






from typing import Dict, List, Optional, Tuple

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


A_			:Tuple	    =		logging.get_logger(__name__)




class 							__A	(     _lowercase					):





				"""simple docstring"""







				UpperCamelCase__					: List[str]     					=["""input_ids""", """attention_mask"""]


				def __init__(					self				,    lowerCamelCase__="[PAD]"				,    lowerCamelCase__="[BOS]"				,    lowerCamelCase__="[EOS]"				,    lowerCamelCase__="[MASK]"				,    lowerCamelCase__="[CLS]"				,    lowerCamelCase__="[SEP]"				,    lowerCamelCase__=2048				,    **lowerCamelCase__				,    ):




									"""simple docstring"""

									__UpperCamelCase :   Dict									=AddedToken(__a				,    lstrip=__a				,    rstrip=__a       ) if isinstance(__a				,    __a       ) else pad_token
									__UpperCamelCase :   str									=AddedToken(__a				,    lstrip=__a				,    rstrip=__a       ) if isinstance(__a				,    __a       ) else bos_token
									__UpperCamelCase :   List[str]									=AddedToken(__a				,    lstrip=__a				,    rstrip=__a       ) if isinstance(__a				,    __a       ) else eos_token
									__UpperCamelCase :   Optional[Any]									=AddedToken(__a				,    lstrip=__a				,    rstrip=__a       ) if isinstance(__a				,    __a       ) else mask_token
									__UpperCamelCase :   Any									=AddedToken(__a				,    lstrip=__a				,    rstrip=__a       ) if isinstance(__a				,    __a       ) else cls_token
									__UpperCamelCase :   str									=AddedToken(__a				,    lstrip=__a				,    rstrip=__a       ) if isinstance(__a				,    __a       ) else sep_token

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

									__UpperCamelCase :   int									=2**8  # utf is 8 bits

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


				def  __lowercase				(					self       ):




									"""simple docstring"""

									__UpperCamelCase :   Any									=self.special_tokens_encoder.copy()
									vocab.update(self.added_tokens_encoder       )
									for i in range(self._utf_vocab_size       ):
														__UpperCamelCase :   Tuple									=chr(__a       )
														__UpperCamelCase :   Union[str, Any]									=i + len(self.special_tokens_encoder       )
									return vocab


				@property
				def  __lowercase				(					self       ):




									"""simple docstring"""

									return self._utf_vocab_size + self._num_special_tokens


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__ = None				,    lowerCamelCase__ = False       ):




									"""simple docstring"""

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

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


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__ = None       ):




									"""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  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

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


				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

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


				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

									if index in self.special_tokens_decoder:
														__UpperCamelCase :   Union[str, Any]									=self.special_tokens_decoder[index]
									elif index in self.added_tokens_decoder:
														__UpperCamelCase :   Optional[Any]									=self.added_tokens_decoder[index]
									else:
														__UpperCamelCase :   Dict									=chr(index - self._num_special_tokens       )
									return token


				def  __lowercase				(					self				,    lowerCamelCase__       ):




									"""simple docstring"""

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







				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__ = None       ):




									"""simple docstring"""

									return ()








"""simple docstring"""





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

import datasets


lowerCAmelCase__        =      '''\
@article{eddine2021frugalscore,
  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},
  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},
  journal={arXiv preprint arXiv:2110.08559},
  year={2021}
}
'''

lowerCAmelCase__        =      '''\
FrugalScore 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.
'''


lowerCAmelCase__        =      '''
Calculates how good are predictions given some references, using certain scores.
Args:
    predictions (list of str): list of predictions to score. Each predictions
        should be a string.
    references (list of str): list of reference for each prediction. Each
        reference should be a string.
    batch_size (int): the batch size for predictions.
    max_length (int): maximum sequence length.
    device (str): either gpu or cpu
Returns:
    scores (list of int): list of scores.
Examples:
    >>> frugalscore = datasets.load_metric("frugalscore")
    >>> results = frugalscore.compute(predictions=[\'hello there\', \'huggingface\'], references=[\'hello world\', \'hugging face\'])
    >>> print([round(s, 3) for s in results["scores"]])
    [0.631, 0.645]
'''





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,      _KWARGS_DESCRIPTION			)
class 							_lowerCamelCase   (							datasets.Metric			):


						def      snake_case_ (self		)			-> Tuple:
								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      snake_case_ (self ,				__a		)			-> Optional[int]:
								if self.config_name == "default":
										UpperCamelCase =						"moussaKam/frugalscore_tiny_bert-base_bert-score"
								else:
										UpperCamelCase =						self.config_name
								UpperCamelCase =						AutoModelForSequenceClassification.from_pretrained(__a		)
								UpperCamelCase =						AutoTokenizer.from_pretrained(__a		)




						def      snake_case_ (self ,				__a ,				__a ,				__a=32 ,				__a=1_28 ,				__a=None ,				)			-> Optional[int]:
								assert len(__a		) == len(
								    __a		), "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:
										UpperCamelCase =						"gpu" if torch.cuda.is_available() else "cpu"
								UpperCamelCase =						TrainingArguments(
								    "trainer" ,				fpaa=(device == "gpu") ,				per_device_eval_batch_size=__a ,				report_to="all" ,				no_cuda=(device == "cpu") ,				log_level="warning" ,				)
								UpperCamelCase =						{"sentence1": predictions, "sentence2": references}
								UpperCamelCase =						datasets.Dataset.from_dict(__a		)

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

								UpperCamelCase =						raw_datasets.map(__a ,				batched=__a		)
								tokenized_datasets.remove_columns(["sentence1", "sentence2"]		)
								UpperCamelCase =						Trainer(self.model ,				__a ,				tokenizer=self.tokenizer		)
								UpperCamelCase =						trainer.predict(__a		)
								return {"scores": list(predictions.predictions.squeeze(-1		)		)}






