


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

lowerCAmelCase__						=			datasets.load_dataset('xnli', split='test+validation')

lowerCAmelCase__						=			0
lowerCAmelCase__						=			0
lowerCAmelCase__						=			0
lowerCAmelCase__						=			0
def 							_UpperCAmelCase    (UpperCamelCase__					:  Optional[Any]    ,							UpperCamelCase__					:  int    ,							UpperCamelCase__					:  Optional[int]    ,							UpperCamelCase__					:  Optional[int]						):
       if spm_diff == list(reversed(UpperCamelCase__						)						):
              # AAA -> AA+A vs A+AA case.
              return True
       elif len(UpperCamelCase__						) == len(UpperCamelCase__						) and fast.decode(UpperCamelCase__						) == fast.decode(UpperCamelCase__						):
              # Second order OK
              # Barrich -> Barr + ich vs Bar + rich
              return True
       _A				:	Optional[int]       =			slow.encode(slow.decode(UpperCamelCase__						)						)
       _A				:	Any       =			fast.encode(fast.decode(UpperCamelCase__						)						)
       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 							_UpperCAmelCase    (UpperCamelCase__					:  Dict    ,							UpperCamelCase__					:  Optional[Any]    ,							UpperCamelCase__					:  Optional[Any]						):
       _A				:	Any       =			fast.encode_plus(UpperCamelCase__						)[0]
       _A				:	str       =			enc.offsets
       _A   ,      _A				:	Union[str, 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 							_UpperCAmelCase    (UpperCamelCase__					:  Union[str, Any]    ,							UpperCamelCase__					:  Dict    ,							UpperCamelCase__					:  Optional[int]    ,							UpperCamelCase__					:  str    ,							UpperCamelCase__					:  Any						):
       # Encoding can be the same with same result AAA -> A + AA vs AA + A
       # We can check that we use at least exactly the same number of tokens.
       for i, (spm_id, tok_id) in enumerate(zip(UpperCamelCase__    ,							UpperCamelCase__						)						):
              if spm_id != tok_id:
                     break
       _A				:	Dict       =			i
       for i, (spm_id, tok_id) in enumerate(zip(reversed(UpperCamelCase__						)    ,							reversed(UpperCamelCase__						)						)						):
              if spm_id != tok_id:
                     break
       _A				:	Union[str, Any]       =			len(UpperCamelCase__						) - i

       _A				:	int       =			spm_ids[first:last]
       _A				:	List[Any]       =			tok_ids[first:last]

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

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

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

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

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

       _A				:	str       =			fast.decode(spm_ids[:first]						)
       _A				:	List[str]       =			fast.decode(spm_ids[last:]						)
       _A				:	Dict       =			fast.decode(spm_ids[first:last]						)
       print()
       print(UpperCamelCase__						)
       return False
def 							_UpperCAmelCase    (UpperCamelCase__					:  Any    ,							UpperCamelCase__					:  Any    ,							UpperCamelCase__					:  Dict						):
       global perfect
       global imperfect
       global wrong
       global total

       _A				:	List[Any]       =			slow.encode(UpperCamelCase__						)
       _A				:	Tuple       =			fast.encode(UpperCamelCase__						)

       _A				:	int       =			False
       total += 1

       if slow_ids != fast_ids:
              if check_details(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__						):
                     _A				:	Optional[int]       =			True
                     imperfect += 1
              else:
                     wrong += 1
       else:
              perfect += 1

       if total % 10000 == 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(UpperCamelCase__						)}\n{fast.tokenize(UpperCamelCase__						)}"
def 							_UpperCAmelCase    (UpperCamelCase__					:  Tuple    ,							UpperCamelCase__					:  List[Any]						):
       global batch_total
       for i in range(len(UpperCamelCase__						)						):
              # premise, all languages
              for text in dataset[i]["premise"].values():
                     test_string(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__						)

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


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

            print(f"========================== Checking {name}: {checkpoint} ==========================")
            lowerCAmelCase__						=			slow_class.from_pretrained(checkpoint, force_download=True)
            lowerCAmelCase__						=			fast_class.from_pretrained(checkpoint, force_download=True)
            test_tokenizer(slow, fast)
            print(f"Accuracy {perfect * 1_00 / total:.2f}")




import warnings

from .generation import FlaxGenerationMixin







class 					lowerCAmelCase__	(    a):




     '''simple docstring'''
     # warning at import time
     warnings.warn(
         "Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will "
         "be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."					,   a					,   )
