

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





class    __UpperCAmelCase	:



   def __init__(				self				:	int,						__A				:	Dict,						__A				:	Union[str, Any]		):
     UpperCAmelCase			:				Any										=					0
     # max greyscale value for #FFFFFF
     UpperCAmelCase			:				str										=					int(self.get_greyscale(2_5_5,						2_5_5,						2_5_5		)		)

     if not self.min_threshold < threshold < self.max_threshold:
       UpperCAmelCase			:				List[str]										=					F'''Factor value should be from 0 to {self.max_threshold}'''
       raise ValueError(__A		)

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



   @classmethod
   def 			__magic_name__       (				cls				:	Optional[Any],						__A				:	List[Any],						__A				:	List[str],						__A				:	Optional[int]		):
     return 0.1_1_4 * blue + 0.5_8_7 * green + 0.2_1_2_6 * red





   def 			__magic_name__       (				self				:	Union[str, Any]		):






     for y in range(self.height		):
       for x in range(self.width		):
         UpperCAmelCase			:				Union[str, Any]										=					int(self.get_greyscale(*self.input_img[y][x]		)		)
         if self.threshold > greyscale + self.error_table[y][x]:
           UpperCAmelCase			:				List[Any]										=					(0, 0, 0)
           UpperCAmelCase			:				int										=					greyscale + self.error_table[x][y]
         else:
           UpperCAmelCase			:				Dict										=					(2_5_5, 2_5_5, 2_5_5)
           UpperCAmelCase			:				Dict										=					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							:  Any     =				[
         Burkes(imread("image_data/lena.jpg", 1), threshold)
         for threshold in (1, 1_2_6, 1_3_0, 1_4_0)
     ]

     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 List, Optional, Tuple

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


if is_sentencepiece_available():
							from .tokenization_xglm import XGLMTokenizer
else:
							SCREAMING_SNAKE_CASE__       			=				None


SCREAMING_SNAKE_CASE__       			=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       			=				{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__       			=				{
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json""",
    },
}

SCREAMING_SNAKE_CASE__       			=				{
    """facebook/xglm-564M""": 2_0_4_8,
}







class     __lowerCamelCase     (  snake_case_     ):


							"""simple docstring"""

							lowerCAmelCase__          =				VOCAB_FILES_NAMES
							lowerCAmelCase__          =				PRETRAINED_VOCAB_FILES_MAP
							lowerCAmelCase__          =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCAmelCase__          =				["input_ids", "attention_mask"]
							lowerCAmelCase__          =				XGLMTokenizer




							def __init__(  self						,       UpperCAmelCase=None						,       UpperCAmelCase=None						,       UpperCAmelCase="<s>"						,       UpperCAmelCase="</s>"						,       UpperCAmelCase="</s>"						,       UpperCAmelCase="<s>"						,       UpperCAmelCase="<unk>"						,       UpperCAmelCase="<pad>"						,       **UpperCAmelCase						,       )     ->       Tuple:






								'''simple docstring'''






								lowercase_			 = 7
								lowercase_			 = [F'<madeupword{i}>' for i in range(self.num_madeup_words  )]

								lowercase_			 = kwargs.get("additional_special_tokens"						,       []  )
								kwargs["additional_special_tokens"] += [
								    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
								]

								super().__init__(
								    UpperCAmelCase						,       tokenizer_file=UpperCAmelCase						,       bos_token=UpperCAmelCase						,       eos_token=UpperCAmelCase						,       sep_token=UpperCAmelCase						,       cls_token=UpperCAmelCase						,       unk_token=UpperCAmelCase						,       pad_token=UpperCAmelCase						,       **UpperCAmelCase						,       )

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




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase = None  )     ->       List[int]:






								'''simple docstring'''






								if token_ids_a is None:
									return [self.sep_token_id] + token_ids_a
								lowercase_			 = [self.sep_token_id]
								return sep + token_ids_a + sep + sep + token_ids_a




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase = None  )     ->       List[int]:






								'''simple docstring'''






								lowercase_			 = [self.sep_token_id]

								if token_ids_a is None:
									return len(sep + token_ids_a  ) * [0]
								return len(sep + token_ids_a + sep + sep + token_ids_a  ) * [0]




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase = 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(UpperCAmelCase  ):
									logger.error(F'Vocabulary path ({save_directory}) should be a directory.'  )
									return
								lowercase_			 = os.path.join(
								    UpperCAmelCase						,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

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

								return (out_vocab_file,)

