



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

class      snake_case__       :







						"""simple docstring"""




						def __init__(     self      ,     __lowercase      ,     __lowercase						)      ->   List[str]:
									"""simple docstring"""




									a__     :		Tuple		 =						0
									# max greyscale value for #FFFFFF
									a__     :		Dict		 =						int(self.get_greyscale(2_5_5      ,     2_5_5      ,     2_5_5						)						)

									if not self.min_threshold < threshold < self.max_threshold:
												a__     :		Union[str, Any]		 =						F'''Factor value should be from 0 to {self.max_threshold}'''
												raise ValueError(__lowercase						)

									a__     :		Tuple		 =						input_img
									a__     :		Optional[Any]		 =						threshold
									a__     :		str		 =						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
									a__     :		List[Any]		 =						[
									    [0 for _ in range(self.height + 4						)] for __ in range(self.width + 1						)
									]
									a__     :		Union[str, Any]		 =						np.ones((self.width, self.height, 3)      ,     np.uinta						) * 2_5_5

						@classmethod
						def 							SCREAMING_SNAKE_CASE__(     cls      ,     __lowercase      ,     __lowercase      ,     __lowercase						)      ->   float:
									"""simple docstring"""




									return 0.1_1_4 * blue + 0.5_8_7 * green + 0.2_1_2_6 * red

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   None:
									"""simple docstring"""




									for y in range(self.height						):
												for x in range(self.width						):
															a__     :		str		 =						int(self.get_greyscale(*self.input_img[y][x]						)						)
															if self.threshold > greyscale + self.error_table[y][x]:
																		a__     :		int		 =						(0, 0, 0)
																		a__     :		Optional[Any]		 =						greyscale + self.error_table[x][y]
															else:
																		a__     :		Any		 =						(2_5_5, 2_5_5, 2_5_5)
																		a__     :		List[str]		 =						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
		_lowercase	:   Union[str, Any]    	=[
		    Burkes(imread("image_data/lena.jpg", 1), threshold)
		    for threshold in (1, 126, 130, 140)
		]

		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()









import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_reformer import ReformerTokenizer
else:
		_lowercase	:   Optional[int]    	=None


_lowercase	:   Union[str, Any]    	=logging.get_logger(__name__)


_lowercase	:   Any    	="▁"

_lowercase	:   Union[str, Any]    	={"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_lowercase	:   List[str]    	={
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    },
    "tokenizer_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json"
        )
    },
}

_lowercase	:   List[str]    	={
    "google/reformer-crime-and-punishment": 52_4288,
}

class      snake_case__       (A__	):







						"""simple docstring"""




						__lowerCAmelCase  :Union[str, Any]			      =  VOCAB_FILES_NAMES
						__lowerCAmelCase  :int			      =  PRETRAINED_VOCAB_FILES_MAP
						__lowerCAmelCase  :List[Any]			      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__lowerCAmelCase  :Any			      =  ["input_ids", "attention_mask"]
						__lowerCAmelCase  :Tuple			      =  ReformerTokenizer

						def __init__(     self      ,     __lowercase=None      ,     __lowercase=None      ,     __lowercase="</s>"      ,     __lowercase="<unk>"      ,     __lowercase=[]      ,     **__lowercase      ,     )      ->   int:
									"""simple docstring"""




									super().__init__(
									    __lowercase      ,     tokenizer_file=__lowercase      ,     eos_token=__lowercase      ,     unk_token=__lowercase      ,     additional_special_tokens=__lowercase      ,     **__lowercase      ,     )

									a__     :		Union[str, Any]		 =						vocab_file
									a__     :		str		 =						False if not self.vocab_file else True

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase = None						)      ->   Tuple[str]:
									"""simple docstring"""




									if not self.can_save_slow_tokenizer:
												raise ValueError(
												    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
												    """tokenizer."""						)

									if not os.path.isdir(__lowercase						):
												logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''						)
												return
									a__     :		Tuple		 =						os.path.join(
									    __lowercase      ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

									if os.path.abspath(self.vocab_file						) != os.path.abspath(__lowercase						):
												copyfile(self.vocab_file      ,     __lowercase						)

									return (out_vocab_file,)




