





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()

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

__A       : Union[str, Any]						    = datasets.load_dataset("xnli", split="test+validation")

__A       : Tuple						    = 0
__A       : str						    = 0
__A       : List[Any]						    = 0
__A       : Union[str, Any]						    = 0





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Union[str, Any]:




    """simple docstring"""




    if spm_diff == list(reversed(_SCREAMING_SNAKE_CASE							)							):
        # AAA -> AA+A vs A+AA case.
        return True
    elif len(_SCREAMING_SNAKE_CASE							) == len(_SCREAMING_SNAKE_CASE							) and fast.decode(_SCREAMING_SNAKE_CASE							) == fast.decode(_SCREAMING_SNAKE_CASE							):
        # Second order OK
        # Barrich -> Barr + ich vs Bar + rich
        return True
    _A    =       slow.encode(slow.decode(_SCREAMING_SNAKE_CASE							)							)
    _A    =       fast.encode(fast.decode(_SCREAMING_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 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Union[str, Any]:




    """simple docstring"""




    _A    =       fast.encode_plus(_SCREAMING_SNAKE_CASE							)[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 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Tuple:




    """simple docstring"""




    for i, (spm_id, tok_id) in enumerate(zip(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)							):
        if spm_id != tok_id:
            break
    _A    =       i
    for i, (spm_id, tok_id) in enumerate(zip(reversed(_SCREAMING_SNAKE_CASE							)       ,    reversed(_SCREAMING_SNAKE_CASE							)							)							):
        if spm_id != tok_id:
            break
    _A    =       len(_SCREAMING_SNAKE_CASE							) - i

    _A    =       spm_ids[first:last]
    _A    =       tok_ids[first:last]

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

    if check_LTR_mark(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							):
        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(_SCREAMING_SNAKE_CASE							)							):
                _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]       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							) and check_details(
                        _SCREAMING_SNAKE_CASE       ,    spm_ids[first + i : last]       ,    tok_ids[first + j : last]       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    ):
                        return True

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

    _A    =       fast.decode(spm_ids[:first]							)
    _A    =       fast.decode(spm_ids[last:]							)
    _A    =       fast.decode(spm_ids[first:last]							)
    print()
    print(_SCREAMING_SNAKE_CASE							)
    return False





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   Optional[int]:




    """simple docstring"""




    global perfect
    global imperfect
    global wrong
    global total

    _A    =       slow.encode(_SCREAMING_SNAKE_CASE							)
    _A    =       fast.encode(_SCREAMING_SNAKE_CASE							)

    _A    =       False
    total += 1

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

    if total % 10_000 == 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(_SCREAMING_SNAKE_CASE							)}\n{fast.tokenize(_SCREAMING_SNAKE_CASE							)}"





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   List[Any]:




    """simple docstring"""




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

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


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

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












from ..utils import DummyObject, requires_backends


__A       : str						    = None





def 							__lowerCAmelCase(					*_SCREAMING_SNAKE_CASE       ,    **_SCREAMING_SNAKE_CASE							)       ->   Tuple:




    """simple docstring"""




    requires_backends(_SCREAMING_SNAKE_CASE       ,    ['sentencepiece', 'tokenizers']							)





