

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()

lowerCAmelCase       :				List[Any]         =					{
    name: (getattr(transformers, name), getattr(transformers, name + 'Fast')) for name in SLOW_TO_FAST_CONVERTERS
}

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

lowerCAmelCase       :				List[str]         =					0
lowerCAmelCase       :				Tuple         =					0
lowerCAmelCase       :				List[str]         =					0
lowerCAmelCase       :				Tuple         =					0

def 				A_					(							a   ,				a   ,				a   ,				a					):

      """simple docstring"""



      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_			:					str          =       slow.encode(slow.decode(a					)					)
      SCREAMING_SNAKE_CASE_			:					Union[str, 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 				A_					(							a   ,				a   ,				a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					str          =       fast.encode_plus(a					)[0]
      SCREAMING_SNAKE_CASE_			:					Dict          =       enc.offsets
      SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			:					Tuple          =       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 				A_					(							a   ,				a   ,				a   ,				a   ,				a					):

      """simple docstring"""



      for i, (spm_id, tok_id) in enumerate(zip(a   ,				a					)					):
            if spm_id != tok_id:
                  break
      SCREAMING_SNAKE_CASE_			:					str          =       i
      for i, (spm_id, tok_id) in enumerate(zip(reversed(a					)   ,				reversed(a					)					)					):
            if spm_id != tok_id:
                  break
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       len(a					) - i

      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       spm_ids[first:last]
      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       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_			:					Dict          =       Counter(spm_ids[first:last]					)
            SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       Counter(tok_ids[first:last]					)

            SCREAMING_SNAKE_CASE_			:					str          =       {spm_ for (spm_, si) in spms.items() if toks.get(spm_   ,				0					) == si}
            SCREAMING_SNAKE_CASE_			:					Optional[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_			:					List[str]          =       [
                            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_			:					Optional[int]          =       fast.decode(spm_ids[:first]					)
      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       fast.decode(spm_ids[last:]					)
      SCREAMING_SNAKE_CASE_			:					int          =       fast.decode(spm_ids[first:last]					)
      print()
      print(a					)
      return False

def 				A_					(							a   ,				a   ,				a					):

      """simple docstring"""



      global perfect
      global imperfect
      global wrong
      global total

      SCREAMING_SNAKE_CASE_			:					Tuple          =       slow.encode(a					)
      SCREAMING_SNAKE_CASE_			:					str          =       fast.encode(a					)

      SCREAMING_SNAKE_CASE_			:					List[str]          =       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 % 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(a					)}\n{fast.tokenize(a					)}"

def 				A_					(							a   ,				a					):

      """simple docstring"""



      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():
        lowerCAmelCase       :				Optional[int]         =					list(slow_class.max_model_input_sizes.keys())
        for checkpoint in checkpoint_names:
            lowerCAmelCase       :				Optional[Any]         =					0
            lowerCAmelCase       :				Union[str, Any]         =					0
            lowerCAmelCase       :				Optional[int]         =					0
            lowerCAmelCase       :				Optional[int]         =					0

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









from math import sqrt

def 				A_					(							a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					List[str]          =       [1]
      for i in range(2   ,				int(sqrt(a					)					) + 1   ,				1					):
            if number % i == 0:
                  values.append(a					)
                  if int(number // i					) != i:
                        values.append(int(number // i					)					)
      return sorted(a					)

def 				A_					(							a					):

      """simple docstring"""



      return sum(factors(a					)					) > n

def 				A_					(							a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       factors(a					)
      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       len(a					)
      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       [[0 for i in range(number + 1					)] for j in range(r + 1					)]
      for i in range(r + 1					):
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       True

      for i in range(1   ,				number + 1					):
            SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       False

      for i in range(1   ,				r + 1					):
            for j in range(1   ,				number + 1					):
                  if j < values[i - 1]:
                        SCREAMING_SNAKE_CASE_			:					Dict          =       subset[i - 1][j]
                  else:
                        SCREAMING_SNAKE_CASE_			:					List[str]          =       subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

      return subset[r][number] != 0

def 				A_					(							a					):

      """simple docstring"""



      return abundant(a					) and not semi_perfect(a					)


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)
    for number in (69, 70, 71):
        print(F'{number} is {"" if weird(number) else "not "}weird.')






