





'''simple docstring'''







import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin





class 					_UpperCAmelCase							(		lowerCAmelCase_			,      unittest.TestCase     ):
		a     :     int     						=BertweetTokenizer
		a     :     Any     						=False
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					super().setUp()

					# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
					__lowerCAmelCase      		=       ["""I""", """m""", """V@@""", """R@@""", """r""", """e@@"""]
					__lowerCAmelCase      		=       dict(zip(__SCREAMING_SNAKE_CASE,range(len(__SCREAMING_SNAKE_CASE    )    )    )    )
					__lowerCAmelCase      		=       ["""#version: 0.2""", """a m</w>"""]
					__lowerCAmelCase      		=       {"""unk_token""": """<unk>"""}

					__lowerCAmelCase      		=       os.path.join(self.tmpdirname,VOCAB_FILES_NAMES["""vocab_file"""]    )
					__lowerCAmelCase      		=       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(__SCREAMING_SNAKE_CASE    )    )
		def 						lowerCamelCase__    (						self,**__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					kwargs.update(self.special_tokens_map    )
					return BertweetTokenizer.from_pretrained(self.tmpdirname,**__SCREAMING_SNAKE_CASE    )
		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       """I am VinAI Research"""
					__lowerCAmelCase      		=       """I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
					return input_text, output_text







		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       BertweetTokenizer(self.vocab_file,self.merges_file,**self.special_tokens_map    )
					__lowerCAmelCase      		=       """I am VinAI Research"""
					__lowerCAmelCase      		=       """I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
					__lowerCAmelCase      		=       tokenizer.tokenize(__SCREAMING_SNAKE_CASE    )
					self.assertListEqual(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )

					__lowerCAmelCase      		=       tokens + [tokenizer.unk_token]

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








'''simple docstring'''







from cva import destroyAllWindows, imread, imshow, waitKey





def      _lowerCAmelCase		(		lowercase      ,			lowercase			)      ->      int:
			__lowerCAmelCase		, __lowerCAmelCase      		=       img.shape[0], img.shape[1]

			def to_grayscale(lowercase      ,			lowercase      ,			lowercase			):
						return 0.21_26 * red + 0.5_87 * green + 0.1_14 * blue

			def normalize(lowercase			):
						return min(lowercase      ,			255			)

			for i in range(lowercase			):
						for j in range(lowercase			):
									__lowerCAmelCase      		=       int(to_grayscale(*img[i][j]			)			)
									__lowerCAmelCase      		=       [
									    normalize(lowercase			),
									    normalize(greyscale + factor			),
									    normalize(greyscale + 2 * factor			),
									]

			return img


if __name__ == "__main__":
	# read original image
	_a      :				Any											=					{
	    percentage: imread("""image_data/lena.jpg""", 1) for percentage in (1_0, 2_0, 3_0, 4_0)
	}

	for percentage, img in images.items():
		make_sepia(img, percentage)

	for percentage, img in images.items():
		imshow(f'Original image with sepia (factor: {percentage})', img)

	waitKey(0)
	destroyAllWindows()

