


import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class A  (       UpperCamelCase_					):
      UpperCamelCase__							:  List[str]				=(EulerAncestralDiscreteScheduler,)
      UpperCamelCase__							:  List[str]				=10






      def 	lowerCamelCase	( self						:					Any      ,       **lowercase_						:					str			) ->						Tuple:
             """simple docstring"""




             _lowerCamelCase	:		Tuple          ={
                 'num_train_timesteps': 1100,
                 'beta_start': 0.0001,
                 'beta_end': 0.02,
                 'beta_schedule': 'linear',
             }

             config.update(**lowercase_			)
             return config






      def 	lowerCamelCase	( self						:					Dict			) ->						Any:
             """simple docstring"""




             for timesteps in [10, 50, 100, 1000]:
                    self.check_over_configs(num_train_timesteps=lowercase_			)






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




             for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]      ,       [0.0002, 0.002, 0.02]			):
                    self.check_over_configs(beta_start=lowercase_      ,       beta_end=lowercase_			)






      def 	lowerCamelCase	( self						:					Any			) ->						int:
             """simple docstring"""




             for schedule in ["linear", "scaled_linear"]:
                    self.check_over_configs(beta_schedule=lowercase_			)






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




             for prediction_type in ["epsilon", "v_prediction"]:
                    self.check_over_configs(prediction_type=lowercase_			)






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




             _lowerCamelCase	:		Any          =self.scheduler_classes[0]
             _lowerCamelCase	:		int          =self.get_scheduler_config()
             _lowerCamelCase	:		List[str]          =scheduler_class(**lowercase_			)

             scheduler.set_timesteps(self.num_inference_steps			)

             _lowerCamelCase	:		Dict          =torch.manual_seed(0			)

             _lowerCamelCase	:		Tuple          =self.dummy_model()
             _lowerCamelCase	:		Dict          =self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
             _lowerCamelCase	:		Any          =sample.to(lowercase_			)

             for i, t in enumerate(scheduler.timesteps			):
                    _lowerCamelCase	:		str          =scheduler.scale_model_input(lowercase_      ,       lowercase_			)

                    _lowerCamelCase	:		Any          =model(lowercase_      ,       lowercase_			)

                    _lowerCamelCase	:		Tuple          =scheduler.step(lowercase_      ,       lowercase_      ,       lowercase_      ,       generator=lowercase_			)
                    _lowerCamelCase	:		List[str]          =output.prev_sample

             _lowerCamelCase	:		Dict          =torch.sum(torch.abs(lowercase_			)			)
             _lowerCamelCase	:		int          =torch.mean(torch.abs(lowercase_			)			)

             assert abs(result_sum.item() - 152.3192			) < 1E-2
             assert abs(result_mean.item() - 0.1983			) < 1E-3






      def 	lowerCamelCase	( self						:					Dict			) ->						Any:
             """simple docstring"""




             _lowerCamelCase	:		List[str]          =self.scheduler_classes[0]
             _lowerCamelCase	:		int          =self.get_scheduler_config(prediction_type='v_prediction'			)
             _lowerCamelCase	:		Optional[int]          =scheduler_class(**lowercase_			)

             scheduler.set_timesteps(self.num_inference_steps			)

             _lowerCamelCase	:		List[str]          =torch.manual_seed(0			)

             _lowerCamelCase	:		str          =self.dummy_model()
             _lowerCamelCase	:		Union[str, Any]          =self.dummy_sample_deter * scheduler.init_noise_sigma
             _lowerCamelCase	:		Union[str, Any]          =sample.to(lowercase_			)

             for i, t in enumerate(scheduler.timesteps			):
                    _lowerCamelCase	:		Tuple          =scheduler.scale_model_input(lowercase_      ,       lowercase_			)

                    _lowerCamelCase	:		Dict          =model(lowercase_      ,       lowercase_			)

                    _lowerCamelCase	:		Optional[int]          =scheduler.step(lowercase_      ,       lowercase_      ,       lowercase_      ,       generator=lowercase_			)
                    _lowerCamelCase	:		List[str]          =output.prev_sample

             _lowerCamelCase	:		Dict          =torch.sum(torch.abs(lowercase_			)			)
             _lowerCamelCase	:		Optional[Any]          =torch.mean(torch.abs(lowercase_			)			)

             assert abs(result_sum.item() - 108.4439			) < 1E-2
             assert abs(result_mean.item() - 0.1412			) < 1E-3






      def 	lowerCamelCase	( self						:					Dict			) ->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase	:		List[Any]          =self.scheduler_classes[0]
             _lowerCamelCase	:		Tuple          =self.get_scheduler_config()
             _lowerCamelCase	:		str          =scheduler_class(**lowercase_			)

             scheduler.set_timesteps(self.num_inference_steps      ,       device=lowercase_			)
             _lowerCamelCase	:		Optional[int]          =torch.manual_seed(0			)

             _lowerCamelCase	:		Any          =self.dummy_model()
             _lowerCamelCase	:		Optional[int]          =self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
             _lowerCamelCase	:		List[str]          =sample.to(lowercase_			)

             for t in scheduler.timesteps:
                    _lowerCamelCase	:		Optional[Any]          =scheduler.scale_model_input(lowercase_      ,       lowercase_			)

                    _lowerCamelCase	:		List[str]          =model(lowercase_      ,       lowercase_			)

                    _lowerCamelCase	:		Dict          =scheduler.step(lowercase_      ,       lowercase_      ,       lowercase_      ,       generator=lowercase_			)
                    _lowerCamelCase	:		List[Any]          =output.prev_sample

             _lowerCamelCase	:		int          =torch.sum(torch.abs(lowercase_			)			)
             _lowerCamelCase	:		Any          =torch.mean(torch.abs(lowercase_			)			)

             assert abs(result_sum.item() - 152.3192			) < 1E-2
             assert abs(result_mean.item() - 0.1983			) < 1E-3






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			)			)}


