





'''simple docstring'''




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_camembert import CamembertTokenizer
else:
  lowercase__       : Optional[int]         =       None


lowercase__       : List[str]         =       logging.get_logger(__name__)

lowercase__       : Optional[Any]         =       {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase__       : List[str]         =       {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
    },
}

lowercase__       : Dict         =       {
    '''camembert-base''': 5_12,
}

lowercase__       : str         =       '''▁'''


class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			VOCAB_FILES_NAMES
     lowerCAmelCase						     =			PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase						     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase						     =			['''input_ids''', '''attention_mask''']
     lowerCAmelCase						     =			CamembertTokenizer


     def __init__(		self		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      _UpperCAmelCase="<s>"		,      _UpperCAmelCase="</s>"		,      _UpperCAmelCase="</s>"		,      _UpperCAmelCase="<s>"		,      _UpperCAmelCase="<unk>"		,      _UpperCAmelCase="<pad>"		,      _UpperCAmelCase="<mask>"		,      _UpperCAmelCase=["<s>NOTUSED", "</s>NOTUSED"]		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          __A		:					int						=			AddedToken(_UpperCAmelCase		,      lstrip=_UpperCAmelCase		,      rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase		,      _UpperCAmelCase) else mask_token

          super().__init__(
              _UpperCAmelCase		,      tokenizer_file=_UpperCAmelCase		,      bos_token=_UpperCAmelCase		,      eos_token=_UpperCAmelCase		,      sep_token=_UpperCAmelCase		,      cls_token=_UpperCAmelCase		,      unk_token=_UpperCAmelCase		,      pad_token=_UpperCAmelCase		,      mask_token=_UpperCAmelCase		,      additional_special_tokens=_UpperCAmelCase		,      **_UpperCAmelCase		,      )

          __A		:					List[str]						=			vocab_file
          __A		:					Optional[int]						=			False if not self.vocab_file else True


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None):

          '''simple docstring'''






          if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          __A		:					Optional[Any]						=			[self.cls_token_id]
          __A		:					Optional[int]						=			[self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None):

          '''simple docstring'''






          __A		:					Optional[int]						=			[self.sep_token_id]
          __A		:					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		,      _UpperCAmelCase		,      _UpperCAmelCase = None):

          '''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
          __A		:					List[Any]						=			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,)


'''simple docstring'''



import math
import sys

import cva
import numpy as np







def   a__			(    a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = math.sqrt(a__		)
     __SCREAMING_SNAKE_CASE      = 1 / (sigma * math.sqrt(2 * math.pi		))
     return cons * np.exp(-((img / sigma) ** 2) * 0.5		)







def   a__			(    a__ ,       a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = kernel_size // 2
     return img[x - half : x + half + 1, y - half : y + half + 1]







def   a__			(    a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = np.zeros((kernel_size, kernel_size)		)
     for i in range(0 ,       a__		):
          for j in range(0 ,       a__		):
               __SCREAMING_SNAKE_CASE      = math.sqrt(
                   abs(i - kernel_size // 2		) ** 2 + abs(j - kernel_size // 2		) ** 2		)
     return vec_gaussian(a__ ,       a__		)







def   a__			(    a__ ,       a__ ,       a__ ,       a__ ,       ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = np.zeros(img.shape		)
     __SCREAMING_SNAKE_CASE      = get_gauss_kernel(a__ ,       a__		)
     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = img.shape
     for i in range(kernel_size // 2 ,       size_x - kernel_size // 2		):
          for j in range(kernel_size // 2 ,       size_y - kernel_size // 2		):
               __SCREAMING_SNAKE_CASE      = get_slice(a__ ,       a__ ,       a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = img_s - img_s[kernel_size // 2, kernel_size // 2]
               __SCREAMING_SNAKE_CASE      = vec_gaussian(a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = np.multiply(a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = np.multiply(a__ ,       a__		)
               __SCREAMING_SNAKE_CASE      = np.sum(a__		) / np.sum(a__		)
               __SCREAMING_SNAKE_CASE      = val
     return imga







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = args[1] if args[1:] else """../image_data/lena.jpg"""
     __SCREAMING_SNAKE_CASE      = float(args[2]		) if args[2:] else 1.0
     __SCREAMING_SNAKE_CASE      = float(args[3]		) if args[3:] else 1.0
     if args[4:]:
          __SCREAMING_SNAKE_CASE      = int(args[4]		)
          __SCREAMING_SNAKE_CASE      = kernel_size + abs(kernel_size % 2 - 1		)
     else:
          __SCREAMING_SNAKE_CASE      = 5
     return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
  UpperCAmelCase  ,	UpperCAmelCase  ,	UpperCAmelCase  ,	UpperCAmelCase					:			Any       =	parse_args(sys.argv)
  UpperCAmelCase					:			Union[str, Any]       =	cva.imread(filename, 0)
  cva.imshow('input image', img)

  UpperCAmelCase					:			Optional[int]       =	img / 2_5_5
  UpperCAmelCase					:			Optional[Any]       =	out.astype('float32')
  UpperCAmelCase					:			Tuple       =	bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
  UpperCAmelCase					:			List[Any]       =	out * 2_5_5
  UpperCAmelCase					:			Any       =	np.uinta(out)
  cva.imshow('output image', out)
  cva.waitKey(0)
  cva.destroyAllWindows()






