



'''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


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


set_seed(42)
_lowerCAmelCase      						=	'''sshleifer/student_marian_en_ro_6_1'''
_lowerCAmelCase      						=	'''sshleifer/tiny-mbart'''






@require_torch
class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase=False  ,__UpperCAmelCase=None  ,__UpperCAmelCase=True  ,__UpperCAmelCase=True  ,__UpperCAmelCase=True  ,__UpperCAmelCase=True  ,)    ->		Tuple:
      lowerCAmelCase__ :  Tuple							      =					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__ :  List[Any]							      =					TrainerState.load_from_json(os.path.join(__UpperCAmelCase  ,"""trainer_state.json"""      )      ).log_history

      if not do_eval:
          return

      lowerCAmelCase__ :  List[Any]							      =					[log for log in logs if """eval_loss""" in log.keys()]

      lowerCAmelCase__ :  Optional[Any]							      =					eval_metrics[0]
      if predict_with_generate:
          assert "eval_bleu" in first_step_stats

          lowerCAmelCase__ :  Optional[int]							      =					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 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      self.run_seqaseq_quick()




  @require_torch_multi_gpu
  def 							UpperCAmelCase_	(    self      )    ->		List[Any]:
      self.run_seqaseq_quick(distributed=__UpperCAmelCase      )




  @require_torch_multi_gpu
  def 							UpperCAmelCase_	(    self      )    ->		Tuple:
      self.run_seqaseq_quick(distributed=__UpperCAmelCase      )




  @unittest.skip("""Requires an update of the env running those tests"""      )
  @require_torch_multi_gpu
  @require_fairscale
  def 							UpperCAmelCase_	(    self      )    ->		Any:
      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 							UpperCAmelCase_	(    self      )    ->		Dict:
      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 							UpperCAmelCase_	(    self      )    ->		Dict:
      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 							UpperCAmelCase_	(    self      )    ->		List[Any]:
      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 							UpperCAmelCase_	(    self      )    ->		Tuple:
      # 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 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Any:
      # as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
      lowerCAmelCase__ :  Optional[Any]							      =					{
          # 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__ :  Any							      =					experiments[experiment_id]
      lowerCAmelCase__ :  Optional[Any]							      =					{"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
      lowerCAmelCase__ :  Optional[int]							      =					"""Running training"""
      with CaptureStderr() as cl:
          self.run_seqaseq_quick(**__UpperCAmelCase  ,extra_args_str=data["""extra_args_str"""]      )
      lowerCAmelCase__ :  Union[str, Any]							      =					len(re.findall(__UpperCAmelCase  ,cl.err      )      )
      self.assertEqual(__UpperCAmelCase  ,data["""n_matches"""]      )




  @slow
  def 							UpperCAmelCase_	(    self      )    ->		Optional[Any]:
      lowerCAmelCase__ :  Tuple							      =					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__ :  str							      =					TrainerState.load_from_json(os.path.join(__UpperCAmelCase  ,"""trainer_state.json"""      )      ).log_history
      lowerCAmelCase__ :  Union[str, Any]							      =					[log for log in logs if """eval_loss""" in log.keys()]
      lowerCAmelCase__ :  Optional[Any]							      =					eval_metrics[0]
      lowerCAmelCase__ :  Tuple							      =					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__ :  Optional[Any]							      =					os.listdir(__UpperCAmelCase      )
      lowerCAmelCase__ :  Union[str, Any]							      =					{os.path.basename(__UpperCAmelCase      ) for p in contents}
      assert "generated_predictions.txt" in contents
      assert "predict_results.json" in contents




  @slow
  @require_bitsandbytes
  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      from transformers.training_args import OptimizerNames

      def train_and_return_metrics(__UpperCAmelCase      ) -> Tuple[int, float]:
          lowerCAmelCase__ :  List[Any]							      =					"""--skip_memory_metrics 0"""

          lowerCAmelCase__ :  Dict							      =					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__ :  List[str]							      =					TrainerState.load_from_json(Path(__UpperCAmelCase  ,"""trainer_state.json"""      )      ).log_history
          lowerCAmelCase__ :  Tuple							      =					int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20      )
          lowerCAmelCase__ :  int							      =					int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20      )

          lowerCAmelCase__ :  Any							      =					logs[0]["""train_loss"""]
          return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

      lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ :  Optional[int]							      =					train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value      )
      lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__ :  str							      =					train_and_return_metrics(OptimizerNames.ADAMW_BNB.value      )

      lowerCAmelCase__ :  Optional[Any]							      =					gpu_alloc_mem_orig - gpu_alloc_mem_bnb

      lowerCAmelCase__ :  List[Any]							      =					gpu_peak_mem_orig + gpu_alloc_mem_orig
      lowerCAmelCase__ :  Optional[int]							      =					gpu_peak_mem_bnb + gpu_alloc_mem_bnb
      lowerCAmelCase__ :  Any							      =					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__ :  Any							      =					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 							UpperCAmelCase_	(    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  ,)    ->		List[str]:
      lowerCAmelCase__ :  List[str]							      =					self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
      lowerCAmelCase__ :  List[str]							      =					self.get_auto_remove_tmp_dir()
      lowerCAmelCase__ :  Optional[int]							      =					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__ :  Union[str, Any]							      =					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__ :  Optional[int]							      =					"""
            --do_predict
        """.split()

      lowerCAmelCase__ :  Dict							      =					[]
      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__ :  Dict							      =					get_gpu_count()
          lowerCAmelCase__ :  Optional[Any]							      =					get_torch_dist_unique_port()
          lowerCAmelCase__ :  Optional[Any]							      =					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__ :  List[str]							      =					[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__ :  List[Any]							      =					["""run_translation.py"""] + args
          with patch.object(__UpperCAmelCase  ,"""argv"""  ,__UpperCAmelCase      ):
              main()

      return output_dir







'''simple docstring'''





import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


_lowerCAmelCase      						=	datasets.logging.get_logger(__name__)


_lowerCAmelCase      						=	'''\
@inproceedings{bleurt,
  title={BLEURT: Learning Robust Metrics for Text Generation},
  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
  booktitle={ACL},
  year={2020},
  url={https://arxiv.org/abs/2004.04696}
}
'''

_lowerCAmelCase      						=	'''\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).

See the project\'s README at https://github.com/google-research/bleurt#readme for more information.
'''

_lowerCAmelCase      						=	'''
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    \'scores\': List of scores.
Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bleurt = datasets.load_metric("bleurt")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results["scores"]])
    [1.03, 1.04]
'''

_lowerCAmelCase      						=	{
    '''bleurt-tiny-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip''',
    '''bleurt-tiny-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip''',
    '''bleurt-base-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip''',
    '''bleurt-base-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip''',
    '''bleurt-large-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip''',
    '''bleurt-large-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip''',
    '''BLEURT-20-D3''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip''',
    '''BLEURT-20-D6''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip''',
    '''BLEURT-20-D12''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip''',
    '''BLEURT-20''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip''',
}






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						, _KWARGS_DESCRIPTION     )
class        lowerCAmelCase_(				datasets.Metric     ):
  '''simple docstring'''




  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      return datasets.MetricInfo(
          description=_DESCRIPTION  ,citation=_CITATION  ,homepage="""https://github.com/google-research/bleurt"""  ,inputs_description=_KWARGS_DESCRIPTION  ,features=datasets.Features(
              {
                  """predictions""": datasets.Value("""string"""  ,id="""sequence"""      ),
                  """references""": datasets.Value("""string"""  ,id="""sequence"""      ),
              }      )  ,codebase_urls=["""https://github.com/google-research/bleurt"""]  ,reference_urls=["""https://github.com/google-research/bleurt""", """https://arxiv.org/abs/2004.04696"""]  ,)




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		Tuple:
      # check that config name specifies a valid BLEURT model
      if self.config_name == "default":
          logger.warning(
              """Using default BLEURT-Base checkpoint for sequence maximum length 128. """
              """You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512')."""      )
          lowerCAmelCase__ :  str							      =					"""bleurt-base-128"""

      if self.config_name.lower() in CHECKPOINT_URLS:
          lowerCAmelCase__ :  Union[str, Any]							      =					self.config_name.lower()

      elif self.config_name.upper() in CHECKPOINT_URLS:
          lowerCAmelCase__ :  List[Any]							      =					self.config_name.upper()

      else:
          raise KeyError(
              F"""{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}"""      )

      # download the model checkpoint specified by self.config_name and set up the scorer
      lowerCAmelCase__ :  int							      =					dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]      )
      lowerCAmelCase__ :  Dict							      =					score.BleurtScorer(os.path.join(__UpperCAmelCase  ,__UpperCAmelCase      )      )






  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase      )    ->		Union[str, Any]:
      lowerCAmelCase__ :  Union[str, Any]							      =					self.scorer.score(references=__UpperCAmelCase  ,candidates=__UpperCAmelCase      )
      return {"scores": scores}


