code: 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   						=						{
      name: (getattr(transformers, name), getattr(transformers, name + """Fast""")) for name in SLOW_TO_FAST_CONVERTERS
  }
  
  _SCREAMING_SNAKE_CASE   						=						datasets.load_dataset("""xnli""", split="""test+validation""")
  
  _SCREAMING_SNAKE_CASE   						=						0
  _SCREAMING_SNAKE_CASE   						=						0
  _SCREAMING_SNAKE_CASE   						=						0
  _SCREAMING_SNAKE_CASE   						=						0
  def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)				->Union[str, Any]:
  
    '''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
    a__     		=     slow.encode(slow.decode(lowerCamelCase__					)					)
    a__     		=     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 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)				->Optional[Any]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     fast.encode_plus(lowerCamelCase__					)[0]
    a__     		=     enc.offsets
    a__		,a__     		=     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 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)				->Union[str, Any]:
  
    '''simple docstring'''
  
  
  
  
    for i, (spm_id, tok_id) in enumerate(zip(lowerCamelCase__ ,				lowerCamelCase__					)					):
      if spm_id != tok_id:
        break
    a__     		=     i
    for i, (spm_id, tok_id) in enumerate(zip(reversed(lowerCamelCase__					) ,				reversed(lowerCamelCase__					)					)					):
      if spm_id != tok_id:
        break
    a__     		=     len(lowerCamelCase__					) - i
  
    a__     		=     spm_ids[first:last]
    a__     		=     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
      a__     		=     Counter(spm_ids[first:last]					)
      a__     		=     Counter(tok_ids[first:last]					)
  
      a__     		=     {spm_ for (spm_, si) in spms.items() if toks.get(spm_ ,				0					) == si}
      a__     		=     3
      for i in range(last - first - min_width					):
        if all(spm_ids[first + i + j] in removable_tokens for j in range(lowerCamelCase__					)					):
          a__     		=     [
              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
  
    a__     		=     fast.decode(spm_ids[:first]					)
    a__     		=     fast.decode(spm_ids[last:]					)
    a__     		=     fast.decode(spm_ids[first:last]					)
    print()
    print(lowerCamelCase__					)
    return False
  def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)				->int:
  
    '''simple docstring'''
  
  
  
  
    global perfect
    global imperfect
    global wrong
    global total
  
    a__     		=     slow.encode(lowerCamelCase__					)
    a__     		=     fast.encode(lowerCamelCase__					)
  
    a__     		=     False
    total += 1
  
    if slow_ids != fast_ids:
      if check_details(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					):
        a__     		=     True
        imperfect += 1
      else:
        wrong += 1
    else:
      perfect += 1
  
    if total % 1_00_00 == 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 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__					)				->Any:
  
    '''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   						=						list(slow_class.max_model_input_sizes.keys())
            for checkpoint in checkpoint_names:
                 _SCREAMING_SNAKE_CASE   						=						0
                 _SCREAMING_SNAKE_CASE   						=						0
                 _SCREAMING_SNAKE_CASE   						=						0
                 _SCREAMING_SNAKE_CASE   						=						0
  
                 print(F"""========================== Checking {name}: {checkpoint} ==========================""")
                 _SCREAMING_SNAKE_CASE   						=						slow_class.from_pretrained(checkpoint, force_download=True)
                 _SCREAMING_SNAKE_CASE   						=						fast_class.from_pretrained(checkpoint, force_download=True)
                 test_tokenizer(slow, fast)
                 print(F"""Accuracy {perfect * 100 / total:.2f}""")
  
  
style_context: import random
  
  
  
  
  
  
  
  class 					UpperCAmelCase_				:
  
  
  
  
      def __init__(					self  :   Optional[int]      ,      lowerCAmelCase  :   list[list[float]]      ,      lowerCAmelCase  :   list[int]      ,      lowerCAmelCase  :   float = 0.01      ,      lowerCAmelCase  :   int = 1000      ,      lowerCAmelCase  :   float = -1      ,      )	-> None:
        a__     		=     sample
        if len(self.sample   ) == 0:
          raise ValueError("Sample data can not be empty"   )
        a__     		=     target
        if len(self.target   ) == 0:
          raise ValueError("Target data can not be empty"   )
        if len(self.sample   ) != len(self.target   ):
          raise ValueError("Sample data and Target data do not have matching lengths"   )
        a__     		=     learning_rate
        a__     		=     epoch_number
        a__     		=     bias
        a__     		=     len(lowerCAmelCase   )
        a__     		=     len(sample[0]   )  # number of columns in dataset
        a__     		=     []
  
  
  
  
      def 						__lowerCamelCase (					self  :   Optional[Any]   )	-> None:
        for sample in self.sample:
          sample.insert(0      ,      self.bias   )
  
        for _ in range(self.col_sample   ):
          self.weight.append(random.random()   )
  
        self.weight.insert(0      ,      self.bias   )
  
        a__     		=     0
  
        while True:
          a__     		=     False
          for i in range(self.number_sample   ):
            a__     		=     0
            for j in range(self.col_sample + 1   ):
              a__     		=     u + self.weight[j] * self.sample[i][j]
            a__     		=     self.sign(lowerCAmelCase   )
            if y != self.target[i]:
              for j in range(self.col_sample + 1   ):
                a__     		=     (
                    self.weight[j]
                    + self.learning_rate
                    * (self.target[i] - y)
                    * self.sample[i][j]
                )
              a__     		=     True
              # print('Epoch: \n',epoch_count)
          a__     		=     epoch_count + 1
          # if you want control the epoch or just by error
          if not has_misclassified:
            print(("\nEpoch:\n", epoch_count)   )
            print("------------------------\n"   )
            # if epoch_count > self.epoch_number or not error:
            break
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[str]      ,      lowerCAmelCase  :   list[float]   )	-> None:
        if len(self.sample   ) == 0:
          raise ValueError("Sample data can not be empty"   )
        sample.insert(0      ,      self.bias   )
        a__     		=     0
        for i in range(self.col_sample + 1   ):
          a__     		=     u + self.weight[i] * sample[i]
  
        a__     		=     self.sign(lowerCAmelCase   )
  
        if y == -1:
          print(("Sample: ", sample)   )
          print("classification: P1"   )
        else:
          print(("Sample: ", sample)   )
          print("classification: P2"   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   int      ,      lowerCAmelCase  :   float   )	-> int:
        return 1 if u >= 0 else -1
  
  _SCREAMING_SNAKE_CASE   						=    [
      [-0.6508, 0.1097, 4.0009],
      [-1.4492, 0.8896, 4.4005],
      [2.0850, 0.6876, 12.0710],
      [0.2626, 1.1476, 7.7985],
      [0.6418, 1.0234, 7.0427],
      [0.2569, 0.6730, 8.3265],
      [1.1155, 0.6043, 7.4446],
      [0.0914, 0.3399, 7.0677],
      [0.0121, 0.5256, 4.6316],
      [-0.0429, 0.4660, 5.4323],
      [0.4340, 0.6870, 8.2287],
      [0.2735, 1.0287, 7.1934],
      [0.4839, 0.4851, 7.4850],
      [0.4089, -0.1267, 5.5019],
      [1.4391, 0.1614, 8.5843],
      [-0.9115, -0.1973, 2.1962],
      [0.3654, 1.0475, 7.4858],
      [0.2144, 0.7515, 7.1699],
      [0.2013, 1.0014, 6.5489],
      [0.6483, 0.2183, 5.8991],
      [-0.1147, 0.2242, 7.2435],
      [-0.7970, 0.8795, 3.8762],
      [-1.0625, 0.6366, 2.4707],
      [0.5307, 0.1285, 5.6883],
      [-1.2200, 0.7777, 1.7252],
      [0.3957, 0.1076, 5.6623],
      [-0.1013, 0.5989, 7.1812],
      [2.4482, 0.9455, 11.2095],
      [2.0149, 0.6192, 10.9263],
      [0.2012, 0.2611, 5.4631],
  ]
  
  _SCREAMING_SNAKE_CASE   						=    [
      -1,
      -1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      -1,
      -1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      -1,
      1,
      -1,
      1,
  ]
  
  
  if __name__ == "__main__":
       import doctest
  
       doctest.testmod()
  
       _SCREAMING_SNAKE_CASE   						=    Perceptron(
           sample=samples, target=target, learning_rate=0.01, epoch_number=1000, bias=-1
       )
       network.training()
       print("""Finished training perceptron""")
       print("""Enter values to predict or q to exit""")
       while True:
            _SCREAMING_SNAKE_CASE   						=    []
            for i in range(len(samples[0])):
                 _SCREAMING_SNAKE_CASE   						=    input("""value: """).strip()
                 if user_input == "q":
                      break
                 _SCREAMING_SNAKE_CASE   						=    float(user_input)
                 sample.insert(i, observation)
            network.sort(sample)
  
  