


'''simple docstring'''

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

import sentencepiece as spm

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


a_  :				List[Any]						= logging.get_logger(__name__)

a_  :				Any						= """▁"""

a_  :				Dict						= {"""vocab_file""": """sentencepiece.bpe.model"""}

a_  :				Dict						= {
    """vocab_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"""
        ),
    }
}

a_  :				str						= {
    """facebook/nllb-200-distilled-600M""": 1024,
}

# fmt: off
a_  :				List[str]						= ["""ace_Arab""", """ace_Latn""", """acm_Arab""", """acq_Arab""", """aeb_Arab""", """afr_Latn""", """ajp_Arab""", """aka_Latn""", """amh_Ethi""", """apc_Arab""", """arb_Arab""", """ars_Arab""", """ary_Arab""", """arz_Arab""", """asm_Beng""", """ast_Latn""", """awa_Deva""", """ayr_Latn""", """azb_Arab""", """azj_Latn""", """bak_Cyrl""", """bam_Latn""", """ban_Latn""", """bel_Cyrl""", """bem_Latn""", """ben_Beng""", """bho_Deva""", """bjn_Arab""", """bjn_Latn""", """bod_Tibt""", """bos_Latn""", """bug_Latn""", """bul_Cyrl""", """cat_Latn""", """ceb_Latn""", """ces_Latn""", """cjk_Latn""", """ckb_Arab""", """crh_Latn""", """cym_Latn""", """dan_Latn""", """deu_Latn""", """dik_Latn""", """dyu_Latn""", """dzo_Tibt""", """ell_Grek""", """eng_Latn""", """epo_Latn""", """est_Latn""", """eus_Latn""", """ewe_Latn""", """fao_Latn""", """pes_Arab""", """fij_Latn""", """fin_Latn""", """fon_Latn""", """fra_Latn""", """fur_Latn""", """fuv_Latn""", """gla_Latn""", """gle_Latn""", """glg_Latn""", """grn_Latn""", """guj_Gujr""", """hat_Latn""", """hau_Latn""", """heb_Hebr""", """hin_Deva""", """hne_Deva""", """hrv_Latn""", """hun_Latn""", """hye_Armn""", """ibo_Latn""", """ilo_Latn""", """ind_Latn""", """isl_Latn""", """ita_Latn""", """jav_Latn""", """jpn_Jpan""", """kab_Latn""", """kac_Latn""", """kam_Latn""", """kan_Knda""", """kas_Arab""", """kas_Deva""", """kat_Geor""", """knc_Arab""", """knc_Latn""", """kaz_Cyrl""", """kbp_Latn""", """kea_Latn""", """khm_Khmr""", """kik_Latn""", """kin_Latn""", """kir_Cyrl""", """kmb_Latn""", """kon_Latn""", """kor_Hang""", """kmr_Latn""", """lao_Laoo""", """lvs_Latn""", """lij_Latn""", """lim_Latn""", """lin_Latn""", """lit_Latn""", """lmo_Latn""", """ltg_Latn""", """ltz_Latn""", """lua_Latn""", """lug_Latn""", """luo_Latn""", """lus_Latn""", """mag_Deva""", """mai_Deva""", """mal_Mlym""", """mar_Deva""", """min_Latn""", """mkd_Cyrl""", """plt_Latn""", """mlt_Latn""", """mni_Beng""", """khk_Cyrl""", """mos_Latn""", """mri_Latn""", """zsm_Latn""", """mya_Mymr""", """nld_Latn""", """nno_Latn""", """nob_Latn""", """npi_Deva""", """nso_Latn""", """nus_Latn""", """nya_Latn""", """oci_Latn""", """gaz_Latn""", """ory_Orya""", """pag_Latn""", """pan_Guru""", """pap_Latn""", """pol_Latn""", """por_Latn""", """prs_Arab""", """pbt_Arab""", """quy_Latn""", """ron_Latn""", """run_Latn""", """rus_Cyrl""", """sag_Latn""", """san_Deva""", """sat_Beng""", """scn_Latn""", """shn_Mymr""", """sin_Sinh""", """slk_Latn""", """slv_Latn""", """smo_Latn""", """sna_Latn""", """snd_Arab""", """som_Latn""", """sot_Latn""", """spa_Latn""", """als_Latn""", """srd_Latn""", """srp_Cyrl""", """ssw_Latn""", """sun_Latn""", """swe_Latn""", """swh_Latn""", """szl_Latn""", """tam_Taml""", """tat_Cyrl""", """tel_Telu""", """tgk_Cyrl""", """tgl_Latn""", """tha_Thai""", """tir_Ethi""", """taq_Latn""", """taq_Tfng""", """tpi_Latn""", """tsn_Latn""", """tso_Latn""", """tuk_Latn""", """tum_Latn""", """tur_Latn""", """twi_Latn""", """tzm_Tfng""", """uig_Arab""", """ukr_Cyrl""", """umb_Latn""", """urd_Arab""", """uzn_Latn""", """vec_Latn""", """vie_Latn""", """war_Latn""", """wol_Latn""", """xho_Latn""", """ydd_Hebr""", """yor_Latn""", """yue_Hant""", """zho_Hans""", """zho_Hant""", """zul_Latn"""]





class      snake_case    (    lowercase			):





  """simple docstring"""




  _lowerCamelCase		    =  VOCAB_FILES_NAMES
  _lowerCamelCase		    =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowerCamelCase		    =  PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase		    =  ["input_ids", "attention_mask"]

  _lowerCamelCase		    =  []
  _lowerCamelCase		    =  []






  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase="<s>"			,  UpperCamelCase="</s>"			,  UpperCamelCase="</s>"			,  UpperCamelCase="<s>"			,  UpperCamelCase="<unk>"			,  UpperCamelCase="<pad>"			,  UpperCamelCase="<mask>"			,  UpperCamelCase=None			,  UpperCamelCase=None			,  UpperCamelCase=None			,  UpperCamelCase = None			,  UpperCamelCase=None			,  UpperCamelCase=False			,  **UpperCamelCase			,  ):







   """simple docstring"""
   # Mask token behave like a normal word, i.e. include the space before it
   lowerCamelCase_      =  AddedToken(UpperCamelCase			,  lstrip=UpperCamelCase			,  rstrip=UpperCamelCase     ) if isinstance(UpperCamelCase			,  UpperCamelCase     ) else mask_token

   lowerCamelCase_      =  {} if sp_model_kwargs is None else sp_model_kwargs
   lowerCamelCase_      =  legacy_behaviour

   super().__init__(
       bos_token=UpperCamelCase			,  eos_token=UpperCamelCase			,  unk_token=UpperCamelCase			,  sep_token=UpperCamelCase			,  cls_token=UpperCamelCase			,  pad_token=UpperCamelCase			,  mask_token=UpperCamelCase			,  tokenizer_file=UpperCamelCase			,  src_lang=UpperCamelCase			,  tgt_lang=UpperCamelCase			,  additional_special_tokens=UpperCamelCase			,  sp_model_kwargs=self.sp_model_kwargs			,  legacy_behaviour=UpperCamelCase			,  **UpperCamelCase			,  )

   lowerCamelCase_      =  spm.SentencePieceProcessor(**self.sp_model_kwargs     )
   self.sp_model.Load(str(UpperCamelCase     )     )
   lowerCamelCase_      =  vocab_file
   # Original fairseq vocab and spm vocab must be "aligned":
   # Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
   # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
   # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
   # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

   # Mimic fairseq token-to-id alignment for the first 4 token
   lowerCamelCase_      =  {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

   # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
   lowerCamelCase_      =  1

   lowerCamelCase_      =  len(self.sp_model     )
   lowerCamelCase_      =  {
       code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(UpperCamelCase     )
   }
   lowerCamelCase_      =  {v: k for k, v in self.lang_code_to_id.items()}
   lowerCamelCase_      =  len(self.sp_model     ) + len(self.lang_code_to_id     ) + self.fairseq_offset

   self.fairseq_tokens_to_ids.update(self.lang_code_to_id     )
   lowerCamelCase_      =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
   lowerCamelCase_      =  list(self.lang_code_to_id.keys()     )

   if additional_special_tokens is not None:
    # Only add those special tokens if they are not already there.
    self._additional_special_tokens.extend(
        [t for t in additional_special_tokens if t not in self._additional_special_tokens]     )

   lowerCamelCase_      =  src_lang if src_lang is not None else "eng_Latn"
   lowerCamelCase_      =  self.lang_code_to_id[self._src_lang]
   lowerCamelCase_      =  tgt_lang
   self.set_src_lang_special_tokens(self._src_lang     )






  def __getstate__(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.__dict__.copy()
   lowerCamelCase_      =  None
   lowerCamelCase_      =  self.sp_model.serialized_model_proto()
   return state






  def __setstate__(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  d

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

   lowerCamelCase_      =  spm.SentencePieceProcessor(**self.sp_model_kwargs     )
   self.sp_model.LoadFromSerializedProto(self.sp_model_proto     )






  @property
  def   snake_case		(    self     ):







   """simple docstring"""
   return len(self.sp_model     ) + len(self.lang_code_to_id     ) + self.fairseq_offset + 1  # Plus 1 for the mask token






  @property
  def   snake_case		(    self     ):







   """simple docstring"""
   return self._src_lang






  @src_lang.setter
  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  new_src_lang
   self.set_src_lang_special_tokens(self._src_lang     )






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







   """simple docstring"""

   if already_has_special_tokens:
    return super().get_special_tokens_mask(
        token_ids_a=UpperCamelCase			,  token_ids_a=UpperCamelCase			,  already_has_special_tokens=UpperCamelCase     )

   lowerCamelCase_      =  [1] * len(self.prefix_tokens     )
   lowerCamelCase_      =  [1] * len(self.suffix_tokens     )
   if token_ids_a is None:
    return prefix_ones + ([0] * len(UpperCamelCase     )) + suffix_ones
   return prefix_ones + ([0] * len(UpperCamelCase     )) + ([0] * len(UpperCamelCase     )) + suffix_ones






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







   """simple docstring"""
   if token_ids_a is None:
    return self.prefix_tokens + token_ids_a + self.suffix_tokens
   # We don't expect to process pairs, but leave the pair logic for API consistency
   return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens






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







   """simple docstring"""
   lowerCamelCase_      =  [self.sep_token_id]
   lowerCamelCase_      =  [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 + sep + token_ids_a + sep     ) * [0]






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   if src_lang is None or tgt_lang is None:
    raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"     )
   lowerCamelCase_      =  src_lang
   lowerCamelCase_      =  self(UpperCamelCase			,  add_special_tokens=UpperCamelCase			,  return_tensors=UpperCamelCase			,  **UpperCamelCase     )
   lowerCamelCase_      =  self.convert_tokens_to_ids(UpperCamelCase     )
   lowerCamelCase_      =  tgt_lang_id
   return inputs






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  {self.convert_ids_to_tokens(UpperCamelCase     ): i for i in range(self.vocab_size     )}
   vocab.update(self.added_tokens_encoder     )
   return vocab






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   return self.sp_model.encode(UpperCamelCase			,  out_type=UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   if token in self.fairseq_tokens_to_ids:
    return self.fairseq_tokens_to_ids[token]
   lowerCamelCase_      =  self.sp_model.PieceToId(UpperCamelCase     )

   # Need to return unknown token if the SP model returned 0
   return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   if index in self.fairseq_ids_to_tokens:
    return self.fairseq_ids_to_tokens[index]
   return self.sp_model.IdToPiece(index - self.fairseq_offset     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  "".join(UpperCamelCase     ).replace(UpperCamelCase			,  " "     ).strip()
   return out_string






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







   """simple docstring"""
   if not os.path.isdir(UpperCamelCase     ):
    logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
    return
   lowerCamelCase_      =  os.path.join(
       UpperCamelCase			,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

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

   return (out_vocab_file,)






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = "eng_Latn"			,  UpperCamelCase = None			,  UpperCamelCase = "fra_Latn"			,  **UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  src_lang
   lowerCamelCase_      =  tgt_lang
   return super().prepare_seqaseq_batch(UpperCamelCase			,  UpperCamelCase			,  **UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   return self.set_src_lang_special_tokens(self.src_lang     )






  def   snake_case		(    self     ):







   """simple docstring"""
   return self.set_tgt_lang_special_tokens(self.tgt_lang     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  self.lang_code_to_id[src_lang]
   if self.legacy_behaviour:
    lowerCamelCase_      =  []
    lowerCamelCase_      =  [self.eos_token_id, self.cur_lang_code]
   else:
    lowerCamelCase_      =  [self.cur_lang_code]
    lowerCamelCase_      =  [self.eos_token_id]







  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  self.lang_code_to_id[lang]
   if self.legacy_behaviour:
    lowerCamelCase_      =  []
    lowerCamelCase_      =  [self.eos_token_id, self.cur_lang_code]
   else:
    lowerCamelCase_      =  [self.cur_lang_code]
    lowerCamelCase_      =  [self.eos_token_id]







'''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



