
'''simple docstring'''





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

import datasets


SCREAMING_SNAKE_CASE    =					"""\
@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}
}
"""

SCREAMING_SNAKE_CASE    =					"""\
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.
"""


SCREAMING_SNAKE_CASE    =					"""
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							):

 '''simple docstring'''







 def        UpperCamelCase     (				self		:     List[Any]   ):



   '''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        UpperCamelCase     (				self		:     Tuple ,							snake_case__		:     str   ):



   '''simple docstring'''




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







 def        UpperCamelCase     (				self		:     str ,							snake_case__		:     Optional[int] ,							snake_case__		:     Union[str, Any] ,							snake_case__		:     Optional[Any]=32 ,							snake_case__		:     Dict=1_28 ,							snake_case__		:     Optional[Any]=None ,							):



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

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

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





'''simple docstring'''





from math import cos, sin







def snake_case_    (				lowercase__		):
  UpperCAmelCase__			:				List[Any]												=       float(lowercase__		)
  return [[scaling_factor * int(x == y		) for x in range(2		)] for y in range(2		)]







def snake_case_    (				lowercase__		):
  UpperCAmelCase__					, UpperCAmelCase__			:				List[Any]												=       cos(lowercase__		), sin(lowercase__		)
  return [[c, -s], [s, c]]







def snake_case_    (				lowercase__		):
  UpperCAmelCase__					, UpperCAmelCase__			:				Dict												=       cos(lowercase__		), sin(lowercase__		)
  UpperCAmelCase__			:				Tuple												=       c * s
  return [[c * c, cs], [cs, s * s]]







def snake_case_    (				lowercase__		):
  UpperCAmelCase__					, UpperCAmelCase__			:				Dict												=       cos(lowercase__		), sin(lowercase__		)
  UpperCAmelCase__			:				Any												=       c * s
  return [[2 * c - 1, 2 * cs], [2 * cs, 2 * s - 1]]


print(F'    {scaling(5) = }')
print(F'  {rotation(4_5) = }')
print(F'{projection(4_5) = }')






print(F'{reflection(4_5) = }')



