

"""simple docstring"""
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


A									=						"""\
@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}
}
"""

A									=						"""\
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.
"""


A									=						"""
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					: str      )	->			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					: Optional[int]      )	->			Any:


         '''simple docstring'''





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




  def 							_UpperCAmelCase      ( self					: Dict       ,       snake_case					: Optional[Any]       ,       snake_case					: int       ,       snake_case					: Tuple=32       ,       snake_case					: str=128       ,       snake_case					: Union[str, Any]=None       ,       )	->			List[Any]:


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

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

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








"""simple docstring"""



def   _lowerCAmelCase		(				UpperCAmelCase__							:       float,    UpperCAmelCase__							:       float,    UpperCAmelCase__							:       float   )		->list[int]:
						if hue < 0 or hue > 3_6_0:
												raise Exception("""hue should be between 0 and 360"""   )

						if saturation < 0 or saturation > 1:
												raise Exception("""saturation should be between 0 and 1"""   )

						if value < 0 or value > 1:
												raise Exception("""value should be between 0 and 1"""   )

						A__    :	int			=	value * saturation
						A__    :	Any			=	hue / 6_0
						A__    :	Any			=	chroma * (1 - abs(hue_section % 2 - 1   ))
						A__    :	Optional[Any]			=	value - chroma

						if hue_section >= 0 and hue_section <= 1:
												A__    :	Dict			=	round(2_5_5 * (chroma + match_value)   )
												A__    :	Optional[Any]			=	round(2_5_5 * (second_largest_component + match_value)   )
												A__    :	Any			=	round(2_5_5 * (match_value)   )
						elif hue_section > 1 and hue_section <= 2:
												A__    :	Optional[int]			=	round(2_5_5 * (second_largest_component + match_value)   )
												A__    :	Union[str, Any]			=	round(2_5_5 * (chroma + match_value)   )
												A__    :	Union[str, Any]			=	round(2_5_5 * (match_value)   )
						elif hue_section > 2 and hue_section <= 3:
												A__    :	Tuple			=	round(2_5_5 * (match_value)   )
												A__    :	List[str]			=	round(2_5_5 * (chroma + match_value)   )
												A__    :	Union[str, Any]			=	round(2_5_5 * (second_largest_component + match_value)   )
						elif hue_section > 3 and hue_section <= 4:
												A__    :	Any			=	round(2_5_5 * (match_value)   )
												A__    :	Dict			=	round(2_5_5 * (second_largest_component + match_value)   )
												A__    :	List[Any]			=	round(2_5_5 * (chroma + match_value)   )
						elif hue_section > 4 and hue_section <= 5:
												A__    :	Any			=	round(2_5_5 * (second_largest_component + match_value)   )
												A__    :	Dict			=	round(2_5_5 * (match_value)   )
												A__    :	Tuple			=	round(2_5_5 * (chroma + match_value)   )
						else:
												A__    :	Tuple			=	round(2_5_5 * (chroma + match_value)   )
												A__    :	Optional[int]			=	round(2_5_5 * (match_value)   )
												A__    :	str			=	round(2_5_5 * (second_largest_component + match_value)   )

						return [red, green, blue]




def   _lowerCAmelCase		(				UpperCAmelCase__							:       int,    UpperCAmelCase__							:       int,    UpperCAmelCase__							:       int   )		->list[float]:
						if red < 0 or red > 2_5_5:
												raise Exception("""red should be between 0 and 255"""   )

						if green < 0 or green > 2_5_5:
												raise Exception("""green should be between 0 and 255"""   )

						if blue < 0 or blue > 2_5_5:
												raise Exception("""blue should be between 0 and 255"""   )

						A__    :	int			=	red / 2_5_5
						A__    :	List[str]			=	green / 2_5_5
						A__    :	Optional[int]			=	blue / 2_5_5
						A__    :	Optional[int]			=	max(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__   )
						A__    :	Tuple			=	value - min(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__   )
						A__    :	Any			=	0 if value == 0 else chroma / value

						if chroma == 0:
												A__    :	Optional[Any]			=	0.0
						elif value == float_red:
												A__    :	Optional[Any]			=	6_0 * (0 + (float_green - float_blue) / chroma)
						elif value == float_green:
												A__    :	Tuple			=	6_0 * (2 + (float_blue - float_red) / chroma)
						else:
												A__    :	int			=	6_0 * (4 + (float_red - float_green) / chroma)

						A__    :	Optional[int]			=	(hue + 3_6_0) % 3_6_0

						return [hue, saturation, value]




def   _lowerCAmelCase		(				UpperCAmelCase__							:       list[float],    UpperCAmelCase__							:       list[float]   )		->bool:
						A__    :	Optional[Any]			=	abs(hsv_a[0] - hsv_a[0]   ) < 0.2
						A__    :	str			=	abs(hsv_a[1] - hsv_a[1]   ) < 0.002
						A__    :	Optional[int]			=	abs(hsv_a[2] - hsv_a[2]   ) < 0.002

						return check_hue and check_saturation and check_value






