


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       (	snake_case__	,					unittest.TestCase   ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE						      =    GPTNeoXJapaneseTokenizer
  _SCREAMING_SNAKE_CASE						      =    False
  _SCREAMING_SNAKE_CASE						      =    {'do_clean_text': False, 'add_prefix_space': False}



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




     """simple docstring"""
     super().setUp()

     snake_case_         =   [
         '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_         =   {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}}  # 😀
     snake_case_         =   {'unk_token': '<unk>'}

     snake_case_         =   os.path.join(self.tmpdirname      ,			VOCAB_FILES_NAMES["vocab_file"]					)
     snake_case_         =   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(_lowerCAmelCase					)					)



  def    lowerCAmelCase__					(       self    :    List[Any]      ,			**_lowerCAmelCase    :    int					)				->	int:




     """simple docstring"""
     kwargs.update(self.special_tokens_map					)
     return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname      ,			**_lowerCAmelCase					)



  def    lowerCAmelCase__					(       self    :    List[str]      ,			_lowerCAmelCase    :    Optional[int]					)				->	Any:




     """simple docstring"""
     snake_case_         =   'こんにちは、世界。 \nこんばんは、㔺界。😀'
     snake_case_         =   'こんにちは、世界。 \nこんばんは、世界。😀'
     return input_text, output_text



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




     """simple docstring"""
     snake_case_         =   self.get_input_output_texts(_lowerCAmelCase					)
     snake_case_         =   tokenizer.encode(_lowerCAmelCase      ,			add_special_tokens=_lowerCAmelCase					)
     snake_case_         =   tokenizer.decode(_lowerCAmelCase      ,			clean_up_tokenization_spaces=_lowerCAmelCase					)
     return text, ids



  def    lowerCAmelCase__					(       self    :    Tuple					)				->	Optional[int]:




     """simple docstring"""
     pass  # TODO add if relevant



  def    lowerCAmelCase__					(       self    :    int					)				->	List[str]:




     """simple docstring"""
     pass  # TODO add if relevant



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




     """simple docstring"""
     pass  # TODO add if relevant



  def    lowerCAmelCase__					(       self    :    List[str]					)				->	int:




     """simple docstring"""
     snake_case_         =   self.get_tokenizer()

     # Testing tokenization
     snake_case_         =   'こんにちは、世界。　こんばんは、㔺界。'
     snake_case_         =   ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
     snake_case_         =   tokenizer.tokenize(_lowerCAmelCase					)
     self.assertListEqual(_lowerCAmelCase      ,			_lowerCAmelCase					)

     # Testing conversion to ids without special tokens
     snake_case_         =   [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
     snake_case_         =   tokenizer.convert_tokens_to_ids(_lowerCAmelCase					)
     self.assertListEqual(_lowerCAmelCase      ,			_lowerCAmelCase					)

     # Testing conversion to ids with special tokens
     snake_case_         =   tokens + [tokenizer.unk_token]
     snake_case_         =   [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 1_9]
     snake_case_         =   tokenizer.convert_tokens_to_ids(_lowerCAmelCase					)
     self.assertListEqual(_lowerCAmelCase      ,			_lowerCAmelCase					)



  @slow
  def    lowerCAmelCase__					(       self    :    Optional[Any]					)				->	Union[str, Any]:




     """simple docstring"""
     snake_case_         =   self.tokenizer_class.from_pretrained("abeja/gpt-neox-japanese-2.7b"					)

     snake_case_         =   tokenizer.encode("ありがとう。"      ,			add_special_tokens=_lowerCAmelCase					)
     snake_case_         =   tokenizer.encode("どういたしまして。"      ,			add_special_tokens=_lowerCAmelCase					)

     snake_case_         =   tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase					)
     snake_case_         =   tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase      ,			_lowerCAmelCase					)

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



  def    lowerCAmelCase__					(       self    :    str					)				->	int:




     """simple docstring"""
     # Intentionally convert some words to accommodate character fluctuations unique to Japanese
     pass



  def    lowerCAmelCase__					(       self    :    Any					)				->	str:




     """simple docstring"""
     # tokenizer has no padding token
     pass







import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey


class       __lowerCAmelCase       :






  """simple docstring"""





  def __init__(       self    :    Optional[Any]      ,			_lowerCAmelCase    :    Union[str, Any]      ,			_lowerCAmelCase    :    int					)				->	Tuple:




     """simple docstring"""
     snake_case_         =   0
     # max greyscale value for #FFFFFF
     snake_case_         =   int(self.get_greyscale(2_5_5      ,			2_5_5      ,			2_5_5					)					)

     if not self.min_threshold < threshold < self.max_threshold:
        snake_case_         =   F'''Factor value should be from 0 to {self.max_threshold}'''
        raise ValueError(_lowerCAmelCase					)

     snake_case_         =   input_img
     snake_case_         =   threshold
     snake_case_      ,      snake_case_         =   self.input_img.shape[1], self.input_img.shape[0]

     # error table size (+4 columns and +1 row) greater than input image because of
     # lack of if statements
     snake_case_         =   [
         [0 for _ in range(self.height + 4					)] for __ in range(self.width + 1					)
     ]
     snake_case_         =   np.ones((self.width, self.height, 3)      ,			np.uinta					) * 2_5_5



  @classmethod
  def    lowerCAmelCase__					(       cls    :    Optional[Any]      ,			_lowerCAmelCase    :    int      ,			_lowerCAmelCase    :    int      ,			_lowerCAmelCase    :    int					)				->	float:




     """simple docstring"""
     return 0.114 * blue + 0.587 * green + 0.2_126 * red



  def    lowerCAmelCase__					(       self    :    Union[str, Any]					)				->	None:




     """simple docstring"""
     for y in range(self.height					):
        for x in range(self.width					):
           snake_case_         =   int(self.get_greyscale(*self.input_img[y][x]					)					)
           if self.threshold > greyscale + self.error_table[y][x]:
              snake_case_         =   (0, 0, 0)
              snake_case_         =   greyscale + self.error_table[x][y]
           else:
              snake_case_         =   (2_5_5, 2_5_5, 2_5_5)
              snake_case_         =   greyscale + self.error_table[x][y] - 2_5_5
           self.error_table[y][x + 1] += int(8 / 3_2 * current_error					)
           self.error_table[y][x + 2] += int(4 / 3_2 * current_error					)
           self.error_table[y + 1][x] += int(8 / 3_2 * current_error					)
           self.error_table[y + 1][x + 1] += int(4 / 3_2 * current_error					)
           self.error_table[y + 1][x + 2] += int(2 / 3_2 * current_error					)
           self.error_table[y + 1][x - 1] += int(4 / 3_2 * current_error					)
           self.error_table[y + 1][x - 2] += int(2 / 3_2 * current_error					)


if __name__ == "__main__":
   # create Burke's instances with original images in greyscale
   SCREAMING_SNAKE_CASE		:Dict											=       [
       Burkes(imread('''image_data/lena.jpg''', 1), threshold)
       for threshold in (1, 1_26, 1_30, 1_40)
   ]

   for burkes in burkes_instances:
      burkes.process()

   for burkes in burkes_instances:
      imshow(
          F'''Original image with dithering threshold: {burkes.threshold}''',
          burkes.output_img,
      )

   waitKey(0)
   destroyAllWindows()




