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

import datasets


__SCREAMING_SNAKE_CASE     :				int             =       '\\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'

__SCREAMING_SNAKE_CASE     :				Tuple             =       '\\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'


__SCREAMING_SNAKE_CASE     :				List[Any]             =       '\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 						__magic_name__				(  datasets.Metric      ):





		def  _A							(							self      :		Dict			):
							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  _A							(							self      :		List[str]  ,					lowerCamelCase__      :		Union[str, Any]			):
							if self.config_name == "default":
												lowerCAmelCase						:   Any							=       '''moussaKam/frugalscore_tiny_bert-base_bert-score'''
							else:
												lowerCAmelCase						:   Any							=       self.config_name
							lowerCAmelCase						:   Optional[Any]							=       AutoModelForSequenceClassification.from_pretrained(lowerCamelCase__			)
							lowerCAmelCase						:   Tuple							=       AutoTokenizer.from_pretrained(lowerCamelCase__			)





		def  _A							(							self      :		List[str]  ,					lowerCamelCase__      :		int  ,					lowerCamelCase__      :		int  ,					lowerCamelCase__      :		Tuple=3_2  ,					lowerCamelCase__      :		List[Any]=1_2_8  ,					lowerCamelCase__      :		str=None  ,					):
							assert len(lowerCamelCase__			) == len(
							    lowerCamelCase__			), "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						:   str							=       '''gpu''' if torch.cuda.is_available() else '''cpu'''
							lowerCAmelCase						:   str							=       TrainingArguments(
							    '''trainer'''  ,					fpaa=(device == '''gpu''')  ,					per_device_eval_batch_size=lowerCamelCase__  ,					report_to='''all'''  ,					no_cuda=(device == '''cpu''')  ,					log_level='''warning'''  ,					)
							lowerCAmelCase						:   str							=       {'''sentence1''': predictions, '''sentence2''': references}
							lowerCAmelCase						:   str							=       datasets.Dataset.from_dict(lowerCamelCase__			)

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

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


def       UpperCAmelCase__							(    __magic_name__  :       int = 99_80_01					):


					'''simple docstring'''

					lowerCAmelCase						:   Tuple							=       0
					for i in range(9_99							,					99							,					-1					):  # 3 digit numbers range from 999 down to 100
										for j in range(9_99							,					99							,					-1					):
															lowerCAmelCase						:   Any							=       str(i * j					)
															if product_string == product_string[::-1] and i * j < n:
																				lowerCAmelCase						:   Optional[int]							=       max(__magic_name__							,					i * j					)
					return answer


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