code: 
  
  
  
  import json
  import os
  from functools import lru_cache
  from typing import TYPE_CHECKING, List, Optional, Tuple, Union
  
  import numpy as np
  import regex as re
  
  from ...utils import is_tf_available, is_torch_available, logging
  
  
  if TYPE_CHECKING:
      if is_torch_available():
          import torch
      if is_tf_available():
          import tensorflow as tf
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  
  
  __lowerCamelCase  				=					logging.get_logger(__name__)
  
  __lowerCamelCase  				=					{
      """vocab_file""": """vocab.json""",
      """merges_file""": """merges.txt""",
  }
  
  __lowerCamelCase  				=					{
      """vocab_file""": {
          """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json""",
      },
      """merges_file""": {
          """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt""",
      },
  }
  
  __lowerCamelCase  				=					{
      """Salesforce/codegen-350M-mono""": 2_0_4_8,
  }
  
  
  
  
  @lru_cache()
  def     UpperCAmelCase__ (							)   -> Dict:
    _A           =    (
        list(range(ord('''!'''							)						,				ord('''~'''							) + 1							)							) + list(range(ord('''¡'''							)						,				ord('''¬'''							) + 1							)							) + list(range(ord('''®'''							)						,				ord('''ÿ'''							) + 1							)							)
    )
    _A           =    bs[:]
    _A           =    0
    for b in range(2**8							):
      if b not in bs:
        bs.append(__snake_case							)
        cs.append(2**8 + n							)
        n += 1
    _A           =    [chr(__snake_case							) for n in cs]
    return dict(zip(__snake_case						,				__snake_case							)							)
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case							)   -> List[str]:
    _A           =    set()
    _A           =    word[0]
    for char in word[1:]:
      pairs.add((prev_char, char)							)
      _A           =    char
    return pairs
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			VOCAB_FILES_NAMES
   lowerCamelCase_          =			PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_          =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_          =			['''input_ids''', '''attention_mask''']
   def __init__(						self	,    a	,    a	,    a="replace"	,    a="<|endoftext|>"	,    a="<|endoftext|>"	,    a="<|endoftext|>"	,    a=None	,    a=False	,    a=False	,    **a	,    )	->							Tuple:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    AddedToken(a	,    lstrip=a	,    rstrip=a					) if isinstance(a	,    a					) else bos_token
     _A           =    AddedToken(a	,    lstrip=a	,    rstrip=a					) if isinstance(a	,    a					) else eos_token
     _A           =    AddedToken(a	,    lstrip=a	,    rstrip=a					) if isinstance(a	,    a					) else unk_token
     _A           =    AddedToken(a	,    lstrip=a	,    rstrip=a					) if isinstance(a	,    a					) else pad_token
     super().__init__(
         errors=a	,    unk_token=a	,    bos_token=a	,    eos_token=a	,    pad_token=a	,    add_prefix_space=a	,    add_bos_token=a	,    **a	,    )
     _A           =    add_bos_token
  
     with open(a	,    encoding='''utf-8'''					) as vocab_handle:
       _A           =    json.load(a					)
     _A           =    {v: k for k, v in self.encoder.items()}
     _A           =    errors  # how to handle errors in decoding
     _A           =    bytes_to_unicode()
     _A           =    {v: k for k, v in self.byte_encoder.items()}
     with open(a	,    encoding='''utf-8'''					) as merges_handle:
       _A           =    merges_handle.read().split('''\n'''					)[1:-1]
     _A           =    [tuple(merge.split()					) for merge in bpe_merges]
     _A           =    dict(zip(a	,    range(len(a					)					)					)					)
     _A           =    {}
     _A           =    add_prefix_space
  
     # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
     _A           =    re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''					)
   @property
   def lowercase_		(						self					)	->							Optional[int]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return len(self.encoder					)
   def lowercase_		(						self					)	->							Optional[int]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return dict(self.encoder	,    **self.added_tokens_encoder					)
   def lowercase_		(						self	,    a					)	->							int:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if token in self.cache:
       return self.cache[token]
     _A           =    tuple(a					)
     _A           =    get_pairs(a					)
  
     if not pairs:
       return token
  
     while True:
       _A           =    min(a	,    key=lambda a					: self.bpe_ranks.get(a	,    float('''inf'''					)					)					)
       if bigram not in self.bpe_ranks:
         break
       _A    ,   _A           =    bigram
       _A           =    []
       _A           =    0
       while i < len(a					):
         try:
           _A           =    word.index(a	,    a					)
         except ValueError:
           new_word.extend(word[i:]					)
           break
         else:
           new_word.extend(word[i:j]					)
           _A           =    j
  
         if word[i] == first and i < len(a					) - 1 and word[i + 1] == second:
           new_word.append(first + second					)
           i += 2
         else:
           new_word.append(word[i]					)
           i += 1
       _A           =    tuple(a					)
       _A           =    new_word
       if len(a					) == 1:
         break
       else:
         _A           =    get_pairs(a					)
     _A           =    ''' '''.join(a					)
     _A           =    word
     return word
   def lowercase_		(						self	,    a	,    a=None					)	->							List[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if self.add_bos_token:
       _A           =    [self.bos_token_id]
     else:
       _A           =    []
  
     _A           =    bos_token_ids + token_ids_a
  
     if token_ids_a is None:
       return output
  
     return output + bos_token_ids + token_ids_a
   def lowercase_		(						self	,    a					)	->							List[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    []
     for token in re.findall(self.pat	,    a					):
       _A           =    ''''''.join(
           self.byte_encoder[b] for b in token.encode('''utf-8'''					)					)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
       bpe_tokens.extend(bpe_token for bpe_token in self.bpe(a					).split(''' '''					)					)
     return bpe_tokens
   def lowercase_		(						self	,    a					)	->							Union[str, Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return self.encoder.get(a	,    self.encoder.get(self.unk_token					)					)
   def lowercase_		(						self	,    a					)	->							Any:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return self.decoder.get(a					)
   def lowercase_		(						self	,    a					)	->							Optional[int]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    ''''''.join(a					)
     _A           =    bytearray([self.byte_decoder[c] for c in text]					).decode('''utf-8'''	,    errors=self.errors					)
     return text
   def lowercase_		(						self	,    a	,    a = None					)	->							Tuple[str]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if not os.path.isdir(a					):
       logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
       return
     _A           =    os.path.join(
         a	,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)
     _A           =    os.path.join(
         a	,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']					)
  
     with open(a	,    '''w'''	,    encoding='''utf-8'''					) as f:
       f.write(json.dumps(self.encoder	,    indent=2	,    sort_keys=a	,    ensure_ascii=a					) + '''\n'''					)
  
     _A           =    0
     with open(a	,    '''w'''	,    encoding='''utf-8'''					) as writer:
       writer.write('''#version: 0.2\n'''					)
       for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	,    key=lambda a					: kv[1]					):
         if index != token_index:
           logger.warning(
               f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
               ''' Please check that the tokenizer is not corrupted!'''					)
           _A           =    token_index
         writer.write(''' '''.join(a					) + '''\n'''					)
         index += 1
  
     return vocab_file, merge_file
   def lowercase_		(						self	,    a	,    a=False	,    **a					)	->							Tuple:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    kwargs.pop('''add_prefix_space'''	,    self.add_prefix_space					)
     if is_split_into_words or add_prefix_space:
       _A           =    ''' ''' + text
     return (text, kwargs)
   def lowercase_		(						self	,    a	,    a = False	,    a = None	,    a = None	,    **a	,    )	->							str:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    super()._decode(
         token_ids=a	,    skip_special_tokens=a	,    clean_up_tokenization_spaces=a	,    **a	,    )
  
     if truncate_before_pattern is not None and len(a					) > 0:
       _A           =    self.truncate(a	,    a					)
  
     return decoded_text
  
  
  
   def lowercase_		(						self	,    a	,    a					)	->							Optional[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     def find_re(a	,    a	,    a					):
       _A           =    pattern.search(a	,    a					)
       return m.start() if m else -1
  
     _A           =    [re.compile(a	,    re.MULTILINE					) for pattern in truncate_before_pattern]
  
     _A           =    list(re.finditer('''^print'''	,    a	,    re.MULTILINE					)					)
  
     if len(a					) > 1:
       _A           =    completion[: prints[1].start()]
  
     _A           =    list(re.finditer('''^def'''	,    a	,    re.MULTILINE					)					)
  
     if len(a					) > 1:
       _A           =    completion[: defs[1].start()]
  
     _A           =    0
  
     _A           =    [
         pos for pos in [find_re(a	,    a	,    a					) for terminal in terminals] if pos != -1
     ]
  
     if len(a					) > 0:
       return completion[: min(a					)]
     else:
       return completion
style_context: 
  
  
  
  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  				=					1_8_0
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case							)   -> Dict:
    _A           =    k_size // 2
    _A    ,   _A           =    np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
    _A           =    (
        1
        / (2 * np.pi * sigma)
        * np.exp(-(np.square(__snake_case							) + np.square(__snake_case							)) / (2 * np.square(__snake_case							))							)
    )
    return g
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case							)   -> int:
    _A           =    np.zeros(__snake_case							)
  
    for row in range(1						,				image_shape[0] - 1							):
      for col in range(1						,				image_shape[1] - 1							):
        _A           =    gradient_direction[row, col]
  
        if (
            0 <= direction < PI / 8
            or 15 * PI / 8 <= direction <= 2 * PI
            or 7 * PI / 8 <= direction <= 9 * PI / 8
        ):
          _A           =    sobel_grad[row, col - 1]
          _A           =    sobel_grad[row, col + 1]
          if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
            _A           =    sobel_grad[row, col]
  
        elif (
            PI / 8 <= direction < 3 * PI / 8
            or 9 * PI / 8 <= direction < 11 * PI / 8
        ):
          _A           =    sobel_grad[row + 1, col - 1]
          _A           =    sobel_grad[row - 1, col + 1]
          if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
            _A           =    sobel_grad[row, col]
  
        elif (
            3 * PI / 8 <= direction < 5 * PI / 8
            or 11 * PI / 8 <= direction < 13 * PI / 8
        ):
          _A           =    sobel_grad[row - 1, col]
          _A           =    sobel_grad[row + 1, col]
          if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
            _A           =    sobel_grad[row, col]
  
        elif (
            5 * PI / 8 <= direction < 7 * PI / 8
            or 13 * PI / 8 <= direction < 15 * PI / 8
        ):
          _A           =    sobel_grad[row - 1, col - 1]
          _A           =    sobel_grad[row + 1, col + 1]
          if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
            _A           =    sobel_grad[row, col]
  
    return destination
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)   -> Optional[int]:
    for row in range(1						,				image_shape[0] - 1							):
      for col in range(1						,				image_shape[1] - 1							):
        if destination[row, col] >= threshold_high:
          _A           =    strong
        elif destination[row, col] <= threshold_low:
          _A           =    0
        else:
          _A           =    weak
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)   -> List[Any]:
    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],
          ):
            _A           =    strong
          else:
            _A           =    0
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case=15						,				__snake_case=30						,				__snake_case=128						,				__snake_case=255							)   -> List[Any]:
    # gaussian_filter
    _A           =    img_convolve(__snake_case						,				gen_gaussian_kernel(9						,				sigma=1.4							)							)
    # get the gradient and degree by sobel_filter
    _A    ,   _A           =    sobel_filter(__snake_case							)
    _A           =    PI + np.radadeg(__snake_case							)
  
    _A           =    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
      __lowerCamelCase  				=					cva.imread(R"""../image_data/lena.jpg""", 0)
      # canny edge detection
      __lowerCamelCase  				=					canny(lena)
      cva.imshow("""canny""", canny_destination)
      cva.waitKey(0)