code: 
  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
  
  snake_case				:	Any							   =						180
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						Dict   ,     _snake_case   :						List[str]     )       ->	Dict:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	Dict			     =  k_size // 2
  	__magic_name__     ,					__magic_name__	:	Optional[int]			     =  np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
  	__magic_name__	:	int			     =  (
  	    1
  	    / (2 * np.pi * sigma)
  	    * np.exp(-(np.square(__snake_case     ) + np.square(__snake_case     )) / (2 * np.square(__snake_case     ))     )
  	)
  	return g
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						str   ,     _snake_case   :						List[str]   ,     _snake_case   :						List[str]     )       ->	int:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	List[Any]			     =  np.zeros(__snake_case     )
  
  	for row in range(1   ,     image_shape[0] - 1     ):
  		for col in range(1   ,     image_shape[1] - 1     ):
  			__magic_name__	:	List[Any]			     =  gradient_direction[row, col]
  
  			if (
  			    0 <= direction < PI / 8
  			    or 15 * PI / 8 <= direction <= 2 * PI
  			    or 7 * PI / 8 <= direction <= 9 * PI / 8
  			):
  				__magic_name__	:	Optional[int]			     =  sobel_grad[row, col - 1]
  				__magic_name__	:	Union[str, Any]			     =  sobel_grad[row, col + 1]
  				if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
  					__magic_name__	:	List[str]			     =  sobel_grad[row, col]
  
  			elif (
  			    PI / 8 <= direction < 3 * PI / 8
  			    or 9 * PI / 8 <= direction < 11 * PI / 8
  			):
  				__magic_name__	:	Optional[Any]			     =  sobel_grad[row + 1, col - 1]
  				__magic_name__	:	Any			     =  sobel_grad[row - 1, col + 1]
  				if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
  					__magic_name__	:	List[Any]			     =  sobel_grad[row, col]
  
  			elif (
  			    3 * PI / 8 <= direction < 5 * PI / 8
  			    or 11 * PI / 8 <= direction < 13 * PI / 8
  			):
  				__magic_name__	:	Any			     =  sobel_grad[row - 1, col]
  				__magic_name__	:	Union[str, Any]			     =  sobel_grad[row + 1, col]
  				if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
  					__magic_name__	:	Union[str, Any]			     =  sobel_grad[row, col]
  
  			elif (
  			    5 * PI / 8 <= direction < 7 * PI / 8
  			    or 13 * PI / 8 <= direction < 15 * PI / 8
  			):
  				__magic_name__	:	Dict			     =  sobel_grad[row - 1, col - 1]
  				__magic_name__	:	List[Any]			     =  sobel_grad[row + 1, col + 1]
  				if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
  					__magic_name__	:	Dict			     =  sobel_grad[row, col]
  
  	return destination
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						Union[str, Any]   ,     _snake_case   :						Tuple   ,     _snake_case   :						Optional[int]   ,     _snake_case   :						List[str]   ,     _snake_case   :						Any   ,     _snake_case   :						int     )       ->	Optional[int]:
  
  
  
  
  
  
  
  	'''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:
  				__magic_name__	:	Union[str, Any]			     =  strong
  			elif destination[row, col] <= threshold_low:
  				__magic_name__	:	List[Any]			     =  0
  			else:
  				__magic_name__	:	List[str]			     =  weak
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						str   ,     _snake_case   :						Optional[int]   ,     _snake_case   :						Optional[Any]   ,     _snake_case   :						Union[str, Any]     )       ->	List[Any]:
  
  
  
  
  
  
  
  	'''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],
  				):
  					__magic_name__	:	Union[str, Any]			     =  strong
  				else:
  					__magic_name__	:	Optional[int]			     =  0
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						Optional[Any]   ,     _snake_case   :						int=15   ,     _snake_case   :						Union[str, Any]=30   ,     _snake_case   :						List[str]=128   ,     _snake_case   :						Union[str, Any]=255     )       ->	List[Any]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	Tuple			     =  img_convolve(__snake_case   ,     gen_gaussian_kernel(9   ,     sigma=1.4     )     )
  	# get the gradient and degree by sobel_filter
  	__magic_name__     ,					__magic_name__	:	List[str]			     =  sobel_filter(__snake_case     )
  	__magic_name__	:	Dict			     =  PI + np.radadeg(__snake_case     )
  
  	__magic_name__	:	Any			     =  suppress_non_maximum(image.shape   ,     __snake_case   ,     __snake_case     )
  
  	detect_high_low_threshold(
  	    image.shape   ,     __snake_case   ,     __snake_case   ,     __snake_case   ,     __snake_case   ,     __snake_case     )
  
  	track_edge(image.shape   ,     __snake_case   ,     __snake_case   ,     __snake_case     )
  
  	return destination
  
  
  if __name__ == "__main__":
  				# read original image in gray mode
  				snake_case				:	Union[str, Any]							   =						cva.imread(R"../image_data/lena.jpg", 0)
  				# canny edge detection
  				snake_case				:	Dict							   =						canny(lena)
  				cva.imshow("canny", canny_destination)
  				cva.waitKey(0)
  
style_context: 
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils import AddedToken
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
  				from .tokenization_xlm_roberta import XLMRobertaTokenizer
  else:
  				snake_case				:	Any							   =						None
  
  
  snake_case				:	Union[str, Any]							   =						logging.get_logger(__name__)
  
  snake_case				:	Any							   =						{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
  
  snake_case				:	Optional[Any]							   =						{
      "vocab_file": {
          "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
          "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
          "xlm-roberta-large-finetuned-conll02-dutch": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
          ),
          "xlm-roberta-large-finetuned-conll02-spanish": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
          ),
          "xlm-roberta-large-finetuned-conll03-english": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
          ),
          "xlm-roberta-large-finetuned-conll03-german": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
          ),
      },
      "tokenizer_file": {
          "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json",
          "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json",
          "xlm-roberta-large-finetuned-conll02-dutch": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"
          ),
          "xlm-roberta-large-finetuned-conll02-spanish": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"
          ),
          "xlm-roberta-large-finetuned-conll03-english": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"
          ),
          "xlm-roberta-large-finetuned-conll03-german": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"
          ),
      },
  }
  
  snake_case				:	Optional[int]							   =						{
      "xlm-roberta-base": 512,
      "xlm-roberta-large": 512,
      "xlm-roberta-large-finetuned-conll02-dutch": 512,
      "xlm-roberta-large-finetuned-conll02-spanish": 512,
      "xlm-roberta-large-finetuned-conll03-english": 512,
      "xlm-roberta-large-finetuned-conll03-german": 512,
  }
  
  
  
  
  
  class    _snake_case      (  snake_case       ):
  			UpperCamelCase__					   =					VOCAB_FILES_NAMES
  			UpperCamelCase__					   =					PRETRAINED_VOCAB_FILES_MAP
  			UpperCamelCase__					   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  			UpperCamelCase__					   =					['input_ids', 'attention_mask']
  			UpperCamelCase__					   =					XLMRobertaTokenizer
  
  
  
  
  
  
  
  			def __init__(       self		,     _a=None		,     _a=None		,     _a="<s>"		,     _a="</s>"		,     _a="</s>"		,     _a="<s>"		,     _a="<unk>"		,     _a="<pad>"		,     _a="<mask>"		,     **_a		,     ):
  				# Mask token behave like a normal word, i.e. include the space before it
  				__magic_name__	:	List[str]			     =  AddedToken(_a		,     lstrip=_a		,     rstrip=_a	) if isinstance(_a		,     _a	) else mask_token
  
  				super().__init__(
  				    _a		,     tokenizer_file=_a		,     bos_token=_a		,     eos_token=_a		,     sep_token=_a		,     cls_token=_a		,     unk_token=_a		,     pad_token=_a		,     mask_token=_a		,     **_a		,     )
  
  				__magic_name__	:	Optional[int]			     =  vocab_file
  				__magic_name__	:	List[str]			     =  False if not self.vocab_file else True
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
  
  				if token_ids_a is None:
  					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  				__magic_name__	:	Union[str, Any]			     =  [self.cls_token_id]
  				__magic_name__	:	str			     =  [self.sep_token_id]
  				return cls + token_ids_a + sep + sep + token_ids_a + sep
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
  				__magic_name__	:	List[str]			     =  [self.sep_token_id]
  				__magic_name__	:	List[str]			     =  [self.cls_token_id]
  
  				if token_ids_a is None:
  					return len(cls + token_ids_a + sep	) * [0]
  				return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
  				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(_a	):
  					logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''	)
  					return
  				__magic_name__	:	Any			     =  os.path.join(
  				    _a		,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
  
  				if os.path.abspath(self.vocab_file	) != os.path.abspath(_a	):
  					copyfile(self.vocab_file		,     _a	)
  
  				return (out_vocab_file,)
  