






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


__a:     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 		SCREAMING_SNAKE_CASE__	(  SCREAMING_SNAKE_CASE_     ):

    '''simple docstring'''







    def __init__(      self		:			List[Any]       ,				lowerCamelCase		:			Any = 101							)						->	Union[str, Any]:


           """simple docstring"""
           _UpperCAmelCase							=    length


    def __len__(      self		:			Union[str, Any]							)						->	Union[str, Any]:


           """simple docstring"""
           return self.length





    def __getitem__(      self		:			Union[str, Any]       ,				lowerCamelCase		:			Dict							)						->	Dict:


           """simple docstring"""
           return i



  class 		SCREAMING_SNAKE_CASE__	:

    '''simple docstring'''







    def __call__(      self		:			str       ,				lowerCamelCase		:			int							)						->	List[str]:


           """simple docstring"""
           return {"input_ids": torch.tensor(snake_case__							), "labels": torch.tensor(snake_case__							)}



  class 		SCREAMING_SNAKE_CASE__	(  nn.Module     ):

    '''simple docstring'''







    def __init__(      self		:			Optional[int]							)						->	Optional[int]:


           """simple docstring"""
           super().__init__()
           # Add some (unused) params otherwise DDP will complain.
           _UpperCAmelCase							=    nn.Linear(120       ,				80							)





    def  lowerCamelCase       (      self		:			Optional[int]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			Optional[Any]=None							)						->	int:


           """simple docstring"""

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

  '''simple docstring'''







  @require_torch_neuroncore
  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    f"""--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        """.split()
         _UpperCAmelCase							=    self.get_auto_remove_tmp_dir()
         _UpperCAmelCase							=    f"""--output_dir {output_dir}""".split()
         _UpperCAmelCase							=    ['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 		SCREAMING_SNAKE_CASE__	(  SCREAMING_SNAKE_CASE_     ):

  '''simple docstring'''







  @require_torch_multi_gpu
  def  lowerCamelCase       (      self		:			Optional[int]							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    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()
         _UpperCAmelCase							=    self.get_auto_remove_tmp_dir()
         _UpperCAmelCase							=    f"""--output_dir {output_dir}""".split()
         _UpperCAmelCase							=    ['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

  __a:     Any   					=					HfArgumentParser((TrainingArguments,))
  __a:     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 [101, 40, 7]:
    __a:     Dict   					=					DummyDataset(dataset_length)




    def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Any:
           _UpperCAmelCase							=    list(range(len(_A ) ) )
           _UpperCAmelCase							=    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}

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

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

    __a:     Any   					=					2

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

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





    __a:     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}

