


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

import datasets


a				:							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'

a				:							Union[str, 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'


a				:							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     ):

       def 				UpperCAmelCase				(			self					):






        '''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	,						a__					):






        '''simple docstring'''






        if self.config_name == "default":
         snake_case   : Tuple	  =      '''moussaKam/frugalscore_tiny_bert-base_bert-score'''
        else:
         snake_case   : Tuple	  =      self.config_name
        snake_case   : Any	  =      AutoModelForSequenceClassification.from_pretrained(a__					)
        snake_case   : Tuple	  =      AutoTokenizer.from_pretrained(a__					)



       def 				UpperCAmelCase				(			self	,						a__	,						a__	,						a__=32	,						a__=1_28	,						a__=None	,						):






        '''simple docstring'''






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

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

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










from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
  import torch

a				:							Union[str, Any]					=			logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
  from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):






  def    _UpperCAmelCase	(   cls       )		->str:


   '''simple docstring'''
   return cls






def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase = None ,     __lowerCamelCase = None ,     __lowerCamelCase = None ,     __lowerCamelCase = None ,     )		->Union[str, Any]:


 '''simple docstring'''
 snake_case   : Union[str, Any]	  =      device
 snake_case   : Any	  =      shape[0]

 snake_case   : Any	  =      layout or torch.strided
 snake_case   : Union[str, Any]	  =      device or torch.device('''cpu'''       )

 if generator is not None:
  snake_case   : Any	  =      generator.device.type if not isinstance(__lowerCamelCase ,     __lowerCamelCase       ) else generator[0].device.type
  if gen_device_type != device.type and gen_device_type == "cpu":
   snake_case   : Optional[int]	  =      '''cpu'''
   if device != "mps":
    logger.info(
        f'''The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'''
        f''' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'''
        f''' slighly speed up this function by passing a generator that was created on the {device} device.'''       )
  elif gen_device_type != device.type and gen_device_type == "cuda":
   raise ValueError(f'''Cannot generate a {device} tensor from a generator of type {gen_device_type}.'''       )

 if isinstance(__lowerCamelCase ,     __lowerCamelCase       ):
  snake_case   : Tuple	  =      (1,) + shape[1:]
  snake_case   : List[Any]	  =      [
      torch.randn(__lowerCamelCase ,     generator=generator[i] ,     device=__lowerCamelCase ,     dtype=__lowerCamelCase ,     layout=__lowerCamelCase       )
      for i in range(__lowerCamelCase       )
  ]
  snake_case   : int	  =      torch.cat(__lowerCamelCase ,     dim=0       ).to(__lowerCamelCase       )
 else:
  snake_case   : Union[str, Any]	  =      torch.randn(__lowerCamelCase ,     generator=__lowerCamelCase ,     device=__lowerCamelCase ,     dtype=__lowerCamelCase ,     layout=__lowerCamelCase       ).to(__lowerCamelCase       )

 return latents






def    _UpperCAmelCase	(   __lowerCamelCase       )		->Tuple:


 '''simple docstring'''
 if is_torch_version('''<''' ,     '''2.0.0'''       ) or not hasattr(__lowerCamelCase ,     '''_dynamo'''       ):
  return False
 return isinstance(__lowerCamelCase ,     torch._dynamo.eval_frame.OptimizedModule       )






