

import unittest

from cer import CER


__a						:str            = CER()

class      _a				( unittest.TestCase							):




      """simple docstring"""

      def 		__A  (      self       :							List[str]  ):
            A_	     =							["White House"]
            A_	     =							["white house"]
            # S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
            A_	     =							cer.compute(predictions=UpperCAmelCase	, references=UpperCAmelCase  )
            self.assertTrue(abs(char_error_rate - 0.1_818_181_818  ) < 1E-6  )





      def 		__A  (      self       :							Union[str, Any]  ):
            A_	     =							["were wolf"]
            A_	     =							["werewolf"]
            # S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
            A_	     =							cer.compute(predictions=UpperCAmelCase	, references=UpperCAmelCase  )
            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=UpperCAmelCase	, references=UpperCAmelCase  )
            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=UpperCAmelCase	, references=UpperCAmelCase  )
            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=UpperCAmelCase	, references=UpperCAmelCase  )
            self.assertTrue(abs(char_error_rate - 0.0  ) < 1E-6  )





      def 		__A  (      self       :							List[Any]  ):
            A_	     =							["werewolf"]
            A_	     =							["weaewolf"]
            # S = 1, D = 0, I = 0, N = 8, CER = 0.125
            A_	     =							cer.compute(predictions=UpperCAmelCase	, references=UpperCAmelCase  )
            self.assertTrue(abs(char_error_rate - 0.125  ) < 1E-6  )





      def 		__A  (      self       :							Dict  ):
            A_	     =							["werewolf"]
            A_	     =							["wereawolf"]
            # S = 0, D = 1, I = 0, N = 8, CER = 0.125
            A_	     =							cer.compute(predictions=UpperCAmelCase	, references=UpperCAmelCase  )
            self.assertTrue(abs(char_error_rate - 0.125  ) < 1E-6  )





      def 		__A  (      self       :							Optional[Any]  ):
            A_	     =							["werewolf"]
            A_	     =							["wereolf"]
            # S = 0, D = 0, I = 1, N = 8, CER = 0.125
            A_	     =							cer.compute(predictions=UpperCAmelCase	, references=UpperCAmelCase  )
            self.assertTrue(abs(char_error_rate - 0.125  ) < 1E-6  )





      def 		__A  (      self       :							Any  ):
            A_	     =							["werewolf"]
            A_	     =							cer.compute(predictions=UpperCAmelCase	, references=UpperCAmelCase  )
            self.assertEqual(UpperCAmelCase	, 0.0  )





      def 		__A  (      self       :							Union[str, Any]  ):
            A_	     =							["werewolf", "I am your father"]
            A_	     =							cer.compute(predictions=UpperCAmelCase	, references=UpperCAmelCase  )
            self.assertEqual(UpperCAmelCase	, 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=UpperCAmelCase	, references=UpperCAmelCase  )
            self.assertTrue(abs(char_error_rate - 0.03_571_428  ) < 1E-6  )





      def 		__A  (      self       :							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=UpperCAmelCase	, references=UpperCAmelCase	, concatenate_texts=UpperCAmelCase  )
            self.assertTrue(abs(char_error_rate - 0.071_428  ) < 1E-6  )





      def 		__A  (      self       :							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=UpperCAmelCase	, references=UpperCAmelCase  )
            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=UpperCAmelCase	, references=UpperCAmelCase  )
            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=UpperCAmelCase	, references=UpperCAmelCase  )
            self.assertFalse(UpperCAmelCase	, 0.0  )







      def 		__A  (      self       :							Optional[int]  ):
            A_	     =							[""]
            A_	     =							["Hypothesis"]
            with self.assertRaises(UpperCAmelCase  ):
                  cer.compute(predictions=UpperCAmelCase	, references=UpperCAmelCase  )


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


import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
      from .tokenization_deberta_va import DebertaVaTokenizer
else:
      __a						:int            = None

__a						:List[str]            = logging.get_logger(__name__)

__a						:str            = {'vocab_file': 'spm.model', 'tokenizer_file': 'tokenizer.json'}

__a						:List[Any]            = {
    'vocab_file': {
        'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'
        ),
        'microsoft/deberta-v2-xxlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'
        ),
    }
}

__a						:Dict            = {
    'microsoft/deberta-v2-xlarge': 512,
    'microsoft/deberta-v2-xxlarge': 512,
    'microsoft/deberta-v2-xlarge-mnli': 512,
    'microsoft/deberta-v2-xxlarge-mnli': 512,
}

__a						:List[Any]            = {
    'microsoft/deberta-v2-xlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xlarge-mnli': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge-mnli': {'do_lower_case': False},
}

class      _a				( snake_case_							):




      """simple docstring"""

      _lowerCamelCase					: Dict					=					VOCAB_FILES_NAMES
      _lowerCamelCase					: Optional[Any]					=					PRETRAINED_VOCAB_FILES_MAP
      _lowerCamelCase					: Tuple					=					PRETRAINED_INIT_CONFIGURATION
      _lowerCamelCase					: Tuple					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowerCamelCase					: Optional[int]					=					DebertaVaTokenizer





      def __init__(      self       :							List[Any]	, UpperCAmelCase       :							str=None	, UpperCAmelCase       :							int=None	, UpperCAmelCase       :							List[str]=False	, UpperCAmelCase       :							Optional[Any]=False	, UpperCAmelCase       :							List[str]="[CLS]"	, UpperCAmelCase       :							str="[SEP]"	, UpperCAmelCase       :							List[str]="[UNK]"	, UpperCAmelCase       :							Tuple="[SEP]"	, UpperCAmelCase       :							Union[str, Any]="[PAD]"	, UpperCAmelCase       :							Union[str, Any]="[CLS]"	, UpperCAmelCase       :							Any="[MASK]"	, **UpperCAmelCase       :							Dict	, ):
            super().__init__(
                UpperCAmelCase	, tokenizer_file=UpperCAmelCase	, do_lower_case=UpperCAmelCase	, bos_token=UpperCAmelCase	, eos_token=UpperCAmelCase	, unk_token=UpperCAmelCase	, sep_token=UpperCAmelCase	, pad_token=UpperCAmelCase	, cls_token=UpperCAmelCase	, mask_token=UpperCAmelCase	, split_by_punct=UpperCAmelCase	, **UpperCAmelCase	, )

            A_	     =							do_lower_case
            A_	     =							split_by_punct
            A_	     =							vocab_file
            A_	     =							False if not self.vocab_file else True





      def 		__A  (      self       :							Optional[int]	, UpperCAmelCase       :							Tuple	, UpperCAmelCase       :							List[str]=None  ):

            if token_ids_a is None:
                  return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
            A_	     =							[self.cls_token_id]
            A_	     =							[self.sep_token_id]
            return cls + token_ids_a + sep + token_ids_a + sep





      def 		__A  (      self       :							List[Any]	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							Optional[int]=None	, UpperCAmelCase       :							int=False  ):

            if already_has_special_tokens:
                  return super().get_special_tokens_mask(
                      token_ids_a=UpperCAmelCase	, token_ids_a=UpperCAmelCase	, already_has_special_tokens=UpperCAmelCase  )

            if token_ids_a is not None:
                  return [1] + ([0] * len(UpperCAmelCase  )) + [1] + ([0] * len(UpperCAmelCase  )) + [1]
            return [1] + ([0] * len(UpperCAmelCase  )) + [1]





      def 		__A  (      self       :							List[Any]	, UpperCAmelCase       :							Union[str, Any]	, UpperCAmelCase       :							Optional[int]=None  ):
            A_	     =							[self.sep_token_id]
            A_	     =							[self.cls_token_id]
            if token_ids_a is None:
                  return len(cls + token_ids_a + sep  ) * [0]
            return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]







      def 		__A  (      self       :							str	, UpperCAmelCase       :							str	, UpperCAmelCase       :							Optional[str] = None  ):
            if not self.can_save_slow_tokenizer:
                  raise ValueError(
                      "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                      "tokenizer."  )

            if not os.path.isdir(UpperCAmelCase  ):
                  logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
                  return
            A_	     =							os.path.join(
                UpperCAmelCase	, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

            if os.path.abspath(self.vocab_file  ) != os.path.abspath(UpperCAmelCase  ):
                  copyfile(self.vocab_file	, UpperCAmelCase  )

            return (out_vocab_file,)