
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


UpperCAmelCase__	:  Optional[Any]      =   '\\n@article{eddine2021frugalscore,\n  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},\n  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},\n  journal={arXiv preprint arXiv:2110.08559},\n  year={2021}\n}\n'

UpperCAmelCase__	:  List[Any]      =   '\\nFrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.\n'


UpperCAmelCase__	:  List[str]      =   '\nCalculates how good are predictions given some references, using certain scores.\nArgs:\n    predictions (list of str): list of predictions to score. Each predictions\n        should be a string.\n    references (list of str): list of reference for each prediction. Each\n        reference should be a string.\n    batch_size (int): the batch size for predictions.\n    max_length (int): maximum sequence length.\n    device (str): either gpu or cpu\nReturns:\n    scores (list of int): list of scores.\nExamples:\n    >>> frugalscore = datasets.load_metric("frugalscore")\n    >>> results = frugalscore.compute(predictions=[\'hello there\', \'huggingface\'], references=[\'hello world\', \'hugging face\'])\n    >>> print([round(s, 3) for s in results["scores"]])\n    [0.631, 0.645]\n'






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,							_KWARGS_DESCRIPTION     )
class UpperCAmelCase     (		datasets.Metric     ):





   '''simple docstring'''


   def 				__magic_name__       (      self  :							Dict  ):

      """simple docstring"""
      return datasets.MetricInfo(
          description=_DESCRIPTION    ,    citation=_CITATION    ,    inputs_description=_KWARGS_DESCRIPTION    ,    features=datasets.Features(
              {
                  '''predictions''': datasets.Value('''string'''  ),
                  '''references''': datasets.Value('''string'''  ),
              }  )    ,    homepage='''https://github.com/moussaKam/FrugalScore'''    ,    )


   def 				__magic_name__       (      self  :							List[Any]    ,    lowerCAmelCase_  :							int  ):

      """simple docstring"""
      if self.config_name == "default":
         _A:						Dict	    = '''moussaKam/frugalscore_tiny_bert-base_bert-score'''
      else:
         _A:						str	    = self.config_name
      _A:						Any	    = AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_  )
      _A:						Tuple	    = AutoTokenizer.from_pretrained(lowerCAmelCase_  )


   def 				__magic_name__       (      self  :							Dict    ,    lowerCAmelCase_  :							Dict    ,    lowerCAmelCase_  :							str    ,    lowerCAmelCase_  :							Any=3_2    ,    lowerCAmelCase_  :							str=1_2_8    ,    lowerCAmelCase_  :							str=None    ,    ):

      """simple docstring"""
      assert len(lowerCAmelCase_  ) == len(
          lowerCAmelCase_  ), "predictions and references should have the same number of sentences."
      if device is not None:
         assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
      else:
         _A:						Union[str, Any]	    = '''gpu''' if torch.cuda.is_available() else '''cpu'''
      _A:						Optional[int]	    = TrainingArguments(
          '''trainer'''    ,    fpaa=(device == '''gpu''')    ,    per_device_eval_batch_size=lowerCAmelCase_    ,    report_to='''all'''    ,    no_cuda=(device == '''cpu''')    ,    log_level='''warning'''    ,    )
      _A:						Optional[int]	    = {'''sentence1''': predictions, '''sentence2''': references}
      _A:						Dict	    = datasets.Dataset.from_dict(lowerCAmelCase_  )

      def tokenize_function(lowerCAmelCase_  :							Any  ):
         return self.tokenizer(
             data['''sentence1''']    ,    data['''sentence2''']    ,    max_length=lowerCAmelCase_    ,    truncation=lowerCAmelCase_    ,    padding=lowerCAmelCase_  )

      _A:						Dict	    = raw_datasets.map(lowerCAmelCase_    ,    batched=lowerCAmelCase_  )
      tokenized_datasets.remove_columns(['''sentence1''', '''sentence2''']  )
      _A:						Optional[int]	    = Trainer(self.model    ,    lowerCAmelCase_    ,    tokenizer=self.tokenizer  )
      _A:						int	    = trainer.predict(lowerCAmelCase_  )
      return {"scores": list(predictions.predictions.squeeze(-1  )  )}





# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


UpperCAmelCase__	:  Union[str, Any]      =   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  :							List[Any]    ,    lowerCAmelCase_  :							int = 1_0_1  ):

         """simple docstring"""
         _A:						str	    = length


      def __len__(      self  :							List[Any]  ):

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


      def __getitem__(      self  :							Any    ,    lowerCAmelCase_  :							Any  ):

         """simple docstring"""
         return i






   class UpperCAmelCase     :





      '''simple docstring'''


      def __call__(      self  :							Any    ,    lowerCAmelCase_  :							Union[str, Any]  ):

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






   class UpperCAmelCase     (		nn.Module     ):





      '''simple docstring'''


      def __init__(      self  :							str  ):

         """simple docstring"""
         super().__init__()
         # Add some (unused) params otherwise DDP will complain.
         _A:						Tuple	    = nn.Linear(1_2_0    ,    8_0  )


      def 				__magic_name__       (      self  :							List[Any]    ,    lowerCAmelCase_  :							List[str]    ,    lowerCAmelCase_  :							List[str]=None  ):

         """simple docstring"""
         if labels is not None:
            return torch.tensor(0.0    ,    device=input_ids.device  ), input_ids
         else:
            return input_ids






def 						lowerCamelCase__							(      )   ->     Optional[int]:
   _A:						Any	    = HfArgumentParser((TrainingArguments,)   )
   sys.argv += ["--output_dir", "./examples"]
   _A:						Any	    = parser.parse_args_into_dataclasses()[0]

   logger.warning(
       f"""Process rank: {training_args.local_rank}, device: {training_args.device}, """
       f"""tpu_num_cores: {training_args.tpu_num_cores}"""	,	)

   # 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 [10_01, 2_56, 15]:
      _A:						Any	    = DummyDataset(a   )

      def compute_metrics(a   ) -> Dict:
         _A:						Dict	    = list(range(len(a   )   )   )
         _A:						List[str]	    = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
         return {"success": success}

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

      _A:						Optional[Any]	    = trainer.predict(a   )
      logger.info(p.metrics   )
      if p.metrics["test_success"] is not True:
         logger.error(p.metrics   )
         exit(1   )

      _A:						str	    = 2

      _A:						Optional[Any]	    = trainer.evaluate()
      logger.info(a   )
      if metrics["eval_success"] is not True:
         logger.error(a   )
         exit(1   )

      _A:						Optional[Any]	    = trainer.predict(a   )
      logger.info(p.metrics   )
      if p.metrics["test_success"] is not True:
         logger.error(p.metrics   )
         exit(1   )

      _A:						str	    = None

   logger.info('''🔥 All distributed tests successful'''   )






def 						lowerCamelCase__							(      a   )   ->     Optional[Any]:
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
   main()



