


'''simple docstring'''







from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


lowerCAmelCase     :  List[str]											=    logging.get_logger(__name__)


if is_torch_available():
       import torch
       from torch import nn
       from torch.utils.data import Dataset

       from transformers import Trainer
       class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



            """simple docstring"""






            def __init__(			self   ,						snake_case__ = 101 ):

                   '''simple docstring'''

                   _lowerCAmelCase     :				Optional[int]       =							length







            def __len__(			self ):

                   '''simple docstring'''

                   return self.length







            def __getitem__(			self   ,						snake_case__ ):

                   '''simple docstring'''

                   return i
       class 						UpperCamelCase__       :



            """simple docstring"""






            def __call__(			self   ,						snake_case__ ):

                   '''simple docstring'''

                   return {"input_ids": torch.tensor(snake_case__ ), "labels": torch.tensor(snake_case__ )}
       class 						UpperCamelCase__       (			nn.Module						):



            """simple docstring"""






            def __init__(			self ):

                   '''simple docstring'''

                   super().__init__()
                   # Add some (unused) params otherwise DDP will complain.
                   _lowerCAmelCase     :				Any       =							nn.Linear(120   ,						80 )







            def a						(			self   ,						snake_case__   ,						snake_case__=None ):

                   '''simple docstring'''

                   if labels is not None:
                          return torch.tensor(0.0   ,						device=input_ids.device ), input_ids
                   else:
                          return input_ids
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     @require_torch_neuroncore
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				List[str]       =							F'--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
            _lowerCAmelCase     :				Tuple       =							self.get_auto_remove_tmp_dir()
            _lowerCAmelCase     :				int       =							F'--output_dir {output_dir}'.split()
            _lowerCAmelCase     :				Union[str, Any]       =							['torchrun'] + distributed_args + args
            execute_subprocess_async(snake_case__   ,						env=self.get_env() )
            # successful return here == success - any errors would have caused an error in the sub-call
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     @require_torch_multi_gpu
     def a						(			self ):

            '''simple docstring'''

            _lowerCAmelCase     :				str       =							F'--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '.split()
            _lowerCAmelCase     :				List[Any]       =							self.get_auto_remove_tmp_dir()
            _lowerCAmelCase     :				Dict       =							F'--output_dir {output_dir}'.split()
            _lowerCAmelCase     :				Tuple       =							['torchrun'] + distributed_args + args
            execute_subprocess_async(snake_case__   ,						env=self.get_env() )
            # successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
       # The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
       #
       # PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

       lowerCAmelCase     :  Any											=    HfArgumentParser((TrainingArguments,))
       lowerCAmelCase     :  List[str]											=    parser.parse_args_into_dataclasses()[0]

       logger.warning(
           F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '''
           F'''distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'''
       )

       # Essentially, what we want to verify in the distributed case is that we get all samples back,
       # in the right order. (this is crucial for prediction for instance)
       for dataset_length in [1_01, 40, 7]:
              lowerCAmelCase     :  Dict											=    DummyDataset(dataset_length)






              def       lowercase					(_A       ):







                     """simple docstring"""







                     _lowerCAmelCase     :				List[Any]       =							list(range(len(_A       )       )       )
                     _lowerCAmelCase     :				Tuple       =							p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
                     if not success and training_args.local_rank == 0:
                            logger.warning(
                                'Predictions and/or labels do not match expected results:\n  - predictions: '
                                f'{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}'       )
                     return {"success": success}

              lowerCAmelCase     :  Optional[Any]											=    Trainer(
                  model=DummyModel(),
                  args=training_args,
                  data_collator=DummyDataCollator(),
                  eval_dataset=dataset,
                  compute_metrics=compute_metrics,
              )
              lowerCAmelCase     :  str											=    trainer.evaluate()
              logger.info(metrics)
              if metrics["eval_success"] is not True:
                     logger.error(metrics)
                     exit(1)

              lowerCAmelCase     :  Dict											=    trainer.predict(dataset)
              logger.info(p.metrics)
              if p.metrics["test_success"] is not True:
                     logger.error(p.metrics)
                     exit(1)

              lowerCAmelCase     :  Any											=    2

              lowerCAmelCase     :  Union[str, Any]											=    trainer.evaluate()
              logger.info(metrics)
              if metrics["eval_success"] is not True:
                     logger.error(metrics)
                     exit(1)

              lowerCAmelCase     :  int											=    trainer.predict(dataset)
              logger.info(p.metrics)
              if p.metrics["test_success"] is not True:
                     logger.error(p.metrics)
                     exit(1)
              lowerCAmelCase     :  Any											=    None





'''simple docstring'''







import os

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

import datasets


lowerCAmelCase     :  Optional[int]											=    datasets.logging.get_logger(__name__)


lowerCAmelCase     :  List[str]											=    """\
@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     :  List[Any]											=    """\
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     :  str											=    """
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     :  Optional[Any]											=    {
    """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 						UpperCamelCase__       (			datasets.Metric						):



     """simple docstring"""






     def a						(			self ):

            '''simple docstring'''

            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 a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            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     :				Tuple       =							'bleurt-base-128'

            if self.config_name.lower() in CHECKPOINT_URLS:
                   _lowerCAmelCase     :				int       =							self.config_name.lower()

            elif self.config_name.upper() in CHECKPOINT_URLS:
                   _lowerCAmelCase     :				str       =							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     :				Optional[Any]       =							dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name] )
            _lowerCAmelCase     :				str       =							score.BleurtScorer(os.path.join(snake_case__   ,						snake_case__ ) )







     def a						(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							self.scorer.score(references=snake_case__   ,						candidates=snake_case__ )
            return {"scores": scores}

