import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_layoutlm import LayoutLMTokenizer


lowerCamelCase__   = logging.get_logger(__name__)

lowerCamelCase__   = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase__   = {
    '''vocab_file''': {
        '''microsoft/layoutlm-base-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/vocab.txt'''
        ),
        '''microsoft/layoutlm-large-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''microsoft/layoutlm-base-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/tokenizer.json'''
        ),
        '''microsoft/layoutlm-large-uncased''': (
            '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

lowerCamelCase__   = {
    '''microsoft/layoutlm-base-uncased''': 512,
    '''microsoft/layoutlm-large-uncased''': 512,
}

lowerCamelCase__   = {
    '''microsoft/layoutlm-base-uncased''': {'''do_lower_case''': True},
    '''microsoft/layoutlm-large-uncased''': {'''do_lower_case''': True},
}




class  _UpperCAmelCase       (			lowerCAmelCase   ):



 '''simple docstring'''




 __A           = VOCAB_FILES_NAMES
 __A           = PRETRAINED_VOCAB_FILES_MAP
 __A           = PRETRAINED_INIT_CONFIGURATION
 __A           = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __A           = LayoutLMTokenizer







 def __init__(						self							:					Optional[int]				,      lowercase_							:					int=None				,      lowercase_							:					int=None				,      lowercase_							:					Any=True				,      lowercase_							:					Optional[int]="[UNK]"				,      lowercase_							:					Optional[Any]="[SEP]"				,      lowercase_							:					Any="[PAD]"				,      lowercase_							:					List[str]="[CLS]"				,      lowercase_							:					Optional[Any]="[MASK]"				,      lowercase_							:					Dict=True				,      lowercase_							:					Any=None				,      **lowercase_							:					Union[str, Any]				,      )		->     List[Any]:


   """simple docstring"""







   super().__init__(
       lowercase_				,      tokenizer_file=lowercase_				,      do_lower_case=lowercase_				,      unk_token=lowercase_				,      sep_token=lowercase_				,      pad_token=lowercase_				,      cls_token=lowercase_				,      mask_token=lowercase_				,      tokenize_chinese_chars=lowercase_				,      strip_accents=lowercase_				,      **lowercase_				,      )

   _UpperCamelCase				=  json.loads(self.backend_tokenizer.normalizer.__getstate__())
   if (
       normalizer_state.get("lowercase"				,      lowercase_) != do_lower_case
       or normalizer_state.get("strip_accents"				,      lowercase_) != strip_accents
       or normalizer_state.get("handle_chinese_chars"				,      lowercase_) != tokenize_chinese_chars
   ):
     _UpperCamelCase				=  getattr(lowercase_				,      normalizer_state.pop("type"))
     _UpperCamelCase				=  do_lower_case
     _UpperCamelCase				=  strip_accents
     _UpperCamelCase				=  tokenize_chinese_chars
     _UpperCamelCase				=  normalizer_class(**lowercase_)

   _UpperCamelCase				=  do_lower_case







 def 						__UpperCAmelCase							(						self							:					Union[str, Any]				,      lowercase_							:					int				,      lowercase_							:					Tuple=None)		->     Optional[int]:


   """simple docstring"""







   _UpperCamelCase				=  [self.cls_token_id] + token_ids_a + [self.sep_token_id]

   if token_ids_a:
     output += token_ids_a + [self.sep_token_id]

   return output







 def 						__UpperCAmelCase							(						self							:					List[Any]				,      lowercase_							:					List[int]				,      lowercase_							:					Optional[List[int]] = None)		->     List[int]:


   """simple docstring"""







   _UpperCamelCase				=  [self.sep_token_id]
   _UpperCamelCase				=  [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) * [0] + len(token_ids_a + sep) * [1]







 def 						__UpperCAmelCase							(						self							:					Optional[Any]				,      lowercase_							:					str				,      lowercase_							:					Optional[str] = None)		->     Tuple[str]:


   """simple docstring"""







   _UpperCamelCase				=  self._tokenizer.model.save(lowercase_				,      name=lowercase_)
   return tuple(lowercase_)





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__   = 180


def 							lowerCAmelCase__    (       a__  ,			a__      )	->List[str]:




  '''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(a__      ) + np.square(a__      )) / (2 * np.square(a__      ))      )
  )
  return g


def 							lowerCAmelCase__    (       a__  ,			a__  ,			a__      )	->Optional[int]:




  '''simple docstring'''

  _UpperCamelCase				=  np.zeros(a__      )

  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__    (       a__  ,			a__  ,			a__  ,			a__  ,			a__  ,			a__      )	->Union[str, Any]:




  '''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__    (       a__  ,			a__  ,			a__  ,			a__      )	->str:




  '''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__    (       a__  ,			a__=15  ,			a__=30  ,			a__=128  ,			a__=255      )	->str:




  '''simple docstring'''

  _UpperCamelCase				=  img_convolve(a__  ,			gen_gaussian_kernel(9  ,			sigma=1.4      )      )
  # get the gradient and degree by sobel_filter
  _UpperCamelCase	,		_UpperCamelCase				=  sobel_filter(a__      )
  _UpperCamelCase				=  PI + np.radadeg(a__      )

  _UpperCamelCase				=  suppress_non_maximum(image.shape  ,			a__  ,			a__      )

  detect_high_low_threshold(
      image.shape  ,			a__  ,			a__  ,			a__  ,			a__  ,			a__      )

  track_edge(image.shape  ,			a__  ,			a__  ,			a__      )

  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)




