

"""simple docstring"""


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

import datasets


__A		     =    "\\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"

__A		     =    "\\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"


__A		     =    "\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 					snake_case			(       datasets.Metric						):

				def 							lowercase_  (			self							:       Union[str, Any])->     List[str]:



						'''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 							lowercase_  (			self							:       Union[str, Any]  ,						UpperCamelCase__							:       Tuple)->     Tuple:



						'''simple docstring'''


						if self.config_name == "default":
								__lowerCAmelCase: int					   =    "moussaKam/frugalscore_tiny_bert-base_bert-score"
						else:
								__lowerCAmelCase: Union[str, Any]					   =    self.config_name
						__lowerCAmelCase: Dict					   =    AutoModelForSequenceClassification.from_pretrained(UpperCamelCase__)
						__lowerCAmelCase: Any					   =    AutoTokenizer.from_pretrained(UpperCamelCase__)




				def 							lowercase_  (			self							:       Dict  ,						UpperCamelCase__							:       Union[str, Any]  ,						UpperCamelCase__							:       Tuple  ,						UpperCamelCase__							:       Optional[int]=3_2  ,						UpperCamelCase__							:       str=1_2_8  ,						UpperCamelCase__							:       int=None  ,						)->     str:



						'''simple docstring'''


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

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

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





"""simple docstring"""


def 					a__					(					__SCREAMING_SNAKE_CASE = 1_0_0_0				)			->			int:
		__lowerCAmelCase: int					   =    0
		__lowerCAmelCase: Optional[int]					   =    0
		while 1:
				num += 3
				if num >= n:
						break
				total += num
				num += 2
				if num >= n:
						break
				total += num
				num += 1
				if num >= n:
						break
				total += num
				num += 3
				if num >= n:
						break
				total += num
				num += 1
				if num >= n:
						break
				total += num
				num += 2
				if num >= n:
						break
				total += num
				num += 3
				if num >= n:
						break
				total += num
		return total


if __name__ == "__main__":
			print(F'''{solution() = }''')


