lowercase    	=		os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F"{bindir}/../../examples/pytorch/translation"):
   from run_translation import main  # noqa


set_seed(42)
_lowercase    	=		"sshleifer/student_marian_en_ro_6_1"
_lowercase    	=		"sshleifer/tiny-mbart"



@require_torch
class      _UpperCAmelCase      (							A__  ):




     def 		snake_case_		(					self			,    a__=False			,    a__=None			,    a__=True			,    a__=True			,    a__=True			,    a__=True			,    ):
         A__					=       self.run_trainer(
             eval_steps=1			,    max_len=1_2			,    model_name=a__			,    num_train_epochs=1			,    distributed=a__			,    extra_args_str=a__			,    predict_with_generate=a__			,    do_train=a__			,    do_eval=a__			,    do_predict=a__			,    )
         A__					=       TrainerState.load_from_json(os.path.join(a__			,    '''trainer_state.json''')).log_history

         if not do_eval:
             return

         A__					=       [log for log in logs if '''eval_loss''' in log.keys()]

         A__					=       eval_metrics[0]
         if predict_with_generate:
             assert "eval_bleu" in first_step_stats

             A__					=       eval_metrics[-1]
             assert isinstance(last_step_stats['''eval_bleu''']			,    a__)
             assert not math.isnan(float(last_step_stats['''eval_loss'''])), "eval_loss must not be `nan`"




     @require_torch_non_multi_gpu
     def 		snake_case_		(					self):
         self.run_seqaseq_quick()




     @require_torch_multi_gpu
     def 		snake_case_		(					self):
         self.run_seqaseq_quick(distributed=a__)




     @require_torch_multi_gpu
     def 		snake_case_		(					self):
         self.run_seqaseq_quick(distributed=a__)




     @unittest.skip('''Requires an update of the env running those tests''')
     @require_torch_multi_gpu
     @require_fairscale
     def 		snake_case_		(					self):
         self.run_seqaseq_quick(distributed=a__			,    extra_args_str='''--sharded_ddp simple''')




     @unittest.skip('''Requires an update of the env running those tests''')
     @require_torch_multi_gpu
     @require_fairscale
     def 		snake_case_		(					self):
         self.run_seqaseq_quick(distributed=a__			,    extra_args_str='''--sharded_ddp simple --fp16''')




     @unittest.skip('''Requires an update of the env running those tests''')
     @require_torch_multi_gpu
     @require_fairscale
     def 		snake_case_		(					self):
         self.run_seqaseq_quick(distributed=a__			,    extra_args_str='''--sharded_ddp zero_dp_2'''			,    predict_with_generate=a__)




     @unittest.skip('''Requires an update of the env running those tests''')
     @require_torch_multi_gpu
     @require_fairscale
     def 		snake_case_		(					self):
         self.run_seqaseq_quick(
             distributed=a__			,    extra_args_str='''--sharded_ddp zero_dp_2 --fp16'''			,    predict_with_generate=a__)




     @require_apex
     @require_torch_gpu
     def 		snake_case_		(					self):
         # XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
         # program and it breaks other tests that run from the same pytest worker, therefore until this is
         # sorted out it must be run only in an external program, that is distributed=True in this
         # test and only under one or more gpus - if we want cpu will need to make a special test
         #
         # specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
         # 2nd main() call it botches the future eval.
         #
         self.run_seqaseq_quick(distributed=a__			,    extra_args_str='''--fp16 --fp16_backend=apex''')
         # test 2nd time - was getting eval_loss': nan'
         # to reproduce the problem set distributed=False
         self.run_seqaseq_quick(distributed=a__			,    extra_args_str='''--fp16 --fp16_backend=apex''')




     @parameterized.expand(['''base''', '''low''', '''high''', '''mixed'''])
     @require_torch_multi_gpu
     def 		snake_case_		(					self			,    a__):
         # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
         A__					=       {
             # test with the default log_level - should be info and thus log info once
             '''base''': {'''extra_args_str''': '''''', '''n_matches''': 1},
             # test with low log_level and log_level_replica - should be noisy on all processes
             # now the info string should appear twice on 2 processes
             '''low''': {'''extra_args_str''': '''--log_level debug --log_level_replica debug''', '''n_matches''': 2},
             # test with high log_level and low log_level_replica
             # now the info string should appear once only on the replica
             '''high''': {'''extra_args_str''': '''--log_level error --log_level_replica debug''', '''n_matches''': 1},
             # test with high log_level and log_level_replica - should be quiet on all processes
             '''mixed''': {'''extra_args_str''': '''--log_level error --log_level_replica error''', '''n_matches''': 0},
         }

         A__					=       experiments[experiment_id]
         A__					=       {'''distributed''': True, '''predict_with_generate''': False, '''do_eval''': False, '''do_predict''': False}
         A__					=       '''Running training'''
         with CaptureStderr() as cl:
             self.run_seqaseq_quick(**a__			,    extra_args_str=data['''extra_args_str'''])
         A__					=       len(re.findall(a__			,    cl.err))
         self.assertEqual(a__			,    data['''n_matches'''])




     @slow
     def 		snake_case_		(					self):
         A__					=       self.run_trainer(
             eval_steps=2			,    max_len=1_2_8			,    model_name=a__			,    learning_rate=3e-4			,    num_train_epochs=1_0			,    distributed=a__			,    )

         # Check metrics
         A__					=       TrainerState.load_from_json(os.path.join(a__			,    '''trainer_state.json''')).log_history
         A__					=       [log for log in logs if '''eval_loss''' in log.keys()]
         A__					=       eval_metrics[0]
         A__					=       eval_metrics[-1]

         assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
         assert isinstance(last_step_stats['''eval_bleu''']			,    a__)

         # test if do_predict saves generations and metrics
         A__					=       os.listdir(a__)
         A__					=       {os.path.basename(a__) for p in contents}
         assert "generated_predictions.txt" in contents
         assert "predict_results.json" in contents




     @slow
     @require_bitsandbytes
     def 		snake_case_		(					self):
         from transformers.training_args import OptimizerNames

         def train_and_return_metrics(a__) -> Tuple[int, float]:
             A__					=       '''--skip_memory_metrics 0'''

             A__					=       self.run_trainer(
                 max_len=1_2_8			,    model_name=a__			,    learning_rate=3e-4			,    num_train_epochs=1			,    optim=a__			,    distributed=a__			,    extra_args_str=a__			,    do_eval=a__			,    do_predict=a__			,    n_gpus_to_use=1			,    )

             # Check metrics
             A__					=       TrainerState.load_from_json(Path(a__			,    '''trainer_state.json''')).log_history
             A__					=       int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**2_0)
             A__					=       int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**2_0)

             A__					=       logs[0]['''train_loss''']
             return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

         A__			,						A__			,						A__					=       train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value)
         A__			,						A__			,						A__					=       train_and_return_metrics(OptimizerNames.ADAMW_BNB.value)

         A__					=       gpu_alloc_mem_orig - gpu_alloc_mem_bnb

         A__					=       gpu_peak_mem_orig + gpu_alloc_mem_orig
         A__					=       gpu_peak_mem_bnb + gpu_alloc_mem_bnb
         A__					=       gpu_total_mem_orig - gpu_total_mem_bnb

         # sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
         # doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
         # in 2 bytes and the diff in optim memory usage is derived as so:
         #
         # - normal 25*8=~200MB (8 bytes per param)
         # - bnb    25*2= ~50MB (2 bytes per param)
         #
         # Thus we should expect ~150MB total memory saved.
         #
         # Peak memory should be the same - the total should be different by about that same margin
         #
         # After leaving a small margin to accommodate for differences between gpus let's check
         # that we have at least 120MB in savings
         A__					=       1_2_0

         # uncomment the following if this test starts failing - requires py38 for a new print feature
         # gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
         # print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
         # print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
         # print(f"{gpu_alloc_mem_diff=}MB")
         # print(f"{gpu_peak_mem_diff=}MB")
         # print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
         # print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

         self.assertGreater(
             a__			,    a__			,    '''should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got'''
             F" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"
             F" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB"			,    )

         self.assertGreater(
             a__			,    a__			,    '''should use ~150MB less total gpu memory with BNB, compared to without it for this model but got'''
             F" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"
             F" gpu_total_mem_bnb={gpu_total_mem_bnb}MB"			,    )

         self.assertEqual(
             a__			,    a__			,    F"loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}")




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__ = 3e-3			,    a__ = "adafactor"			,    a__ = False			,    a__ = None			,    a__ = 0			,    a__ = True			,    a__ = True			,    a__ = True			,    a__ = True			,    a__ = None			,    ):
         A__					=       self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
         A__					=       self.get_auto_remove_tmp_dir()
         A__					=       F"\n            --model_name_or_path {model_name}\n            --train_file {data_dir}/train.json\n            --validation_file {data_dir}/val.json\n            --test_file {data_dir}/test.json\n            --output_dir {output_dir}\n            --overwrite_output_dir\n            --max_train_samples 8\n            --max_source_length {max_len}\n            --max_target_length {max_len}\n            --do_train\n            --num_train_epochs {str(a__)}\n            --per_device_train_batch_size 4\n            --learning_rate {learning_rate}\n            --warmup_steps 8\n            --logging_steps 0\n            --logging_strategy no\n            --save_steps {str(a__)}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        ".split()

         A__					=       F"\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(a__)}\n        ".split()

         A__					=       '''
            --do_predict
        '''.split()

         A__					=       []
         if do_train:
             args += args_train

         if do_eval:
             args += args_eval

         if do_predict:
             args += args_predict

         if predict_with_generate:
             args += "--predict_with_generate".split()

         if do_train:
             if optim == "adafactor":
                 args += "--adafactor".split()
             else:
                 args += F"--optim {optim}".split()

         if extra_args_str is not None:
             args += extra_args_str.split()

         if distributed:
             if n_gpus_to_use is None:
                 A__					=       get_gpu_count()
             A__					=       get_torch_dist_unique_port()
             A__					=       F"\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            ".split()
             A__					=       [sys.executable] + distributed_args + args
             # keep for quick debug
             # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
             execute_subprocess_async(a__			,    env=self.get_env())
         else:
             A__					=       ['''run_translation.py'''] + args
             with patch.object(a__			,    '''argv'''			,    a__):
                 main()

         return output_dir
