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

import datasets


UpperCAmelCase__	:  Optional[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[str]      =   '\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 UpperCAmelCase     (		datasets.Metric     ):





			'''simple docstring'''


			def 				__magic_name__       (      self  :							Dict  ):

						"""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  :							List[Any]    ,    lowerCAmelCase_  :							int  ):

						"""simple docstring"""
						if self.config_name == "default":
									_A:						Dict	    = '''moussaKam/frugalscore_tiny_bert-base_bert-score'''
						else:
									_A:						str	    = self.config_name
						_A:						Any	    = AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_  )
						_A:						Tuple	    = AutoTokenizer.from_pretrained(lowerCAmelCase_  )


			def 				__magic_name__       (      self  :							Dict    ,    lowerCAmelCase_  :							Dict    ,    lowerCAmelCase_  :							str    ,    lowerCAmelCase_  :							Any=3_2    ,    lowerCAmelCase_  :							str=1_2_8    ,    lowerCAmelCase_  :							str=None    ,    ):

						"""simple docstring"""
						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:
									_A:						Union[str, Any]	    = '''gpu''' if torch.cuda.is_available() else '''cpu'''
						_A:						Optional[int]	    = TrainingArguments(
						    '''trainer'''    ,    fpaa=(device == '''gpu''')    ,    per_device_eval_batch_size=lowerCAmelCase_    ,    report_to='''all'''    ,    no_cuda=(device == '''cpu''')    ,    log_level='''warning'''    ,    )
						_A:						Optional[int]	    = {'''sentence1''': predictions, '''sentence2''': references}
						_A:						Dict	    = datasets.Dataset.from_dict(lowerCAmelCase_  )

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

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





UpperCAmelCase__	:  str      =   1000






def 						lowerCamelCase__							(      a   )   ->     float:
   return x * x






def 						lowerCamelCase__							(      a	,	a	,	a	,	a = 4   )   ->     float:
   assert callable(
       a   ), f"""the function(object) passed should be callable your input : {function}"""
   assert isinstance(a	,	(float, int)   ), f"""a should be float or integer your input : {a}"""
   assert isinstance(function(a   )	,	(float, int)   ), (
       "the function should return integer or float return type of your function, "
       f"""{type(a   )}"""
   )
   assert isinstance(a	,	(float, int)   ), f"""b should be float or integer your input : {b}"""
   assert (
       isinstance(a	,	a   ) and precision > 0
   ), f"""precision should be positive integer your input : {precision}"""

   # just applying the formula of simpson for approximate integration written in
   # mentioned article in first comment of this file and above this function

   _A:						int	    = (b - a) / N_STEPS
   _A:						Any	    = function(a   ) + function(a   )

   for i in range(1	,	a   ):
      _A:						List[Any]	    = a + h * i
      result += function(a   ) * (4 if i % 2 else 2)

   result *= h / 3
   return round(a	,	a   )


if __name__ == "__main__":
   import doctest

   doctest.testmod()



