code: 
  
  import torch
  
  from diffusers import DDIMScheduler
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  
  
  class 		__magic_name__    (							_a							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			List[str]												=     (DDIMScheduler,)
     lowerCamelCase__				:			Union[str, Any]												=     (('eta', 0.0), ('num_inference_steps', 50))
  
  
  
     def 				_UpperCAmelCase				(       self,					**lowercase_ )							->					Optional[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						={
           '''num_train_timesteps''': 1000,
           '''beta_start''': 0.0001,
           '''beta_end''': 0.02,
           '''beta_schedule''': '''linear''',
           '''clip_sample''': True,
       }
  
       config.update(**lowercase_ )
       return config
  
  
  
     def 				_UpperCAmelCase				(       self,					**lowercase_ )							->					List[str]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.scheduler_classes[0]
       a__						=self.get_scheduler_config(**lowercase_ )
       a__						=scheduler_class(**lowercase_ )
  
       a__,   a__						=10, 0.0
  
       a__						=self.dummy_model()
       a__						=self.dummy_sample_deter
  
       scheduler.set_timesteps(lowercase_ )
  
       for t in scheduler.timesteps:
         a__						=model(lowercase_,					lowercase_ )
         a__						=scheduler.step(lowercase_,					lowercase_,					lowercase_,					lowercase_ ).prev_sample
  
       return sample
  
  
  
     def 				_UpperCAmelCase				(       self )							->					List[str]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       for timesteps in [100, 500, 1000]:
         self.check_over_configs(num_train_timesteps=lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       for steps_offset in [0, 1]:
         self.check_over_configs(steps_offset=lowercase_ )
  
       a__						=self.scheduler_classes[0]
       a__						=self.get_scheduler_config(steps_offset=1 )
       a__						=scheduler_class(**lowercase_ )
       scheduler.set_timesteps(5 )
       assert torch.equal(scheduler.timesteps,					torch.LongTensor([801, 601, 401, 201, 1] ) )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					str:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1],					[0.002, 0.02, 0.2, 2] ):
         self.check_over_configs(beta_start=lowercase_,					beta_end=lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					str:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Optional[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Optional[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       for timestep_spacing in ["trailing", "leading"]:
         self.check_over_configs(timestep_spacing=lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Optional[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       for rescale_betas_zero_snr in [True, False]:
         self.check_over_configs(rescale_betas_zero_snr=lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					List[str]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       self.check_over_configs(thresholding=lowercase_ )
       for threshold in [0.5, 1.0, 2.0]:
         for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(
               thresholding=lowercase_,					prediction_type=lowercase_,					sample_max_value=lowercase_,					)
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Tuple:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       for t in [1, 10, 49]:
         self.check_over_forward(time_step=lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Optional[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       for t, num_inference_steps in zip([1, 10, 50],					[10, 50, 500] ):
         self.check_over_forward(time_step=lowercase_,					num_inference_steps=lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Union[str, Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       for t, eta in zip([1, 10, 49],					[0.0, 0.5, 1.0] ):
         self.check_over_forward(time_step=lowercase_,					eta=lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self )							->					List[str]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.scheduler_classes[0]
       a__						=self.get_scheduler_config()
       a__						=scheduler_class(**lowercase_ )
  
       assert torch.sum(torch.abs(scheduler._get_variance(0,					0 ) - 0.0 ) ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(420,					400 ) - 0.14771 ) ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(980,					960 ) - 0.32460 ) ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(0,					0 ) - 0.0 ) ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(487,					486 ) - 0.00979 ) ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(999,					998 ) - 0.02 ) ) < 1E-5
  
  
  
     def 				_UpperCAmelCase				(       self )							->					str:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.full_loop()
  
       a__						=torch.sum(torch.abs(lowercase_ ) )
       a__						=torch.mean(torch.abs(lowercase_ ) )
  
       assert abs(result_sum.item() - 172.0067 ) < 1E-2
       assert abs(result_mean.item() - 0.223967 ) < 1E-3
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Union[str, Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.full_loop(prediction_type='''v_prediction''' )
  
       a__						=torch.sum(torch.abs(lowercase_ ) )
       a__						=torch.mean(torch.abs(lowercase_ ) )
  
       assert abs(result_sum.item() - 52.5302 ) < 1E-2
       assert abs(result_mean.item() - 0.0684 ) < 1E-3
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Dict:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.full_loop(set_alpha_to_one=lowercase_,					beta_start=0.01 )
       a__						=torch.sum(torch.abs(lowercase_ ) )
       a__						=torch.mean(torch.abs(lowercase_ ) )
  
       assert abs(result_sum.item() - 149.8295 ) < 1E-2
       assert abs(result_mean.item() - 0.1951 ) < 1E-3
  
  
  
     def 				_UpperCAmelCase				(       self )							->					List[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.full_loop(set_alpha_to_one=lowercase_,					beta_start=0.01 )
       a__						=torch.sum(torch.abs(lowercase_ ) )
       a__						=torch.mean(torch.abs(lowercase_ ) )
  
       assert abs(result_sum.item() - 149.0784 ) < 1E-2
       assert abs(result_mean.item() - 0.1941 ) < 1E-3
  
style_context: 
  
  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 		__magic_name__    (							datasets.Metric							):
  
     '''simple docstring'''
  
  
  
  
     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,					lowercase_ )							->					Dict:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if self.config_name == "default":
         a__						='''moussaKam/frugalscore_tiny_bert-base_bert-score'''
       else:
         a__						=self.config_name
       a__						=AutoModelForSequenceClassification.from_pretrained(lowercase_ )
       a__						=AutoTokenizer.from_pretrained(lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_=32,					lowercase_=128,					lowercase_=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:
         a__						='''gpu''' if torch.cuda.is_available() else '''cpu'''
       a__						=TrainingArguments(
           '''trainer''',					fpaa=(device == '''gpu'''),					per_device_eval_batch_size=lowercase_,					report_to='''all''',					no_cuda=(device == '''cpu'''),					log_level='''warning''',					)
       a__						={'''sentence1''': predictions, '''sentence2''': references}
       a__						=datasets.Dataset.from_dict(lowercase_ )
  
       def tokenize_function(lowercase_ ):
         return self.tokenizer(
             data['''sentence1'''],					data['''sentence2'''],					max_length=lowercase_,					truncation=lowercase_,					padding=lowercase_ )
  
       a__						=raw_datasets.map(lowercase_,					batched=lowercase_ )
       tokenized_datasets.remove_columns(['''sentence1''', '''sentence2'''] )
       a__						=Trainer(self.model,					lowercase_,					tokenizer=self.tokenizer )
       a__						=trainer.predict(lowercase_ )
       return {"scores": list(predictions.predictions.squeeze(-1 ) )}
  