





"""simple docstring"""





def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE = 1_000     ):




		"""simple docstring"""


		if (not isinstance(_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     )) or (digit_position <= 0):
				raise ValueError("Digit position must be a positive integer"     )
		elif (not isinstance(_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     )) or (precision < 0):
				raise ValueError("Precision must be a nonnegative integer"     )

		# compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
		# accurate
		UpperCamelCase =						(
		    4 * _subsum(_SCREAMING_SNAKE_CASE	,						1	,						_SCREAMING_SNAKE_CASE     )
		    - 2 * _subsum(_SCREAMING_SNAKE_CASE	,						4	,						_SCREAMING_SNAKE_CASE     )
		    - _subsum(_SCREAMING_SNAKE_CASE	,						5	,						_SCREAMING_SNAKE_CASE     )
		    - _subsum(_SCREAMING_SNAKE_CASE	,						6	,						_SCREAMING_SNAKE_CASE     )
		)

		# return the first hex digit of the fractional part of the result
		return hex(int((sum_result % 1) * 16     )     )[2:]


def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		UpperCamelCase =						0.0
		for sum_index in range(digit_pos_to_extract + precision     ):
				UpperCamelCase =						8 * sum_index + denominator_addend
				if sum_index < digit_pos_to_extract:
						# if the exponential term is an integer and we mod it by the denominator
						# before dividing, only the integer part of the sum will change;
						# the fractional part will not
						UpperCamelCase =						pow(
						    16	,						digit_pos_to_extract - 1 - sum_index	,						_SCREAMING_SNAKE_CASE     )
				else:
						UpperCamelCase =						pow(16	,						digit_pos_to_extract - 1 - sum_index     )
				total += exponential_term / denominator
		return total


if __name__ == "__main__":
						import doctest

						doctest.testmod()













"""simple docstring"""





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 							_lowerCamelCase   (							datasets.Metric			):


						def      snake_case_ (self		)			-> Tuple:
								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      snake_case_ (self ,				__a		)			-> Optional[int]:
								if self.config_name == "default":
										UpperCamelCase =						"moussaKam/frugalscore_tiny_bert-base_bert-score"
								else:
										UpperCamelCase =						self.config_name
								UpperCamelCase =						AutoModelForSequenceClassification.from_pretrained(__a		)
								UpperCamelCase =						AutoTokenizer.from_pretrained(__a		)




						def      snake_case_ (self ,				__a ,				__a ,				__a=32 ,				__a=1_28 ,				__a=None ,				)			-> Optional[int]:
								assert len(__a		) == len(
								    __a		), "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 =						"gpu" if torch.cuda.is_available() else "cpu"
								UpperCamelCase =						TrainingArguments(
								    "trainer" ,				fpaa=(device == "gpu") ,				per_device_eval_batch_size=__a ,				report_to="all" ,				no_cuda=(device == "cpu") ,				log_level="warning" ,				)
								UpperCamelCase =						{"sentence1": predictions, "sentence2": references}
								UpperCamelCase =						datasets.Dataset.from_dict(__a		)

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

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






