





import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 					A__   (       __snake_case       ,   unittest.TestCase			):
      _UpperCAmelCase	:Optional[Any]						    =   OpenAIGPTTokenizer
      _UpperCAmelCase	:Dict						    =   OpenAIGPTTokenizerFast
      _UpperCAmelCase	:Tuple						    =   True
      _UpperCAmelCase	:str						    =   False





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       UpperCamelCase	:					Dict       	=   [
           "l",
           "o",
           "w",
           "e",
           "r",
           "s",
           "t",
           "i",
           "d",
           "n",
           "w</w>",
           "r</w>",
           "t</w>",
           "lo",
           "low",
           "er</w>",
           "low</w>",
           "lowest</w>",
           "newer</w>",
           "wider</w>",
           "<unk>",
       ]
       UpperCamelCase	:					List[str]       	=   dict(zip(A_       ,							range(len(A_   )   )   )   )
       UpperCamelCase	:					Optional[Any]       	=   ["#version: 0.2", "l o", "lo w", "e r</w>", ""]

       UpperCamelCase	:					Dict       	=   os.path.join(self.tmpdirname       ,							VOCAB_FILES_NAMES["vocab_file"]   )
       UpperCamelCase	:					Dict       	=   os.path.join(self.tmpdirname       ,							VOCAB_FILES_NAMES["merges_file"]   )
       with open(self.vocab_file       ,							"w"   ) as fp:
        fp.write(json.dumps(A_   )   )
       with open(self.merges_file       ,							"w"   ) as fp:
        fp.write("\n".join(A_   )   )





      def        __UpperCamelCase( self       ,							A_   ):
       '''simple docstring'''




       return "lower newer", "lower newer"





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					str       	=   OpenAIGPTTokenizer(self.vocab_file       ,							self.merges_file   )

       UpperCamelCase	:					Dict       	=   "lower"
       UpperCamelCase	:					List[str]       	=   ["low", "er</w>"]
       UpperCamelCase	:					Any       	=   tokenizer.tokenize(A_   )
       self.assertListEqual(A_       ,							A_   )

       UpperCamelCase	:					Union[str, Any]       	=   tokens + ["<unk>"]
       UpperCamelCase	:					Optional[int]       	=   [14, 15, 20]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(A_   )       ,							A_   )





      def        __UpperCamelCase( self       ,							A_=15   ):
       '''simple docstring'''




       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
         UpperCamelCase	:					int       	=   self.rust_tokenizer_class.from_pretrained(A_       ,							**A_   )

         # Simple input
         UpperCamelCase	:					Optional[Any]       	=   "This is a simple input"
         UpperCamelCase	:					Tuple       	=   ["This is a simple input 1", "This is a simple input 2"]
         UpperCamelCase	:					Any       	=   ("This is a simple input", "This is a pair")
         UpperCamelCase	:					List[str]       	=   [
             ("This is a simple input 1", "This is a simple input 2"),
             ("This is a simple pair 1", "This is a simple pair 2"),
         ]

         # Simple input tests
         self.assertRaises(A_       ,							tokenizer_r.encode       ,							A_       ,							max_length=A_       ,							padding="max_length"   )

         # Simple input
         self.assertRaises(A_       ,							tokenizer_r.encode_plus       ,							A_       ,							max_length=A_       ,							padding="max_length"   )

         # Simple input
         self.assertRaises(
             A_       ,							tokenizer_r.batch_encode_plus       ,							A_       ,							max_length=A_       ,							padding="max_length"       ,							)

         # Pair input
         self.assertRaises(A_       ,							tokenizer_r.encode       ,							A_       ,							max_length=A_       ,							padding="max_length"   )

         # Pair input
         self.assertRaises(A_       ,							tokenizer_r.encode_plus       ,							A_       ,							max_length=A_       ,							padding="max_length"   )

         # Pair input
         self.assertRaises(
             A_       ,							tokenizer_r.batch_encode_plus       ,							A_       ,							max_length=A_       ,							padding="max_length"       ,							)





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       pass



@require_ftfy
@require_spacy
@require_tokenizers
class 					A__   (       __snake_case			):
      pass







import functools



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> int:
 UpperCamelCase	:					Optional[int]       	=   len(_lowerCAmelCase     )
 UpperCamelCase	:					List[str]       	=   len(_lowerCAmelCase     )

 @functools.cache
 def min_distance(_lowerCAmelCase							,   _lowerCAmelCase     ) -> int:
  # if first word index is overflow - delete all from the second word
  if indexa >= len_worda:
   return len_worda - indexa
  # if second word index is overflow - delete all from the first word
  if indexa >= len_worda:
   return len_worda - indexa
  UpperCamelCase	:					Union[str, Any]       	=   int(worda[indexa] != worda[indexa]     )  # current letters not identical
  return min(
      1 + min_distance(indexa + 1							,   _lowerCAmelCase     )							,   1 + min_distance(_lowerCAmelCase							,   indexa + 1     )							,   diff + min_distance(indexa + 1							,   indexa + 1     )							,   )

 return min_distance(0							,   0     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
