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

import datasets


a_					      = '\\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_					      = '\\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_					      = '\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 	_lowercase		(      datasets.Metric ):

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]    )     ->		Tuple:

  """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 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]     ,   snake_case			:							Optional[int]    )     ->		int:

  """simple docstring"""





  if self.config_name == "default":
   UpperCamelCase_      :		List[Any]				=      'moussaKam/frugalscore_tiny_bert-base_bert-score'
  else:
   UpperCamelCase_      :		Optional[int]				=      self.config_name
  UpperCamelCase_      :		List[Any]				=      AutoModelForSequenceClassification.from_pretrained(snake_case    )
  UpperCamelCase_      :		int				=      AutoTokenizer.from_pretrained(snake_case    )


 def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							Dict     ,   snake_case			:							str     ,   snake_case			:							List[Any]=3_2     ,   snake_case			:							List[Any]=1_2_8     ,   snake_case			:							Dict=None     ,   )     ->		Optional[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_      :		Optional[int]				=      'gpu' if torch.cuda.is_available() else 'cpu'
  UpperCamelCase_      :		List[Any]				=      TrainingArguments(
      'trainer'     ,   fpaa=(device == 'gpu')     ,   per_device_eval_batch_size=snake_case     ,   report_to='all'     ,   no_cuda=(device == 'cpu')     ,   log_level='warning'     ,   )
  UpperCamelCase_      :		Optional[Any]				=      {'sentence1': predictions, 'sentence2': references}
  UpperCamelCase_      :		int				=      datasets.Dataset.from_dict(snake_case    )

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

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




import unittest

from cer import CER


a_					      = CER()





class 	_lowercase		(      unittest.TestCase ):

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		List[Any]				=      ['White House']
  UpperCamelCase_      :		Optional[Any]				=      ['white house']
  # S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
  UpperCamelCase_      :		int				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.1818181818    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]    )     ->		List[Any]:

  """simple docstring"""





  UpperCamelCase_      :		str				=      ['were wolf']
  UpperCamelCase_      :		str				=      ['werewolf']
  # S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
  UpperCamelCase_      :		List[str]				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.1111111    ) < 1e-6    )

  UpperCamelCase_      :		Tuple				=      ['werewolf']
  UpperCamelCase_      :		Union[str, Any]				=      ['weae     wolf']
  # S = 1, D = 1, I = 0, N = 8, CER = 0.25
  UpperCamelCase_      :		Optional[int]				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.25    ) < 1e-6    )

  # consecutive whitespaces case 1
  UpperCamelCase_      :		Optional[int]				=      ['were wolf']
  UpperCamelCase_      :		Dict				=      ['were               wolf']
  # S = 0, D = 0, I = 0, N = 9, CER = 0
  UpperCamelCase_      :		Optional[int]				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.0    ) < 1e-6    )

  # consecutive whitespaces case 2
  UpperCamelCase_      :		Optional[Any]				=      ['were   wolf']
  UpperCamelCase_      :		int				=      ['were               wolf']
  # S = 0, D = 0, I = 0, N = 9, CER = 0
  UpperCamelCase_      :		str				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.0    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str    )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		Tuple				=      ['werewolf']
  UpperCamelCase_      :		int				=      ['weaewolf']
  # S = 1, D = 0, I = 0, N = 8, CER = 0.125
  UpperCamelCase_      :		Optional[int]				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.125    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		List[str]				=      ['werewolf']
  UpperCamelCase_      :		Dict				=      ['wereawolf']
  # S = 0, D = 1, I = 0, N = 8, CER = 0.125
  UpperCamelCase_      :		str				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.125    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any    )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		List[str]				=      ['werewolf']
  UpperCamelCase_      :		Any				=      ['wereolf']
  # S = 0, D = 0, I = 1, N = 8, CER = 0.125
  UpperCamelCase_      :		Optional[Any]				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.125    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		Any				=      ['werewolf']
  UpperCamelCase_      :		str				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertEqual(snake_case     ,   0.0    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		Union[str, Any]				=      ['werewolf', 'I am your father']
  UpperCamelCase_      :		Optional[int]				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertEqual(snake_case     ,   0.0    )

  UpperCamelCase_      :		Any				=      ['werewolf', 'I am your father', 'doge']
  UpperCamelCase_      :		List[str]				=      ['werxwolf', 'I       am your father', 'doge']
  # S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
  UpperCamelCase_      :		Dict				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.03571428    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]    )     ->		Dict:

  """simple docstring"""





  UpperCamelCase_      :		Any				=      ['My hovercraft', 'is full of eels']
  UpperCamelCase_      :		Union[str, Any]				=      ['My hovercraft is full', ' of eels']
  # S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
  # whitespace at the front of " of eels" will be strip during preporcessing
  # so need to insert 2 whitespaces
  UpperCamelCase_      :		Optional[int]				=      cer.compute(predictions=snake_case     ,   references=snake_case     ,   concatenate_texts=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.071428    ) < 1e-6    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		Dict				=      ['我能吞下玻璃而不伤身体']
  UpperCamelCase_      :		int				=      [' 能吞虾玻璃而 不霜身体啦']
  # S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
  UpperCamelCase_      :		Dict				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.4545454545    ) < 1e-6    )

  UpperCamelCase_      :		Union[str, Any]				=      ['我能吞下玻璃', '而不伤身体']
  UpperCamelCase_      :		int				=      ['我    能 吞 下 玻 璃', '而不伤身体']
  # S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
  UpperCamelCase_      :		Any				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertTrue(abs(char_error_rate - 0.454545454545    ) < 1e-6    )

  UpperCamelCase_      :		Any				=      ['我能吞下玻璃而不伤身体']
  UpperCamelCase_      :		int				=      cer.compute(predictions=snake_case     ,   references=snake_case    )
  self.assertFalse(snake_case     ,   0.0    )


 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]    )     ->		int:

  """simple docstring"""





  UpperCamelCase_      :		str				=      ['']
  UpperCamelCase_      :		Optional[Any]				=      ['Hypothesis']
  with self.assertRaises(snake_case    ):
   cer.compute(predictions=snake_case     ,   references=snake_case    )


if __name__ == "__main__":
   unittest.main()



