

import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class      snake_case_       (		lowerCamelCase_     ,	unittest.TestCase ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       str						      =    BertweetTokenizer
			SCREAMING_SNAKE_CASE					:       int						      =    False







			def     snake_case__(					self			:    Dict							)				->List[str]:
									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									snake_case_       =     ["I", "m", "V@@", "R@@", "r", "e@@"]
									snake_case_       =     dict(zip(_UpperCamelCase							,       range(len(_UpperCamelCase							)							)							)							)
									snake_case_       =     ["#version: 0.2", "a m</w>"]
									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['''merges_file''']							)
									with open(self.vocab_file							,       '''w'''							,       encoding='''utf-8'''							) as fp:
															for token in vocab_tokens:
																					fp.write(f'''{token} {vocab_tokens[token]}\n'''							)
									with open(self.merges_file							,       '''w'''							,       encoding='''utf-8'''							) as fp:
															fp.write('''\n'''.join(_UpperCamelCase							)							)







			def     snake_case__(					self			:    List[Any]							,       **_UpperCamelCase			:    List[Any]							)				->List[str]:
									kwargs.update(self.special_tokens_map							)
									return BertweetTokenizer.from_pretrained(self.tmpdirname							,       **_UpperCamelCase							)







			def     snake_case__(					self			:    List[str]							,       _UpperCamelCase			:    Union[str, Any]							)				->List[Any]:
									snake_case_       =     "I am VinAI Research"
									snake_case_       =     "I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
									return input_text, output_text







			def     snake_case__(					self			:    Union[str, Any]							)				->List[str]:
									snake_case_       =     BertweetTokenizer(self.vocab_file							,       self.merges_file							,       **self.special_tokens_map							)
									snake_case_       =     "I am VinAI Research"
									snake_case_       =     "I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
									snake_case_       =     tokenizer.tokenize(_UpperCamelCase							)
									self.assertListEqual(_UpperCamelCase							,       _UpperCamelCase							)

									snake_case_       =     tokens + [tokenizer.unk_token]

									snake_case_       =     [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase							)							,       _UpperCamelCase							)


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





class      snake_case_       :







			'''simple docstring'''

			def __init__(					self			:    Tuple							,       _UpperCamelCase			:    Optional[Any]							,       _UpperCamelCase			:    int							)				->List[Any]:
									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(_UpperCamelCase							)

									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     snake_case__(					cls			:    Optional[Any]							,       _UpperCamelCase			:    int							,       _UpperCamelCase			:    int							,       _UpperCamelCase			:    int							)				->float:
									return 0.114 * blue + 0.587 * green + 0.2126 * red







			def     snake_case__(					self			:    Any							)				->None:
									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
					lowerCAmelCase_	     =     [
					    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()