
"""simple docstring"""




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

import datasets


UpperCAmelCase__				:   Any   							=			'\\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'

UpperCAmelCase__				:   List[Any]   							=			'\\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'


UpperCAmelCase__				:   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      lowerCAmelCase_						(datasets.Metric  ):


				"""simple docstring"""



				def      __magic_name__   (self   ) ->		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      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__   ) ->		str:



											"""simple docstring"""




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



				def      __magic_name__   (self       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__       ,	SCREAMING_SNAKE_CASE__=32       ,	SCREAMING_SNAKE_CASE__=1_28       ,	SCREAMING_SNAKE_CASE__=None       ,	) ->		List[Any]:



											"""simple docstring"""




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

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

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






"""simple docstring"""




import re

def lowercase_  (       _snake_case						):
							def docstring_decorator(_snake_case						):
														SCREAMING_SNAKE_CASE__						:	Any									=       fn.__doc__
														SCREAMING_SNAKE_CASE__						:	Dict									=       func_doc.split("""\n"""						)
														SCREAMING_SNAKE_CASE__						:	Any									=       0
														while i < len(_snake_case						) and re.search(R"""^\s*Examples?:\s*$"""       ,lines[i]						) is None:
																					i += 1
														if i < len(_snake_case						):
																					SCREAMING_SNAKE_CASE__						:	List[str]									=       example_docstring
																					SCREAMING_SNAKE_CASE__						:	List[Any]									=       """\n""".join(_snake_case						)
														else:
																					raise ValueError(
																					    f'''The function {fn} should have an empty \'Examples:\' in its docstring as placeholder, '''
																					    f'''current docstring is:\n{func_doc}'''						)
														SCREAMING_SNAKE_CASE__						:	int									=       func_doc
														return fn

							return docstring_decorator




