





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
    UpperCamelCase__    =       slow.encode(slow.decode(_SCREAMING_SNAKE_CASE							)							)
    UpperCamelCase__    =       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"""




    UpperCamelCase__    =       fast.encode_plus(_SCREAMING_SNAKE_CASE							)[0]
    UpperCamelCase__    =       enc.offsets
    UpperCamelCase__, UpperCamelCase__    =       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
    UpperCamelCase__    =       i
    for i, (spm_id, tok_id) in enumerate(zip(reversed(_SCREAMING_SNAKE_CASE							)       ,    reversed(_SCREAMING_SNAKE_CASE							)							)							):
        if spm_id != tok_id:
            break
    UpperCamelCase__    =       len(_SCREAMING_SNAKE_CASE							) - i

    UpperCamelCase__    =       spm_ids[first:last]
    UpperCamelCase__    =       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
        UpperCamelCase__    =       Counter(spm_ids[first:last]							)
        UpperCamelCase__    =       Counter(tok_ids[first:last]							)

        UpperCamelCase__    =       {spm_ for (spm_, si) in spms.items() if toks.get(spm_       ,    0							) == si}
        UpperCamelCase__    =       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							)							):
                UpperCamelCase__    =       [
                    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

    UpperCamelCase__    =       fast.decode(spm_ids[:first]							)
    UpperCamelCase__    =       fast.decode(spm_ids[last:]							)
    UpperCamelCase__    =       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

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

    UpperCamelCase__    =       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							):
            UpperCamelCase__    =       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 sklearn.metrics import accuracy_score

import datasets


__A       : Union[str, Any]						    = "\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n"


__A       : List[Any]						    = "\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {'accuracy': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {'accuracy': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {'accuracy': 0.8778625954198473}\n"


__A       : List[str]						    = "\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,   _KWARGS_DESCRIPTION  )
class     lowerCamelCase(	datasets.Metric  ):
     '''simple docstring'''


     def 					lowerCAmelCase__		(						self       ):
         return datasets.MetricInfo(
             description=_DESCRIPTION							,    citation=_CITATION							,    inputs_description=_KWARGS_DESCRIPTION							,    features=datasets.Features(
                 {
                     'predictions': datasets.Sequence(datasets.Value('int32'       )       ),
                     'references': datasets.Sequence(datasets.Value('int32'       )       ),
                 }
                 if self.config_name == 'multilabel'
                 else {
                     'predictions': datasets.Value('int32'       ),
                     'references': datasets.Value('int32'       ),
                 }       )							,    reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html']							,    )




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_=True							,    snake_case_=None       ):
         return {
             "accuracy": float(
                 accuracy_score(snake_case_							,    snake_case_							,    normalize=snake_case_							,    sample_weight=snake_case_       )       )
         }





