


"""simple docstring"""




from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase						 =	logging.get_logger(__name__)




class A  (       UpperCamelCase_					):
      UpperCamelCase__							:  Any				=['input_ids', 'attention_mask']






      def __init__( self						:					Optional[int]      ,       lowercase_						:					List[str]="[PAD]"      ,       lowercase_						:					Tuple="[BOS]"      ,       lowercase_						:					int="[EOS]"      ,       lowercase_						:					int="[MASK]"      ,       lowercase_						:					Optional[int]="[CLS]"      ,       lowercase_						:					Any="[SEP]"      ,       lowercase_						:					Optional[Any]=2048      ,       **lowercase_						:					Any      ,       ) ->						None:
             """simple docstring"""




             _lowerCamelCase	:		int          =AddedToken(lowercase_      ,       lstrip=lowercase_      ,       rstrip=lowercase_			) if isinstance(lowercase_      ,       lowercase_			) else pad_token
             _lowerCamelCase	:		str          =AddedToken(lowercase_      ,       lstrip=lowercase_      ,       rstrip=lowercase_			) if isinstance(lowercase_      ,       lowercase_			) else bos_token
             _lowerCamelCase	:		Optional[int]          =AddedToken(lowercase_      ,       lstrip=lowercase_      ,       rstrip=lowercase_			) if isinstance(lowercase_      ,       lowercase_			) else eos_token
             _lowerCamelCase	:		Optional[Any]          =AddedToken(lowercase_      ,       lstrip=lowercase_      ,       rstrip=lowercase_			) if isinstance(lowercase_      ,       lowercase_			) else mask_token
             _lowerCamelCase	:		Dict          =AddedToken(lowercase_      ,       lstrip=lowercase_      ,       rstrip=lowercase_			) if isinstance(lowercase_      ,       lowercase_			) else cls_token
             _lowerCamelCase	:		Dict          =AddedToken(lowercase_      ,       lstrip=lowercase_      ,       rstrip=lowercase_			) if isinstance(lowercase_      ,       lowercase_			) else sep_token

             super().__init__(
                 pad_token=lowercase_      ,       bos_token=lowercase_      ,       eos_token=lowercase_      ,       mask_token=lowercase_      ,       cls_token=lowercase_      ,       sep_token=lowercase_      ,       model_max_length=lowercase_      ,       **lowercase_      ,       )

             _lowerCamelCase	:		Optional[int]          =2**8  # utf is 8 bits

             # define special tokens dict
             _lowerCamelCase	:		Dict[str, int]          ={
                 self.pad_token: 0,
                 self.bos_token: 1,
                 self.eos_token: 2,
                 self.mask_token: 3,
                 self.cls_token: 4,
                 self.sep_token: 5,
             }
             _lowerCamelCase	:		Any          =len(self.special_tokens_encoder			)
             _lowerCamelCase	:		Dict[int, str]          ={v: k for k, v in self.special_tokens_encoder.items()}






      def 	lowerCamelCase	( self						:					int			) ->						Dict[str, int]:
             """simple docstring"""




             _lowerCamelCase	:		str          =self.special_tokens_encoder.copy()
             vocab.update(self.added_tokens_encoder			)
             for i in range(self._utf_vocab_size			):
                    _lowerCamelCase	:		Dict          =chr(lowercase_			)
                    _lowerCamelCase	:		List[Any]          =i + len(self.special_tokens_encoder			)
             return vocab






      @property
      def 	lowerCamelCase	( self						:					Optional[int]			) ->						Tuple:
             """simple docstring"""




             return self._utf_vocab_size + self._num_special_tokens






      def 	lowerCamelCase	( self						:					List[Any]      ,       lowercase_						:					List[int]      ,       lowercase_						:					Optional[List[int]] = None      ,       lowercase_						:					bool = False			) ->						List[int]:
             """simple docstring"""




             if already_has_special_tokens:
                    return super().get_special_tokens_mask(
                        token_ids_a=lowercase_      ,       token_ids_a=lowercase_      ,       already_has_special_tokens=lowercase_			)

             # normal case: some special tokens
             if token_ids_a is None:
                    return [1] + [0] * len(lowercase_			) + [1]
             return [1] + ([0] * len(lowercase_			)) + [1] + ([0] * len(lowercase_			)) + [1]






      def 	lowerCamelCase	( self						:					Tuple      ,       lowercase_						:					List[int]      ,       lowercase_						:					Optional[List[int]] = None			) ->						List[int]:
             """simple docstring"""




             if token_ids_a is None:
                    return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
             else:
                    return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]






      def 	lowerCamelCase	( self						:					int      ,       lowercase_						:					str			) ->						List[str]:
             """simple docstring"""




             _lowerCamelCase	:		str          =[chr(lowercase_			) for i in text.encode('utf-8'			)]
             return tokens






      def 	lowerCamelCase	( self						:					List[str]      ,       lowercase_						:					List[Any]			) ->						Optional[Any]:
             """simple docstring"""




             if token in self.special_tokens_encoder:
                    _lowerCamelCase	:		Dict          =self.special_tokens_encoder[token]
             elif token in self.added_tokens_encoder:
                    _lowerCamelCase	:		Tuple          =self.added_tokens_encoder[token]
             elif len(lowercase_			) != 1:
                    _lowerCamelCase	:		Optional[Any]          =self.unk_token_id
             else:
                    _lowerCamelCase	:		int          =ord(lowercase_			) + self._num_special_tokens
             return token_id






      def 	lowerCamelCase	( self						:					Union[str, Any]      ,       lowercase_						:					Optional[int]			) ->						Any:
             """simple docstring"""




             if index in self.special_tokens_decoder:
                    _lowerCamelCase	:		List[str]          =self.special_tokens_decoder[index]
             elif index in self.added_tokens_decoder:
                    _lowerCamelCase	:		str          =self.added_tokens_decoder[index]
             else:
                    _lowerCamelCase	:		str          =chr(index - self._num_special_tokens			)
             return token






      def 	lowerCamelCase	( self						:					int      ,       lowercase_						:					List[Any]			) ->						str:
             """simple docstring"""




             _lowerCamelCase	:		Any          =b''
             for token in tokens:
                    if token in self.special_tokens_decoder:
                           _lowerCamelCase	:		Dict          =self.special_tokens_decoder[token].encode('utf-8'			)
                    elif token in self.added_tokens_decoder:
                           _lowerCamelCase	:		List[Any]          =self.special_tokens_decoder[token].encode('utf-8'			)
                    elif token in self.special_tokens_encoder:
                           _lowerCamelCase	:		Tuple          =token.encode('utf-8'			)
                    elif token in self.added_tokens_encoder:
                           _lowerCamelCase	:		Tuple          =token.encode('utf-8'			)
                    else:
                           _lowerCamelCase	:		List[str]          =bytes([ord(lowercase_			)]			)
                    bstring += tok_string
             _lowerCamelCase	:		Union[str, Any]          =bstring.decode('utf-8'      ,       errors='replace'			)
             return string






      def 	lowerCamelCase	( self						:					Dict      ,       lowercase_						:					str      ,       lowercase_						:					Optional[str] = None			) ->						Tuple[str]:
             """simple docstring"""




             return ()






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 A  (       datasets.Metric					):






      def 	lowerCamelCase	( self						:					Tuple			) ->						Optional[Any]:
             """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 	lowerCamelCase	( self						:					Optional[int]      ,       lowercase_						:					Union[str, Any]			) ->						int:
             """simple docstring"""




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






      def 	lowerCamelCase	( self						:					List[Any]      ,       lowercase_						:					List[str]      ,       lowercase_						:					str      ,       lowercase_						:					Optional[int]=32      ,       lowercase_						:					int=128      ,       lowercase_						:					Tuple=None      ,       ) ->						str:
             """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:
                    _lowerCamelCase	:		List[str]          ='gpu' if torch.cuda.is_available() else 'cpu'
             _lowerCamelCase	:		List[str]          =TrainingArguments(
                 'trainer'      ,       fpaa=(device == 'gpu')      ,       per_device_eval_batch_size=lowercase_      ,       report_to='all'      ,       no_cuda=(device == 'cpu')      ,       log_level='warning'      ,       )
             _lowerCamelCase	:		str          ={'sentence1': predictions, 'sentence2': references}
             _lowerCamelCase	:		Tuple          =datasets.Dataset.from_dict(lowercase_			)

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

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


