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

import datasets


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

__lowerCamelCase							= """\
FrugalScore 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.
"""


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






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



   def        _SCREAMING_SNAKE_CASE     (self	:      List[Any]				)	->			int:


          '''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        _SCREAMING_SNAKE_CASE     (self	:      Union[str, Any]					,		snake_case__	:      int				)	->			List[str]:


          '''simple docstring'''


          if self.config_name == "default":
                 snake_case :     int           =   "moussaKam/frugalscore_tiny_bert-base_bert-score"
          else:
                 snake_case :     Optional[int]           =   self.config_name
          snake_case :     List[str]           =   AutoModelForSequenceClassification.from_pretrained(snake_case__				)
          snake_case :     List[str]           =   AutoTokenizer.from_pretrained(snake_case__				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Dict					,		snake_case__	:      Any					,		snake_case__	:      Optional[int]					,		snake_case__	:      Dict=32					,		snake_case__	:      Tuple=1_28					,		snake_case__	:      List[str]=None					,		)	->			int:


          '''simple docstring'''


          assert len(snake_case__				) == len(
              snake_case__				), "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:
                 snake_case :     str           =   "gpu" if torch.cuda.is_available() else "cpu"
          snake_case :     Union[str, Any]           =   TrainingArguments(
              "trainer"					,		fpaa=(device == "gpu")					,		per_device_eval_batch_size=snake_case__					,		report_to="all"					,		no_cuda=(device == "cpu")					,		log_level="warning"					,		)
          snake_case :     Optional[int]           =   {"sentence1": predictions, "sentence2": references}
          snake_case :     int           =   datasets.Dataset.from_dict(snake_case__				)

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

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







from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest






@custom_tokenizers
class 						UpperCAmelCase	(		A_				):



   def        _SCREAMING_SNAKE_CASE     (self	:      List[str]					,		snake_case__	:      Dict					,		snake_case__	:      Tuple					,		snake_case__	:      Union[str, Any]					,		snake_case__	:      List[str]					,		snake_case__	:      List[Any]				)	->			Any:


          '''simple docstring'''


          return True



   def        _SCREAMING_SNAKE_CASE     (self	:      str				)	->			Dict:


          '''simple docstring'''


          snake_case :     Union[str, Any]           =   CpmTokenizer.from_pretrained("TsinghuaAI/CPM-Generate"				)
          snake_case :     str           =   "Hugging Face大法好，谁用谁知道。"
          snake_case :     Optional[Any]           =   "Hugging Face大法好,谁用谁知道。<unk>"
          snake_case :     int           =   "▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。".split()

          snake_case :     Union[str, Any]           =   tokenizer.tokenize(snake_case__				)
          self.assertListEqual(snake_case__					,		snake_case__				)

          snake_case :     Tuple           =   tokens + [tokenizer.unk_token]

          snake_case :     int           =   [1_37_89, 1_32_83, 14_21, 8, 10, 11_64, 1_36_08, 1_65_28, 63, 8, 9, 4_40, 1_08, 4_40, 1_21, 90, 8, 12, 0]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__				)					,		snake_case__				)

          snake_case :     Dict           =   tokenizer.decode(snake_case__				)
          self.assertEqual(snake_case__					,		snake_case__				)





