
"""simple docstring"""





import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__magic_name__       =			logging.get_logger(__name__)

__magic_name__       =			{"vocab_file": "spiece.model"}

__magic_name__       =			{
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    }
}

__magic_name__       =			{
    "google/bigbird-roberta-base": 4096,
    "google/bigbird-roberta-large": 4096,
    "google/bigbird-base-trivia-itc": 4096,
}
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  Tuple						   =		VOCAB_FILES_NAMES
		__lowercase							:  List[Any]						   =		PRETRAINED_VOCAB_FILES_MAP
		__lowercase							:  Any						   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase							:  Union[str, Any]						   =		['''input_ids''', '''attention_mask''']
		__lowercase							:  List[int]						   =		[]




		def __init__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__="<unk>"							,  lowerCAmelCase__="<s>"							,  lowerCAmelCase__="</s>"							,  lowerCAmelCase__="<pad>"							,  lowerCAmelCase__="[SEP]"							,  lowerCAmelCase__="[MASK]"							,  lowerCAmelCase__="[CLS]"							,  lowerCAmelCase__ = None							,  **lowerCAmelCase__							,  ):
				__SCREAMING_SNAKE_CASE            = AddedToken(lowerCAmelCase__							,  lstrip=lowerCAmelCase__							,  rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__							,  lowerCAmelCase__) else bos_token
				__SCREAMING_SNAKE_CASE            = AddedToken(lowerCAmelCase__							,  lstrip=lowerCAmelCase__							,  rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__							,  lowerCAmelCase__) else eos_token
				__SCREAMING_SNAKE_CASE            = AddedToken(lowerCAmelCase__							,  lstrip=lowerCAmelCase__							,  rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__							,  lowerCAmelCase__) else unk_token
				__SCREAMING_SNAKE_CASE            = AddedToken(lowerCAmelCase__							,  lstrip=lowerCAmelCase__							,  rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__							,  lowerCAmelCase__) else pad_token
				__SCREAMING_SNAKE_CASE            = AddedToken(lowerCAmelCase__							,  lstrip=lowerCAmelCase__							,  rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__							,  lowerCAmelCase__) else cls_token
				__SCREAMING_SNAKE_CASE            = AddedToken(lowerCAmelCase__							,  lstrip=lowerCAmelCase__							,  rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__							,  lowerCAmelCase__) else sep_token

				# Mask token behave like a normal word, i.e. include the space before it
				__SCREAMING_SNAKE_CASE            = AddedToken(lowerCAmelCase__							,  lstrip=lowerCAmelCase__							,  rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__							,  lowerCAmelCase__) else mask_token

				__SCREAMING_SNAKE_CASE            = {} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    bos_token=lowerCAmelCase__							,  eos_token=lowerCAmelCase__							,  unk_token=lowerCAmelCase__							,  pad_token=lowerCAmelCase__							,  sep_token=lowerCAmelCase__							,  mask_token=lowerCAmelCase__							,  cls_token=lowerCAmelCase__							,  sp_model_kwargs=self.sp_model_kwargs							,  **lowerCAmelCase__							,  )

				__SCREAMING_SNAKE_CASE            = vocab_file

				__SCREAMING_SNAKE_CASE            = spm.SentencePieceProcessor(**self.sp_model_kwargs)
				self.sp_model.Load(lowerCAmelCase__)




		@property
		def 	snake_case_     (     self):
				return self.sp_model.get_piece_size()




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = {self.convert_ids_to_tokens(lowerCAmelCase__): i for i in range(self.vocab_size)}
				vocab.update(self.added_tokens_encoder)
				return vocab




		def __getstate__(     self):
				__SCREAMING_SNAKE_CASE            = self.__dict__.copy()
				__SCREAMING_SNAKE_CASE            = None
				return state




		def __setstate__(     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = d

				# for backward compatibility
				if not hasattr(self							,  """sp_model_kwargs"""):
						__SCREAMING_SNAKE_CASE            = {}

				__SCREAMING_SNAKE_CASE            = spm.SentencePieceProcessor(**self.sp_model_kwargs)
				self.sp_model.Load(self.vocab_file)




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				return self.sp_model.encode(lowerCAmelCase__							,  out_type=lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				return self.sp_model.piece_to_id(lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = self.sp_model.IdToPiece(lowerCAmelCase__)
				return token




		def 	snake_case_     (     self							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = []
				__SCREAMING_SNAKE_CASE            = """"""
				__SCREAMING_SNAKE_CASE            = False
				for token in tokens:
						# make sure that special tokens are not decoded using sentencepiece model
						if token in self.all_special_tokens:
								if not prev_is_special:
										out_string += " "
								out_string += self.sp_model.decode(lowerCAmelCase__) + token
								__SCREAMING_SNAKE_CASE            = True
								__SCREAMING_SNAKE_CASE            = []
						else:
								current_sub_tokens.append(lowerCAmelCase__)
								__SCREAMING_SNAKE_CASE            = False
				out_string += self.sp_model.decode(lowerCAmelCase__)
				return out_string.strip()




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = True							,  **lowerCAmelCase__							,  ):
				__SCREAMING_SNAKE_CASE            = kwargs.pop("""use_source_tokenizer"""							,  lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = self.convert_ids_to_tokens(lowerCAmelCase__							,  skip_special_tokens=lowerCAmelCase__)

				# To avoid mixing byte-level and unicode for byte-level BPT
				# we need to build string separately for added tokens and byte-level tokens
				# cf. https://github.com/huggingface/transformers/issues/1133
				__SCREAMING_SNAKE_CASE            = []
				__SCREAMING_SNAKE_CASE            = []
				for token in filtered_tokens:
						if skip_special_tokens and token in self.all_special_ids:
								continue
						if token in self.added_tokens_encoder:
								if current_sub_text:
										sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase__))
										__SCREAMING_SNAKE_CASE            = []
								sub_texts.append(lowerCAmelCase__)
						else:
								current_sub_text.append(lowerCAmelCase__)
				if current_sub_text:
						sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase__))

				# Mimic the behavior of the Rust tokenizer:
				# No space before [MASK] and [SEP]
				if spaces_between_special_tokens:
						__SCREAMING_SNAKE_CASE            = re.sub(R""" (\[(MASK|SEP)\])"""							,  R"""\1"""							,  """ """.join(lowerCAmelCase__))
				else:
						__SCREAMING_SNAKE_CASE            = """""".join(lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = (
				    clean_up_tokenization_spaces
				    if clean_up_tokenization_spaces is not None
				    else self.clean_up_tokenization_spaces
				)
				if clean_up_tokenization_spaces:
						__SCREAMING_SNAKE_CASE            = self.clean_up_tokenization(lowerCAmelCase__)
						return clean_text
				else:
						return text




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__ = None):
				if not os.path.isdir(lowerCAmelCase__):
						logger.error(f"Vocabulary path ({save_directory}) should be a directory")
						return
				__SCREAMING_SNAKE_CASE            = os.path.join(
				    lowerCAmelCase__							,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

				if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCAmelCase__) and os.path.isfile(self.vocab_file):
						copyfile(self.vocab_file							,  lowerCAmelCase__)
				elif not os.path.isfile(self.vocab_file):
						with open(lowerCAmelCase__							,  """wb""") as fi:
								__SCREAMING_SNAKE_CASE            = self.sp_model.serialized_model_proto()
								fi.write(lowerCAmelCase__)

				return (out_vocab_file,)




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__ = None):
				if token_ids_a is None:
						return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				__SCREAMING_SNAKE_CASE            = [self.cls_token_id]
				__SCREAMING_SNAKE_CASE            = [self.sep_token_id]
				return cls + token_ids_a + sep + token_ids_a + sep




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = False):
				if already_has_special_tokens:
						return super().get_special_tokens_mask(
						    token_ids_a=lowerCAmelCase__							,  token_ids_a=lowerCAmelCase__							,  already_has_special_tokens=lowerCAmelCase__)

				if token_ids_a is None:
						return [1] + ([0] * len(lowerCAmelCase__)) + [1]
				return [1] + ([0] * len(lowerCAmelCase__)) + [1] + ([0] * len(lowerCAmelCase__)) + [1]




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__ = None):
				__SCREAMING_SNAKE_CASE            = [self.sep_token_id]
				__SCREAMING_SNAKE_CASE            = [self.cls_token_id]
				if token_ids_a is None:
						return len(cls + token_ids_a + sep) * [0]
				return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]








'''simple docstring'''

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


a_  :				Dict						= os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f'''{bindir}/../../examples/pytorch/translation'''):
     from run_translation import main  # noqa


set_seed(42)
a_  :				int						= """sshleifer/student_marian_en_ro_6_1"""
a_  :				str						= """sshleifer/tiny-mbart"""





@require_torch
class      snake_case    (    lowercase			):





  """simple docstring"""




  def   snake_case		(    self			,  UpperCamelCase=False			,  UpperCamelCase=None			,  UpperCamelCase=True			,  UpperCamelCase=True			,  UpperCamelCase=True			,  UpperCamelCase=True			,  ):







   """simple docstring"""
   lowerCamelCase_      =  self.run_trainer(
       eval_steps=1			,  max_len=12			,  model_name=UpperCamelCase			,  num_train_epochs=1			,  distributed=UpperCamelCase			,  extra_args_str=UpperCamelCase			,  predict_with_generate=UpperCamelCase			,  do_train=UpperCamelCase			,  do_eval=UpperCamelCase			,  do_predict=UpperCamelCase			,  )
   lowerCamelCase_      =  TrainerState.load_from_json(os.path.join(UpperCamelCase			,  "trainer_state.json"     )     ).log_history

   if not do_eval:
    return

   lowerCamelCase_      =  [log for log in logs if "eval_loss" in log.keys()]

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

    lowerCamelCase_      =  eval_metrics[-1]
    assert isinstance(last_step_stats["eval_bleu"]			,  UpperCamelCase     )
    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     ):







   """simple docstring"""
   self.run_seqaseq_quick()






  @require_torch_multi_gpu
  def   snake_case		(    self     ):







   """simple docstring"""
   self.run_seqaseq_quick(distributed=UpperCamelCase     )






  @require_torch_multi_gpu
  def   snake_case		(    self     ):







   """simple docstring"""
   self.run_seqaseq_quick(distributed=UpperCamelCase     )






  @unittest.skip("Requires an update of the env running those tests"     )
  @require_torch_multi_gpu
  @require_fairscale
  def   snake_case		(    self     ):







   """simple docstring"""
   self.run_seqaseq_quick(distributed=UpperCamelCase			,  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     ):







   """simple docstring"""
   self.run_seqaseq_quick(distributed=UpperCamelCase			,  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     ):







   """simple docstring"""
   self.run_seqaseq_quick(distributed=UpperCamelCase			,  extra_args_str="--sharded_ddp zero_dp_2"			,  predict_with_generate=UpperCamelCase     )






  @unittest.skip("Requires an update of the env running those tests"     )
  @require_torch_multi_gpu
  @require_fairscale
  def   snake_case		(    self     ):







   """simple docstring"""
   self.run_seqaseq_quick(
       distributed=UpperCamelCase			,  extra_args_str="--sharded_ddp zero_dp_2 --fp16"			,  predict_with_generate=UpperCamelCase     )






  @require_apex
  @require_torch_gpu
  def   snake_case		(    self     ):







   """simple docstring"""
   # 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=UpperCamelCase			,  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=UpperCamelCase			,  extra_args_str="--fp16 --fp16_backend=apex"     )






  @parameterized.expand(["base", "low", "high", "mixed"]     )
  @require_torch_multi_gpu
  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
   lowerCamelCase_      =  {
       # 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},
   }

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






  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.run_trainer(
       eval_steps=2			,  max_len=128			,  model_name=UpperCamelCase			,  learning_rate=3e-4			,  num_train_epochs=10			,  distributed=UpperCamelCase			,  )

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

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

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






  @slow
  @require_bitsandbytes
  def   snake_case		(    self     ):







   """simple docstring"""
   from transformers.training_args import OptimizerNames

   def train_and_return_metrics(UpperCamelCase     ) -> Tuple[int, float]:
    lowerCamelCase_      =  "--skip_memory_metrics 0"

    lowerCamelCase_      =  self.run_trainer(
        max_len=128			,  model_name=UpperCamelCase			,  learning_rate=3e-4			,  num_train_epochs=1			,  optim=UpperCamelCase			,  distributed=UpperCamelCase			,  extra_args_str=UpperCamelCase			,  do_eval=UpperCamelCase			,  do_predict=UpperCamelCase			,  n_gpus_to_use=1			,  )

    # Check metrics
    lowerCamelCase_      =  TrainerState.load_from_json(Path(UpperCamelCase			,  "trainer_state.json"     )     ).log_history
    lowerCamelCase_      =  int(logs[0]["train_mem_gpu_peaked_delta"] / 2**20     )
    lowerCamelCase_      =  int(logs[0]["train_mem_gpu_alloc_delta"] / 2**20     )

    lowerCamelCase_      =  logs[0]["train_loss"]
    return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

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

   lowerCamelCase_      =  gpu_alloc_mem_orig - gpu_alloc_mem_bnb

   lowerCamelCase_      =  gpu_peak_mem_orig + gpu_alloc_mem_orig
   lowerCamelCase_      =  gpu_peak_mem_bnb + gpu_alloc_mem_bnb
   lowerCamelCase_      =  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
   lowerCamelCase_      =  120

   # 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(
       UpperCamelCase			,  UpperCamelCase			,  "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(
       UpperCamelCase			,  UpperCamelCase			,  "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(
       UpperCamelCase			,  UpperCamelCase			,  f'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''     )







  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase = 3e-3			,  UpperCamelCase = "adafactor"			,  UpperCamelCase = False			,  UpperCamelCase = None			,  UpperCamelCase = 0			,  UpperCamelCase = True			,  UpperCamelCase = True			,  UpperCamelCase = True			,  UpperCamelCase = True			,  UpperCamelCase = None			,  ):







   """simple docstring"""
   lowerCamelCase_      =  self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
   lowerCamelCase_      =  self.get_auto_remove_tmp_dir()
   lowerCamelCase_      =  f'''
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(UpperCamelCase     )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(UpperCamelCase     )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

   lowerCamelCase_      =  f'''
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(UpperCamelCase     )}
        '''.split()

   lowerCamelCase_      =  "\n            --do_predict\n        ".split()

   lowerCamelCase_      =  []
   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:
     lowerCamelCase_      =  get_gpu_count()
    lowerCamelCase_      =  get_torch_dist_unique_port()
    lowerCamelCase_      =  f'''
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            '''.split()
    lowerCamelCase_      =  [sys.executable] + distributed_args + args
    # keep for quick debug
    # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
    execute_subprocess_async(UpperCamelCase			,  env=self.get_env()     )
   else:
    lowerCamelCase_      =  ["run_translation.py"] + args
    with patch.object(UpperCamelCase			,  "argv"			,  UpperCamelCase     ):
     main()

   return output_dir



