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

import datasets


__snake_case         =     '''\
@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}
}
'''

__snake_case         =     '''\
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.
'''


__snake_case         =     '''
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       __snake_case (  datasets.Metric ):
 def 						UpperCAmelCase__							(	self    )						->				List[str]:
       '''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		, snake_case__    )						->				str:
       '''simple docstring'''





       if self.config_name == "default":
             UpperCAmelCase     :			int     	='''moussaKam/frugalscore_tiny_bert-base_bert-score'''
       else:
             UpperCAmelCase     :			Any     	=self.config_name
       UpperCAmelCase     :			Any     	=AutoModelForSequenceClassification.from_pretrained(snake_case__    )
       UpperCAmelCase     :			int     	=AutoTokenizer.from_pretrained(snake_case__    )






 def 						UpperCAmelCase__							(	self		, snake_case__		, snake_case__		, snake_case__=32		, snake_case__=128		, snake_case__=None		, )						->				List[Any]:
       '''simple docstring'''





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

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

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

import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest







class       __snake_case (  lowerCamelCase__ ):
 __lowerCamelCase     : Dict 	=   (VQDiffusionScheduler,)
 def 						UpperCAmelCase__							(	self		, **snake_case__    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	={
           '''num_vec_classes''': 4097,
           '''num_train_timesteps''': 100,
       }

       config.update(**snake_case__    )
       return config
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				List[Any]:
       '''simple docstring'''





       UpperCAmelCase     :			List[str]     	=4
       UpperCAmelCase     :			Optional[int]     	=8
       UpperCAmelCase     :			Optional[Any]     	=8

       UpperCAmelCase     :			str     	=torch.randint(0		, snake_case__		, (batch_size, height * width)    )

       return sample
 @property
 def 						UpperCAmelCase__							(	self    )						->				List[Any]:
       '''simple docstring'''





       assert False
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				Tuple:
       '''simple docstring'''





       def model(snake_case__		, snake_case__		, *snake_case__    ):
             UpperCAmelCase	,    UpperCAmelCase     :			Union[str, Any]     	=sample.shape
             UpperCAmelCase     :			Tuple     	=torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)    )
             UpperCAmelCase     :			List[str]     	=F.log_softmax(logits.double()		, dim=1    ).float()
             return return_value

       return model
 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





       for timesteps in [2, 5, 100, 1000]:
             self.check_over_configs(num_train_timesteps=snake_case__    )
 def 						UpperCAmelCase__							(	self    )						->				Optional[Any]:
       '''simple docstring'''





       for num_vec_classes in [5, 100, 1000, 4000]:
             self.check_over_configs(num_vec_classes=snake_case__    )
 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





       for t in [0, 50, 99]:
             self.check_over_forward(time_step=snake_case__    )






 def 						UpperCAmelCase__							(	self    )						->				int:
       '''simple docstring'''





       pass
