



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




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)