

"""simple docstring"""




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			      =		{
    name: (getattr(transformers, name), getattr(transformers, name + "Fast")) for name in SLOW_TO_FAST_CONVERTERS
}

_UpperCamelCase			      =		datasets.load_dataset("xnli", split="test+validation")

_UpperCamelCase			      =		0
_UpperCamelCase			      =		0
_UpperCamelCase			      =		0
_UpperCamelCase			      =		0







def 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__						):






      if spm_diff == list(reversed(snake_case__						)						):
            # AAA -> AA+A vs A+AA case.
            return True
      elif len(snake_case__						) == len(snake_case__						) and fast.decode(snake_case__						) == fast.decode(snake_case__						):
            # Second order OK
            # Barrich -> Barr + ich vs Bar + rich
            return True
      __lowerCAmelCase :						List[str] = slow.encode(slow.decode(snake_case__						)						)
      __lowerCAmelCase :						Dict = fast.encode(fast.decode(snake_case__						)						)
      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			(					lowercase__       ,     lowercase__       ,     lowercase__						):
      __lowerCAmelCase :						Optional[Any] = fast.encode_plus(snake_case__						)[0]
      __lowerCAmelCase :						Dict = enc.offsets
      __lowerCAmelCase,							__lowerCAmelCase :						Optional[Any] = 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			(					lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__       ,     lowercase__						):






      for i, (spm_id, tok_id) in enumerate(zip(snake_case__       ,     snake_case__						)						):
            if spm_id != tok_id:
                  break
      __lowerCAmelCase :						str = i
      for i, (spm_id, tok_id) in enumerate(zip(reversed(snake_case__						)       ,     reversed(snake_case__						)						)						):
            if spm_id != tok_id:
                  break
      __lowerCAmelCase :						str = len(snake_case__						) - i

      __lowerCAmelCase :						Dict = spm_ids[first:last]
      __lowerCAmelCase :						Union[str, Any] = tok_ids[first:last]

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

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

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

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

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

      __lowerCAmelCase :						Optional[int] = fast.decode(spm_ids[:first]						)
      __lowerCAmelCase :						Union[str, Any] = fast.decode(spm_ids[last:]						)
      __lowerCAmelCase :						Dict = fast.decode(spm_ids[first:last]						)
      print()
      print(snake_case__						)
      return False







def 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__						):
      global perfect
      global imperfect
      global wrong
      global total

      __lowerCAmelCase :						Any = slow.encode(snake_case__						)
      __lowerCAmelCase :						List[str] = fast.encode(snake_case__						)

      __lowerCAmelCase :						Optional[Any] = False
      total += 1

      if slow_ids != fast_ids:
            if check_details(snake_case__       ,     snake_case__       ,     snake_case__       ,     snake_case__       ,     snake_case__						):
                  __lowerCAmelCase :						str = True
                  imperfect += 1
            else:
                  wrong += 1
      else:
            perfect += 1

      if total % 1_0_0_0_0 == 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(snake_case__						)}\n{fast.tokenize(snake_case__						)}"""







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

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


if __name__ == "__main__":
 for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
  _UpperCamelCase			      =		list(slow_class.max_model_input_sizes.keys())
  for checkpoint in checkpoint_names:
   _UpperCamelCase			      =		0
   _UpperCamelCase			      =		0
   _UpperCamelCase			      =		0
   _UpperCamelCase			      =		0

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





def 		_lowercase			(					lowercase__						):
      return "".join(chr(ord(lowercase__						) + 3_2						) if '''A''' <= char <= '''Z''' else char for char in word						)


if __name__ == "__main__":
 from doctest import testmod

 testmod()


