

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()

UpperCAmelCase__	:       List[str]			      =      {
    name: (getattr(transformers, name), getattr(transformers, name + """Fast""")) for name in SLOW_TO_FAST_CONVERTERS
}

UpperCAmelCase__	:       List[str]			      =      datasets.load_dataset("""xnli""", split="""test+validation""")

UpperCAmelCase__	:       Optional[Any]			      =      0
UpperCAmelCase__	:       str			      =      0
UpperCAmelCase__	:       Dict			      =      0
UpperCAmelCase__	:       Tuple			      =      0






def    __lowercase (       _A  ,      _A  ,      _A  ,      _A     )       ->  int:
						if spm_diff == list(reversed(_A     )     ):
												# AAA -> AA+A vs A+AA case.
												return True
						elif len(_A     ) == len(_A     ) and fast.decode(_A     ) == fast.decode(_A     ):
												# Second order OK
												# Barrich -> Barr + ich vs Bar + rich
												return True
						SCREAMING_SNAKE_CASE	: List[Any]					       =				slow.encode(slow.decode(_A     )     )
						SCREAMING_SNAKE_CASE	: List[Any]					       =				fast.encode(fast.decode(_A     )     )
						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 (       _A  ,      _A  ,      _A     )       ->  Tuple:
						SCREAMING_SNAKE_CASE	: Optional[int]					       =				fast.encode_plus(_A     )[0]
						SCREAMING_SNAKE_CASE	: Optional[int]					       =				enc.offsets
						SCREAMING_SNAKE_CASE	: Optional[int]					       =				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 (       _A  ,      _A  ,      _A  ,      _A  ,      _A     )       ->  str:
						# Encoding can be the same with same result AAA -> A + AA vs AA + A
						# We can check that we use at least exactly the same number of tokens.
						for i, (spm_id, tok_id) in enumerate(zip(_A  ,      _A     )     ):
												if spm_id != tok_id:
																		break
						SCREAMING_SNAKE_CASE	: Tuple					       =				i
						for i, (spm_id, tok_id) in enumerate(zip(reversed(_A     )  ,      reversed(_A     )     )     ):
												if spm_id != tok_id:
																		break
						SCREAMING_SNAKE_CASE	: str					       =				len(_A     ) - i

						SCREAMING_SNAKE_CASE	: List[str]					       =				spm_ids[first:last]
						SCREAMING_SNAKE_CASE	: Tuple					       =				tok_ids[first:last]

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

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

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

												SCREAMING_SNAKE_CASE	: Any					       =				{spm_ for (spm_, si) in spms.items() if toks.get(spm_  ,      0     ) == si}
												SCREAMING_SNAKE_CASE	: int					       =				3
												for i in range(last - first - min_width     ):
																		if all(spm_ids[first + i + j] in removable_tokens for j in range(_A     )     ):
																								SCREAMING_SNAKE_CASE	: Dict					       =				[
																								    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]  ,      _A  ,      _A     ) and check_details(
																														    _A  ,      spm_ids[first + i : last]  ,      tok_ids[first + j : last]  ,      _A  ,      _A  ,      ):
																																				return True

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

						SCREAMING_SNAKE_CASE	: Any					       =				fast.decode(spm_ids[:first]     )
						SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				fast.decode(spm_ids[last:]     )
						SCREAMING_SNAKE_CASE	: Dict					       =				fast.decode(spm_ids[first:last]     )
						print()
						print(_A     )
						return False






def    __lowercase (       _A  ,      _A  ,      _A     )       ->  Tuple:
						global perfect
						global imperfect
						global wrong
						global total

						SCREAMING_SNAKE_CASE	: Optional[int]					       =				slow.encode(_A     )
						SCREAMING_SNAKE_CASE	: Dict					       =				fast.encode(_A     )

						SCREAMING_SNAKE_CASE	: Dict					       =				False
						total += 1

						if slow_ids != fast_ids:
												if check_details(_A  ,      _A  ,      _A  ,      _A  ,      _A     ):
																		SCREAMING_SNAKE_CASE	: Tuple					       =				True
																		imperfect += 1
												else:
																		wrong += 1
						else:
												perfect += 1

						if total % 10000 == 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(_A     )}\n{fast.tokenize(_A     )}"






def    __lowercase (       _A  ,      _A     )       ->  Any:
						global batch_total
						for i in range(len(_A     )     ):
												# premise, all languages
												for text in dataset[i]["premise"].values():
																		test_string(_A  ,      _A  ,      _A     )

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


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

						print(F"""========================== Checking {name}: {checkpoint} ==========================""")
						UpperCAmelCase__	:       Dict			      =      slow_class.from_pretrained(checkpoint, force_download=True)
						UpperCAmelCase__	:       Optional[int]			      =      fast_class.from_pretrained(checkpoint, force_download=True)
						test_tokenizer(slow, fast)
						print(F"""Accuracy {perfect * 100 / total:.2f}""")







from __future__ import annotations

UpperCAmelCase__	:       Optional[int]			      =      [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]







class     a__    :







  """simple docstring"""




  def __init__(     self      :	Optional[Any]			,			UpperCAmelCase__      :	int						)   ->None:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: int					       =				-1
        SCREAMING_SNAKE_CASE	: int					       =				-1
        SCREAMING_SNAKE_CASE	: list[int]					       =				[-1] * length
        SCREAMING_SNAKE_CASE	: Node | None					       =				None
        SCREAMING_SNAKE_CASE	: Node | None					       =				None




  def __repr__(     self      :	Dict						)   ->str:
        """simple docstring"""







        return f"Node(min_value={self.minn} max_value={self.maxx})"






def    __lowercase (       _A     )       ->  Node | None:
      SCREAMING_SNAKE_CASE	: Tuple					       =				Node(len(_A     )     )
      SCREAMING_SNAKE_CASE       ,		SCREAMING_SNAKE_CASE	: int					       =				min(_A     ), max(_A     )
      # Leaf node case where the node contains only one unique value
      if root.minn == root.maxx:
            return root
      SCREAMING_SNAKE_CASE	: int					       =				(root.minn + root.maxx) // 2

      SCREAMING_SNAKE_CASE	: list[int]					       =				[]
      SCREAMING_SNAKE_CASE	: list[int]					       =				[]

      for index, num in enumerate(_A     ):
            if num <= pivot:
                  left_arr.append(_A     )
            else:
                  right_arr.append(_A     )
            SCREAMING_SNAKE_CASE	: int					       =				len(_A     )
      SCREAMING_SNAKE_CASE	: int					       =				build_tree(_A     )
      SCREAMING_SNAKE_CASE	: Tuple					       =				build_tree(_A     )
      return root






def    __lowercase (       _A  ,      _A  ,      _A     )       ->  int:
      if index < 0 or node is None:
            return 0
      # Leaf node cases
      if node.minn == node.maxx:
            return index + 1 if node.minn == num else 0
      SCREAMING_SNAKE_CASE	: List[str]					       =				(node.minn + node.maxx) // 2
      if num <= pivot:
            # go the left subtree and map index to the left subtree
            return rank_till_index(node.left  ,      _A  ,      node.map_left[index] - 1     )
      else:
            # go to the right subtree and map index to the right subtree
            return rank_till_index(node.right  ,      _A  ,      index - node.map_left[index]     )






def    __lowercase (       _A  ,      _A  ,      _A  ,      _A     )       ->  int:
      if start > end:
            return 0
      SCREAMING_SNAKE_CASE	: Any					       =				rank_till_index(_A  ,      _A  ,      _A     )
      SCREAMING_SNAKE_CASE	: Optional[int]					       =				rank_till_index(_A  ,      _A  ,      start - 1     )
      return rank_till_end - rank_before_start






def    __lowercase (       _A  ,      _A  ,      _A  ,      _A     )       ->  int:
      if index > (end - start) or start > end or node is None:
            return -1
      # Leaf node case
      if node.minn == node.maxx:
            return node.minn
      # Number of elements in the left subtree in interval [start, end]
      SCREAMING_SNAKE_CASE	: Tuple					       =				node.map_left[end] - (
          node.map_left[start - 1] if start else 0
      )
      if num_elements_in_left_tree > index:
            return quantile(
                node.left  ,      _A  ,      (node.map_left[start - 1] if start else 0)  ,      node.map_left[end] - 1  ,      )
      else:
            return quantile(
                node.right  ,      index - num_elements_in_left_tree  ,      start - (node.map_left[start - 1] if start else 0)  ,      end - node.map_left[end]  ,      )






def    __lowercase (       _A  ,      _A  ,      _A  ,      _A  ,      _A     )       ->  int:
      if (
          start > end
          or node is None
          or start_num > end_num
          or node.minn > end_num
          or node.maxx < start_num
      ):
            return 0
      if start_num <= node.minn and node.maxx <= end_num:
            return end - start + 1
      SCREAMING_SNAKE_CASE	: Dict					       =				range_counting(
          node.left  ,      (node.map_left[start - 1] if start else 0)  ,      node.map_left[end] - 1  ,      _A  ,      _A  ,      )
      SCREAMING_SNAKE_CASE	: List[str]					       =				range_counting(
          node.right  ,      start - (node.map_left[start - 1] if start else 0)  ,      end - node.map_left[end]  ,      _A  ,      _A  ,      )
      return left + right


if __name__ == "__main__":
  import doctest

  doctest.testmod()




