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}""")


_SCREAMING_SNAKE_CASE   						=    [0.485, 0.456, 0.406]
_SCREAMING_SNAKE_CASE   						=    [0.229, 0.224, 0.225]
_SCREAMING_SNAKE_CASE   						=    [0.5, 0.5, 0.5]
_SCREAMING_SNAKE_CASE   						=    [0.5, 0.5, 0.5]
_SCREAMING_SNAKE_CASE   						=    [0.48145466, 0.4578275, 0.40821073]
_SCREAMING_SNAKE_CASE   						=    [0.26862954, 0.26130258, 0.27577711]

