






"""simple docstring"""







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:
 lowerCAmelCase_		:			Any				   =					None


lowerCAmelCase_		:			Dict				   =					logging.get_logger(__name__)

lowerCAmelCase_		:			List[str]				   =					{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase_		:			Dict				   =					{
    '''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''',
    },
}

lowerCAmelCase_		:			Optional[Any]				   =					{
    '''facebook/xglm-564M''': 2_0_4_8,
}





class      UpperCamelCase_       (				a_     ):
  _A				:    Optional[int] =		VOCAB_FILES_NAMES
  _A				:    Union[str, Any] =		PRETRAINED_VOCAB_FILES_MAP
  _A				:    Optional[int] =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _A				:    Dict =		['input_ids', 'attention_mask']
  _A				:    Union[str, Any] =		XGLMTokenizer



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



         """simple docstring"""





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

         UpperCAmelCase					=	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__(
             snake_case__       ,						tokenizer_file=snake_case__       ,						bos_token=snake_case__       ,						eos_token=snake_case__       ,						sep_token=snake_case__       ,						cls_token=snake_case__       ,						unk_token=snake_case__       ,						pad_token=snake_case__       ,						**snake_case__       ,						)

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



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ = None )	->	List[int]:



         """simple docstring"""





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



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ = None )	->	List[int]:



         """simple docstring"""





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

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

         return (out_vocab_file,)








"""simple docstring"""







import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

lowerCAmelCase_		:			List[Any]				   =					1_8_0






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       UpperCAmelCase					=	k_size // 2
       UpperCAmelCase			,				UpperCAmelCase					=	np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       UpperCAmelCase					=	(
           1
           / (2 * np.pi * sigma)
           * np.exp(-(np.square(lowerCAmelCase						) + np.square(lowerCAmelCase						)) / (2 * np.square(lowerCAmelCase						))						)
       )
       return g






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       UpperCAmelCase					=	np.zeros(lowerCAmelCase						)

       for row in range(1		,					image_shape[0] - 1						):
              for col in range(1		,					image_shape[1] - 1						):
                     UpperCAmelCase					=	gradient_direction[row, col]

                     if (
                         0 <= direction < PI / 8
                         or 15 * PI / 8 <= direction <= 2 * PI
                         or 7 * PI / 8 <= direction <= 9 * PI / 8
                     ):
                            UpperCAmelCase					=	sobel_grad[row, col - 1]
                            UpperCAmelCase					=	sobel_grad[row, col + 1]
                            if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                                   UpperCAmelCase					=	sobel_grad[row, col]

                     elif (
                         PI / 8 <= direction < 3 * PI / 8
                         or 9 * PI / 8 <= direction < 11 * PI / 8
                     ):
                            UpperCAmelCase					=	sobel_grad[row + 1, col - 1]
                            UpperCAmelCase					=	sobel_grad[row - 1, col + 1]
                            if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                                   UpperCAmelCase					=	sobel_grad[row, col]

                     elif (
                         3 * PI / 8 <= direction < 5 * PI / 8
                         or 11 * PI / 8 <= direction < 13 * PI / 8
                     ):
                            UpperCAmelCase					=	sobel_grad[row - 1, col]
                            UpperCAmelCase					=	sobel_grad[row + 1, col]
                            if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                                   UpperCAmelCase					=	sobel_grad[row, col]

                     elif (
                         5 * PI / 8 <= direction < 7 * PI / 8
                         or 13 * PI / 8 <= direction < 15 * PI / 8
                     ):
                            UpperCAmelCase					=	sobel_grad[row - 1, col - 1]
                            UpperCAmelCase					=	sobel_grad[row + 1, col + 1]
                            if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                                   UpperCAmelCase					=	sobel_grad[row, col]

       return destination






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       for row in range(1		,					image_shape[0] - 1						):
              for col in range(1		,					image_shape[1] - 1						):
                     if destination[row, col] >= threshold_high:
                            UpperCAmelCase					=	strong
                     elif destination[row, col] <= threshold_low:
                            UpperCAmelCase					=	0
                     else:
                            UpperCAmelCase					=	weak






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						):







       '''simple docstring'''
       for row in range(1		,					image_shape[0]						):
              for col in range(1		,					image_shape[1]						):
                     if destination[row, col] == weak:
                            if 255 in (
                                destination[row, col + 1],
                                destination[row, col - 1],
                                destination[row - 1, col],
                                destination[row + 1, col],
                                destination[row - 1, col - 1],
                                destination[row + 1, col - 1],
                                destination[row - 1, col + 1],
                                destination[row + 1, col + 1],
                            ):
                                   UpperCAmelCase					=	strong
                            else:
                                   UpperCAmelCase					=	0






def 					_lowerCAmelCase				(					lowerCAmelCase		,					lowerCAmelCase=15		,					lowerCAmelCase=30		,					lowerCAmelCase=128		,					lowerCAmelCase=255						):







       '''simple docstring'''
       # gaussian_filter
       UpperCAmelCase					=	img_convolve(lowerCAmelCase		,					gen_gaussian_kernel(9		,					sigma=1.4						)						)
       # get the gradient and degree by sobel_filter
       UpperCAmelCase			,				UpperCAmelCase					=	sobel_filter(lowerCAmelCase						)
       UpperCAmelCase					=	PI + np.radadeg(lowerCAmelCase						)

       UpperCAmelCase					=	suppress_non_maximum(image.shape		,					lowerCAmelCase		,					lowerCAmelCase						)

       detect_high_low_threshold(
           image.shape		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						)

       track_edge(image.shape		,					lowerCAmelCase		,					lowerCAmelCase		,					lowerCAmelCase						)

       return destination


if __name__ == "__main__":
 # read original image in gray mode
 lowerCAmelCase_		:			Optional[Any]				   =					cva.imread(R'''../image_data/lena.jpg''', 0)
 # canny edge detection
 lowerCAmelCase_		:			Dict				   =					canny(lena)
 cva.imshow('''canny''', canny_destination)
 cva.waitKey(0)
