


from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

SCREAMING_SNAKE_CASE		:Tuple											=       {
    name: (getattr(transformers, name), getattr(transformers, name + '''Fast''')) for name in SLOW_TO_FAST_CONVERTERS
}

SCREAMING_SNAKE_CASE		:Dict											=       datasets.load_dataset('''xnli''', split='''test+validation''')

SCREAMING_SNAKE_CASE		:int											=       0
SCREAMING_SNAKE_CASE		:Dict											=       0
SCREAMING_SNAKE_CASE		:Any											=       0
SCREAMING_SNAKE_CASE		:Optional[Any]											=       0



def      _lowerCAmelCase  (							lowerCAmelCase_							:Dict						,  lowerCAmelCase_							:List[Any]						,  lowerCAmelCase_							:List[Any]						,  lowerCAmelCase_							:Optional[int]							)->str:
   '''simple docstring'''






   if spm_diff == list(reversed(lowerCAmelCase_							)							):
      # AAA -> AA+A vs A+AA case.
      return True
   elif len(lowerCAmelCase_							) == len(lowerCAmelCase_							) and fast.decode(lowerCAmelCase_							) == fast.decode(lowerCAmelCase_							):
      # Second order OK
      # Barrich -> Barr + ich vs Bar + rich
      return True
   snake_case_         =   slow.encode(slow.decode(lowerCAmelCase_							)							)
   snake_case_         =   fast.encode(fast.decode(lowerCAmelCase_							)							)
   if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
      # Type 3 error.
      # Snehagatha ->
      #       Sne, h, aga, th, a
      #       Sne, ha, gat, ha
      # Encoding the wrong with sp does not even recover what spm gave us
      # It fits tokenizer however...
      return True
   return False



def      _lowerCAmelCase  (							lowerCAmelCase_							:int						,  lowerCAmelCase_							:List[str]						,  lowerCAmelCase_							:Union[str, Any]							)->int:
   '''simple docstring'''






   snake_case_         =   fast.encode_plus(lowerCAmelCase_							)[0]
   snake_case_         =   enc.offsets
   snake_case_      ,      snake_case_         =   offsets[idx], offsets[idx - 1]
   if curr is not None and line[curr[0] : curr[1]] == "\u200f":
      return True
   if prev is not None and line[prev[0] : prev[1]] == "\u200f":
      return True



def      _lowerCAmelCase  (							lowerCAmelCase_							:Tuple						,  lowerCAmelCase_							:Dict						,  lowerCAmelCase_							:List[str]						,  lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:str							)->int:
   '''simple docstring'''






   for i, (spm_id, tok_id) in enumerate(zip(lowerCAmelCase_						,  lowerCAmelCase_							)							):
      if spm_id != tok_id:
         break
   snake_case_         =   i
   for i, (spm_id, tok_id) in enumerate(zip(reversed(lowerCAmelCase_							)						,  reversed(lowerCAmelCase_							)							)							):
      if spm_id != tok_id:
         break
   snake_case_         =   len(lowerCAmelCase_							) - i

   snake_case_         =   spm_ids[first:last]
   snake_case_         =   tok_ids[first:last]

   if check_diff(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_							):
      return True

   if check_LTR_mark(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_							):
      return True

   if last - first > 5:
      # We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
      snake_case_         =   Counter(spm_ids[first:last]							)
      snake_case_         =   Counter(tok_ids[first:last]							)

      snake_case_         =   {spm_ for (spm_, si) in spms.items() if toks.get(spm_						,  0							) == si}
      snake_case_         =   3
      for i in range(last - first - min_width							):
         if all(spm_ids[first + i + j] in removable_tokens for j in range(lowerCAmelCase_							)							):
            snake_case_         =   [
                k
                for k in range(last - first - min_width							)
                if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
            ]
            for j in possible_matches:
               if check_diff(spm_ids[first : first + i]						,  tok_ids[first : first + j]						,  lowerCAmelCase_						,  lowerCAmelCase_							) and check_details(
                   lowerCAmelCase_						,  spm_ids[first + i : last]						,  tok_ids[first + j : last]						,  lowerCAmelCase_						,  lowerCAmelCase_						,  ):
                  return True

   print(F'''Spm: {[fast.decode([spm_ids[i]]							) for i in range(lowerCAmelCase_						,  lowerCAmelCase_							)]}'''							)
   try:
      print(F'''Tok: {[fast.decode([tok_ids[i]]							) for i in range(lowerCAmelCase_						,  lowerCAmelCase_							)]}'''							)
   except Exception:
      pass

   snake_case_         =   fast.decode(spm_ids[:first]							)
   snake_case_         =   fast.decode(spm_ids[last:]							)
   snake_case_         =   fast.decode(spm_ids[first:last]							)
   print()
   print(lowerCAmelCase_							)
   return False



def      _lowerCAmelCase  (							lowerCAmelCase_							:str						,  lowerCAmelCase_							:Optional[Any]						,  lowerCAmelCase_							:Any							)->Any:
   '''simple docstring'''






   global perfect
   global imperfect
   global wrong
   global total

   snake_case_         =   slow.encode(lowerCAmelCase_							)
   snake_case_         =   fast.encode(lowerCAmelCase_							)

   snake_case_         =   False
   total += 1

   if slow_ids != fast_ids:
      if check_details(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_							):
         snake_case_         =   True
         imperfect += 1
      else:
         wrong += 1
   else:
      perfect += 1

   if total % 10_000 == 0:
      print(F'''({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})'''							)

   if skip_assert:
      return

   assert (
       slow_ids == fast_ids
   ), F'''line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(lowerCAmelCase_							)}\n{fast.tokenize(lowerCAmelCase_							)}'''



def      _lowerCAmelCase  (							lowerCAmelCase_							:Tuple						,  lowerCAmelCase_							:List[str]							)->Dict:
   '''simple docstring'''






   global batch_total
   for i in range(len(lowerCAmelCase_							)							):
      # premise, all languages
      for text in dataset[i]["premise"].values():
         test_string(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_							)

      # hypothesis, all languages
      for text in dataset[i]["hypothesis"]["translation"]:
         test_string(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_							)


if __name__ == "__main__":
   for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
      SCREAMING_SNAKE_CASE		:str											=       list(slow_class.max_model_input_sizes.keys())
      for checkpoint in checkpoint_names:
         SCREAMING_SNAKE_CASE		:Union[str, Any]											=       0
         SCREAMING_SNAKE_CASE		:int											=       0
         SCREAMING_SNAKE_CASE		:Any											=       0
         SCREAMING_SNAKE_CASE		:str											=       0

         print(F'''========================== Checking {name}: {checkpoint} ==========================''')
         SCREAMING_SNAKE_CASE		:List[str]											=       slow_class.from_pretrained(checkpoint, force_download=True)
         SCREAMING_SNAKE_CASE		:Union[str, Any]											=       fast_class.from_pretrained(checkpoint, force_download=True)
         test_tokenizer(slow, fast)
         print(F'''Accuracy {perfect * 1_00 / total:.2f}''')








import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey


class       __lowerCAmelCase       :






  """simple docstring"""





  def __init__(       self    :    Optional[Any]      ,			_lowerCAmelCase    :    Union[str, Any]      ,			_lowerCAmelCase    :    int					)				->	Tuple:




     """simple docstring"""
     snake_case_         =   0
     # max greyscale value for #FFFFFF
     snake_case_         =   int(self.get_greyscale(2_5_5      ,			2_5_5      ,			2_5_5					)					)

     if not self.min_threshold < threshold < self.max_threshold:
        snake_case_         =   F'''Factor value should be from 0 to {self.max_threshold}'''
        raise ValueError(_lowerCAmelCase					)

     snake_case_         =   input_img
     snake_case_         =   threshold
     snake_case_      ,      snake_case_         =   self.input_img.shape[1], self.input_img.shape[0]

     # error table size (+4 columns and +1 row) greater than input image because of
     # lack of if statements
     snake_case_         =   [
         [0 for _ in range(self.height + 4					)] for __ in range(self.width + 1					)
     ]
     snake_case_         =   np.ones((self.width, self.height, 3)      ,			np.uinta					) * 2_5_5



  @classmethod
  def    lowerCAmelCase__					(       cls    :    Optional[Any]      ,			_lowerCAmelCase    :    int      ,			_lowerCAmelCase    :    int      ,			_lowerCAmelCase    :    int					)				->	float:




     """simple docstring"""
     return 0.114 * blue + 0.587 * green + 0.2_126 * red



  def    lowerCAmelCase__					(       self    :    Union[str, Any]					)				->	None:




     """simple docstring"""
     for y in range(self.height					):
        for x in range(self.width					):
           snake_case_         =   int(self.get_greyscale(*self.input_img[y][x]					)					)
           if self.threshold > greyscale + self.error_table[y][x]:
              snake_case_         =   (0, 0, 0)
              snake_case_         =   greyscale + self.error_table[x][y]
           else:
              snake_case_         =   (2_5_5, 2_5_5, 2_5_5)
              snake_case_         =   greyscale + self.error_table[x][y] - 2_5_5
           self.error_table[y][x + 1] += int(8 / 3_2 * current_error					)
           self.error_table[y][x + 2] += int(4 / 3_2 * current_error					)
           self.error_table[y + 1][x] += int(8 / 3_2 * current_error					)
           self.error_table[y + 1][x + 1] += int(4 / 3_2 * current_error					)
           self.error_table[y + 1][x + 2] += int(2 / 3_2 * current_error					)
           self.error_table[y + 1][x - 1] += int(4 / 3_2 * current_error					)
           self.error_table[y + 1][x - 2] += int(2 / 3_2 * current_error					)


if __name__ == "__main__":
   # create Burke's instances with original images in greyscale
   SCREAMING_SNAKE_CASE		:Dict											=       [
       Burkes(imread('''image_data/lena.jpg''', 1), threshold)
       for threshold in (1, 1_26, 1_30, 1_40)
   ]

   for burkes in burkes_instances:
      burkes.process()

   for burkes in burkes_instances:
      imshow(
          F'''Original image with dithering threshold: {burkes.threshold}''',
          burkes.output_img,
      )

   waitKey(0)
   destroyAllWindows()




