





import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 				_lowerCAmelCase			(	UpperCAmelCase_							,			unittest.TestCase							):


  '''simple docstring'''
  a_							:				Union[str, Any]					    =GPTNeoXJapaneseTokenizer
  a_							:				Optional[Any]					    =False
  a_							:				int					    ={"""do_clean_text""": False, """add_prefix_space""": False}







  def UpperCamelCase_       (					self					:    List[Any]							):


    '''simple docstring'''





    super().setUp()

    _snake_case      : Optional[int]														=		[
        'こん',
        'こんに',
        'にちは',
        'ばんは',
        '世界,㔺界',
        '、',
        '。',
        '<BR>',
        '<SP>',
        '<TAB>',
        '<URL>',
        '<EMAIL>',
        '<TEL>',
        '<DATE>',
        '<PRICE>',
        '<BLOCK>',
        '<KIGOU>',
        '<U2000U2BFF>',
        '<|emoji1|>',
        '<unk>',
        '<|startoftext|>',
        '<|endoftext|>',
    ]
    _snake_case      : Tuple														=		{'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}}  # 😀
    _snake_case      : Dict														=		{'unk_token': '<unk>'}

    _snake_case      : Optional[Any]														=		os.path.join(self.tmpdirname					, VOCAB_FILES_NAMES['vocab_file']							)
    _snake_case      : Optional[int]														=		os.path.join(self.tmpdirname					, VOCAB_FILES_NAMES['emoji_file']							)
    with open(self.vocab_file					, 'w'					, encoding='utf-8'							) as vocab_writer:
      vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]							)							)
    with open(self.emoji_file					, 'w'							) as emoji_writer:
      emoji_writer.write(json.dumps(UpperCamelCase							)							)







  def UpperCamelCase_       (					self					:    Optional[Any]					, **UpperCamelCase					:    str							):


    '''simple docstring'''





    kwargs.update(self.special_tokens_map							)
    return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname					, **UpperCamelCase							)







  def UpperCamelCase_       (					self					:    Tuple					, UpperCamelCase					:    Any							):


    '''simple docstring'''





    _snake_case      : Union[str, Any]														=		'こんにちは、世界。 \nこんばんは、㔺界。😀'
    _snake_case      : Any														=		'こんにちは、世界。 \nこんばんは、世界。😀'
    return input_text, output_text







  def UpperCamelCase_       (					self					:    str					, UpperCamelCase					:    Tuple							):


    '''simple docstring'''





    _snake_case							,    _snake_case      : int														=		self.get_input_output_texts(UpperCamelCase							)
    _snake_case      : Dict														=		tokenizer.encode(UpperCamelCase					, add_special_tokens=UpperCamelCase							)
    _snake_case      : str														=		tokenizer.decode(UpperCamelCase					, clean_up_tokenization_spaces=UpperCamelCase							)
    return text, ids







  def UpperCamelCase_       (					self					:    List[str]							):


    '''simple docstring'''





    pass  # TODO add if relevant







  def UpperCamelCase_       (					self					:    Tuple							):


    '''simple docstring'''





    pass  # TODO add if relevant







  def UpperCamelCase_       (					self					:    Optional[Any]							):


    '''simple docstring'''





    pass  # TODO add if relevant







  def UpperCamelCase_       (					self					:    Dict							):


    '''simple docstring'''





    _snake_case      : Dict														=		self.get_tokenizer()

    # Testing tokenization
    _snake_case      : int														=		'こんにちは、世界。　こんばんは、㔺界。'
    _snake_case      : Tuple														=		['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
    _snake_case      : Optional[int]														=		tokenizer.tokenize(UpperCamelCase							)
    self.assertListEqual(UpperCamelCase					, UpperCamelCase							)

    # Testing conversion to ids without special tokens
    _snake_case      : Dict														=		[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
    _snake_case      : Union[str, Any]														=		tokenizer.convert_tokens_to_ids(UpperCamelCase							)
    self.assertListEqual(UpperCamelCase					, UpperCamelCase							)

    # Testing conversion to ids with special tokens
    _snake_case      : str														=		tokens + [tokenizer.unk_token]
    _snake_case      : Optional[int]														=		[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
    _snake_case      : Any														=		tokenizer.convert_tokens_to_ids(UpperCamelCase							)
    self.assertListEqual(UpperCamelCase					, UpperCamelCase							)







  @slow
  def UpperCamelCase_       (					self					:    List[Any]							):


    '''simple docstring'''





    _snake_case      : int														=		self.tokenizer_class.from_pretrained('abeja/gpt-neox-japanese-2.7b'							)

    _snake_case      : List[str]														=		tokenizer.encode('ありがとう。'					, add_special_tokens=UpperCamelCase							)
    _snake_case      : Optional[Any]														=		tokenizer.encode('どういたしまして。'					, add_special_tokens=UpperCamelCase							)

    _snake_case      : str														=		tokenizer.build_inputs_with_special_tokens(UpperCamelCase							)
    _snake_case      : int														=		tokenizer.build_inputs_with_special_tokens(UpperCamelCase					, UpperCamelCase							)

    assert encoded_sentence == ids_a
    assert encoded_pair == ids_a + ids_a







  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





    pass







  def UpperCamelCase_       (					self					:    Optional[int]							):


    '''simple docstring'''





    pass











def 			lowerCamelCase_		(					lowerCAmelCase:		str			,							lowerCAmelCase:		str )->						int:
  # The longer word should come first
  if len(lowerCAmelCase ) < len(lowerCAmelCase ):
    return levenshtein_distance(lowerCAmelCase			,							lowerCAmelCase )

  if len(lowerCAmelCase ) == 0:
    return len(lowerCAmelCase )

  _snake_case      : Dict														=		list(range(len(lowerCAmelCase ) + 1 ) )

  for i, ca in enumerate(lowerCAmelCase ):
    _snake_case      : Optional[int]														=		[i + 1]

    for j, ca in enumerate(lowerCAmelCase ):
      # Calculate insertions, deletions and substitutions
      _snake_case      : Optional[Any]														=		previous_row[j + 1] + 1
      _snake_case      : Optional[int]														=		current_row[j] + 1
      _snake_case      : Union[str, Any]														=		previous_row[j] + (ca != ca)

      # Get the minimum to append to the current row
      current_row.append(min(lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase ) )

    # Store the previous row
    _snake_case      : str														=		current_row

  # Returns the last element (distance)
  return previous_row[-1]


if __name__ == "__main__":
      lowerCAmelCase_        =			input("""Enter the first word:\n""").strip()
      lowerCAmelCase_        =			input("""Enter the second word:\n""").strip()

      lowerCAmelCase_        =			levenshtein_distance(first_word, second_word)
      print(F"""Levenshtein distance between {first_word} and {second_word} is {result}""")




