code: import unittest
  
  from cer import CER
  
  
  _SCREAMING_SNAKE_CASE   						=    CER()
  
  
  
  
  
  
  
  class 					UpperCAmelCase_				(							unittest.TestCase    ):
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[str]   )	-> int:
        a__     		=     ["White House"]
        a__     		=     ["white house"]
        # S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
        a__     		=     cer.compute(predictions=lowerCAmelCase      ,      references=lowerCAmelCase   )
        self.assertTrue(abs(char_error_rate - 0.1_818_181_818   ) < 1e-6   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Optional[int]   )	-> Dict:
        a__     		=     ["were wolf"]
        a__     		=     ["werewolf"]
        # S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
        a__     		=     cer.compute(predictions=lowerCAmelCase      ,      references=lowerCAmelCase   )
        self.assertTrue(abs(char_error_rate - 0.1_111_111   ) < 1e-6   )
  
        a__     		=     ["werewolf"]
        a__     		=     ["weae     wolf"]
        # S = 1, D = 1, I = 0, N = 8, CER = 0.25
        a__     		=     cer.compute(predictions=lowerCAmelCase      ,      references=lowerCAmelCase   )
        self.assertTrue(abs(char_error_rate - 0.25   ) < 1e-6   )
  
        # consecutive whitespaces case 1
        a__     		=     ["were wolf"]
        a__     		=     ["were               wolf"]
        # S = 0, D = 0, I = 0, N = 9, CER = 0
        a__     		=     cer.compute(predictions=lowerCAmelCase      ,      references=lowerCAmelCase   )
        self.assertTrue(abs(char_error_rate - 0.0   ) < 1e-6   )
  
        # consecutive whitespaces case 2
        a__     		=     ["were   wolf"]
        a__     		=     ["were               wolf"]
        # S = 0, D = 0, I = 0, N = 9, CER = 0
        a__     		=     cer.compute(predictions=lowerCAmelCase      ,      references=lowerCAmelCase   )
        self.assertTrue(abs(char_error_rate - 0.0   ) < 1e-6   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Optional[Any]   )	-> Union[str, Any]:
        a__     		=     ["werewolf"]
        a__     		=     ["weaewolf"]
        # S = 1, D = 0, I = 0, N = 8, CER = 0.125
        a__     		=     cer.compute(predictions=lowerCAmelCase      ,      references=lowerCAmelCase   )
        self.assertTrue(abs(char_error_rate - 0.125   ) < 1e-6   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[Any]   )	-> Dict:
        a__     		=     ["werewolf"]
        a__     		=     ["wereawolf"]
        # S = 0, D = 1, I = 0, N = 8, CER = 0.125
        a__     		=     cer.compute(predictions=lowerCAmelCase      ,      references=lowerCAmelCase   )
        self.assertTrue(abs(char_error_rate - 0.125   ) < 1e-6   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Tuple   )	-> str:
        a__     		=     ["werewolf"]
        a__     		=     ["wereolf"]
        # S = 0, D = 0, I = 1, N = 8, CER = 0.125
        a__     		=     cer.compute(predictions=lowerCAmelCase      ,      references=lowerCAmelCase   )
        self.assertTrue(abs(char_error_rate - 0.125   ) < 1e-6   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Union[str, Any]   )	-> Tuple:
        a__     		=     ["werewolf"]
        a__     		=     cer.compute(predictions=lowerCAmelCase      ,      references=lowerCAmelCase   )
        self.assertEqual(lowerCAmelCase      ,      0.0   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Optional[Any]   )	-> Optional[Any]:
        a__     		=     ["werewolf", "I am your father"]
        a__     		=     cer.compute(predictions=lowerCAmelCase      ,      references=lowerCAmelCase   )
        self.assertEqual(lowerCAmelCase      ,      0.0   )
  
        a__     		=     ["werewolf", "I am your father", "doge"]
        a__     		=     ["werxwolf", "I       am your father", "doge"]
        # S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
        a__     		=     cer.compute(predictions=lowerCAmelCase      ,      references=lowerCAmelCase   )
        self.assertTrue(abs(char_error_rate - 0.03_571_428   ) < 1e-6   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[Any]   )	-> List[str]:
        a__     		=     ["My hovercraft", "is full of eels"]
        a__     		=     ["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
        a__     		=     cer.compute(predictions=lowerCAmelCase      ,      references=lowerCAmelCase      ,      concatenate_texts=lowerCAmelCase   )
        self.assertTrue(abs(char_error_rate - 0.071_428   ) < 1e-6   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[Any]   )	-> List[str]:
        a__     		=     ["我能吞下玻璃而不伤身体"]
        a__     		=     [" 能吞虾玻璃而 不霜身体啦"]
        # S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
        a__     		=     cer.compute(predictions=lowerCAmelCase      ,      references=lowerCAmelCase   )
        self.assertTrue(abs(char_error_rate - 0.4_545_454_545   ) < 1e-6   )
  
        a__     		=     ["我能吞下玻璃", "而不伤身体"]
        a__     		=     ["我    能 吞 下 玻 璃", "而不伤身体"]
        # S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
        a__     		=     cer.compute(predictions=lowerCAmelCase      ,      references=lowerCAmelCase   )
        self.assertTrue(abs(char_error_rate - 0.454_545_454_545   ) < 1e-6   )
  
        a__     		=     ["我能吞下玻璃而不伤身体"]
        a__     		=     cer.compute(predictions=lowerCAmelCase      ,      references=lowerCAmelCase   )
        self.assertFalse(lowerCAmelCase      ,      0.0   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   str   )	-> List[Any]:
        a__     		=     [""]
        a__     		=     ["Hypothesis"]
        with self.assertRaises(lowerCAmelCase   ):
          cer.compute(predictions=lowerCAmelCase      ,      references=lowerCAmelCase   )
  
  
  if __name__ == "__main__":
       unittest.main()
  
  
style_context: from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _SCREAMING_SNAKE_CASE   						=    {
      """configuration_open_llama""": ["""OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenLlamaConfig"""],
  }
  
  try:
       if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _SCREAMING_SNAKE_CASE   						=    ["""LlamaTokenizer"""]
  
  try:
       if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _SCREAMING_SNAKE_CASE   						=    ["""LlamaTokenizerFast"""]
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       _SCREAMING_SNAKE_CASE   						=    [
           """OpenLlamaForCausalLM""",
           """OpenLlamaModel""",
           """OpenLlamaPreTrainedModel""",
           """OpenLlamaForSequenceClassification""",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig
  
       try:
            if not is_sentencepiece_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from transformers import LlamaTokenizer
  
       try:
            if not is_tokenizers_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from transformers import LlamaTokenizerFast
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_open_llama import (
                OpenLlamaForCausalLM,
                OpenLlamaForSequenceClassification,
                OpenLlamaModel,
                OpenLlamaPreTrainedModel,
            )
  
  
  else:
       import sys
  
       _SCREAMING_SNAKE_CASE   						=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  