




'''simple docstring'''







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





class      lowerCamelCase_							:




					'''simple docstring'''






					def __init__(    self,  __UpperCamelCase,  __UpperCamelCase ):
							_snake_case	       =       0
							# max greyscale value for #FFFFFF
							_snake_case	       =       int(self.get_greyscale(255,  255,  255 ) )

							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 ) * 255






					@classmethod
					def 				__SCREAMING_SNAKE_CASE	(    cls,  __UpperCamelCase,  __UpperCamelCase,  __UpperCamelCase ):
							return 0.1_14 * blue + 0.5_87 * green + 0.21_26 * red






					def 				__SCREAMING_SNAKE_CASE	(    self ):
							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	       =       (255, 255, 255)
													_snake_case	       =       greyscale + self.error_table[x][y] - 255
											self.error_table[y][x + 1] += int(8 / 32 * current_error )
											self.error_table[y][x + 2] += int(4 / 32 * current_error )
											self.error_table[y + 1][x] += int(8 / 32 * current_error )
											self.error_table[y + 1][x + 1] += int(4 / 32 * current_error )
											self.error_table[y + 1][x + 2] += int(2 / 32 * current_error )
											self.error_table[y + 1][x - 1] += int(4 / 32 * current_error )
											self.error_table[y + 1][x - 2] += int(2 / 32 * current_error )


if __name__ == "__main__":
						# create Burke's instances with original images in greyscale
						_lowerCamelCase							:						int								=  [
						    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()











'''simple docstring'''


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:
				_snake_case      :      int   							=		None


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


_snake_case      :      Union[str, Any]   							=		'▁'

_snake_case      :      Any   							=		{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

_snake_case      :      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'
        )
    },
}

_snake_case      :      Union[str, Any]   							=		{
    'google/reformer-crime-and-punishment': 524288,
}

class 			A    (		_a    ):
							lowercase_							=				VOCAB_FILES_NAMES
							lowercase_							=				PRETRAINED_VOCAB_FILES_MAP
							lowercase_							=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_							=				['input_ids', 'attention_mask']
							lowercase_							=				ReformerTokenizer







							def __init__(  self       :  Any							,			lowerCAmelCase_       :  int=None							,			lowerCAmelCase_       :  Optional[Any]=None							,			lowerCAmelCase_       :  List[str]="</s>"							,			lowerCAmelCase_       :  Any="<unk>"							,			lowerCAmelCase_       :  List[str]=[]							,			**lowerCAmelCase_       :  List[Any]							,			)			->							Dict:


														"""simple docstring"""


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

														_a	     =				vocab_file
														_a	     =				False if not self.vocab_file else True







							def    __lowerCAmelCase			(  self       :  List[Any]							,			lowerCAmelCase_       :  str							,			lowerCAmelCase_       :  Optional[str] = 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(lowerCAmelCase_					):
																					logger.error(F'Vocabulary path ({save_directory}) should be a directory'					)
																					return
														_a	     =				os.path.join(
														    lowerCAmelCase_							,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

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

														return (out_vocab_file,)

