





"""simple docstring"""
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


lowerCamelCase_          =	"\\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"

lowerCamelCase_          =	"\\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"


lowerCamelCase_          =	"\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       _SCREAMING_SNAKE_CASE(     datasets.Metric						):




       def 					_UpperCamelCase  (		self							)    ->   Optional[int]:





             """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 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Dict:





             """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     :int	       =						AutoModelForSequenceClassification.from_pretrained(lowercase_							)
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						AutoTokenizer.from_pretrained(lowercase_							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=32	,SCREAMING_SNAKE_CASE__=1_28	,SCREAMING_SNAKE_CASE__=None	,)    ->   Union[str, Any]:





             """simple docstring"""






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

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

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










"""simple docstring"""
# Lint as: python3

import hashlib
from typing import Union



def   __lowerCamelCase     (							a_  :	Union[str, int, bytes]							)    ->   bytes:
      if isinstance(a_				,     a_							):
            # Data already in bytes, returns as it as
            return hash_data
      elif isinstance(a_				,     a_							):
            # We keep the data as it as for it ot be later encoded to UTF-8
            # However replace `\\` with `/` for Windows compatibility
            __SCREAMING_SNAKE_CASE     :str	       =						hash_data.replace('''\\'''				,     '''/'''							)
      elif isinstance(a_				,     a_							):
            __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						str(a_							)
      else:
            # If data is not of the required type, raise error
            raise InvalidKeyError(a_							)

      return hash_data.encode('''utf-8'''							)






class       _SCREAMING_SNAKE_CASE(     A						):




       def __init__(		self	,SCREAMING_SNAKE_CASE__							)    ->   Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Tuple	       =						'''\nFAILURE TO GENERATE DATASET: Invalid key type detected'''
             __SCREAMING_SNAKE_CASE     :int	       =						f'''\nFound Key {hash_data} of type {type(SCREAMING_SNAKE_CASE__							)}'''
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						'''\nKeys should be either str, int or bytes type'''
             super().__init__(f'''{self.prefix}{self.err_msg}{self.suffix}'''							)






class       _SCREAMING_SNAKE_CASE(     A						):




       def __init__(		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=""							)    ->   List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :str	       =						key
             __SCREAMING_SNAKE_CASE     :str	       =						duplicate_key_indices
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						fix_msg
             __SCREAMING_SNAKE_CASE     :int	       =						'''Found multiple examples generated with the same key'''
             if len(SCREAMING_SNAKE_CASE__							) <= 20:
                   __SCREAMING_SNAKE_CASE     :Dict	       =						f'''\nThe examples at index {", ".join(SCREAMING_SNAKE_CASE__							)} have the key {key}'''
             else:
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						f'''\nThe examples at index {", ".join(duplicate_key_indices[:20]							)}... ({len(SCREAMING_SNAKE_CASE__							) - 20} more) have the key {key}'''
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						'''\n''' + fix_msg if fix_msg else ''''''
             super().__init__(f'''{self.prefix}{self.err_msg}{self.suffix}'''							)






class       _SCREAMING_SNAKE_CASE:




       def __init__(		self	,SCREAMING_SNAKE_CASE__							)    ->   Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Dict	       =						hashlib.mda(_as_bytes(SCREAMING_SNAKE_CASE__							)							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Tuple	       =						self._split_mda.copy()
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						_as_bytes(SCREAMING_SNAKE_CASE__							)
             mda.update(SCREAMING_SNAKE_CASE__							)
             # Convert to integer with hexadecimal conversion
             return int(mda.hexdigest()	,16							)