"""simple docstring"""







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

_SCREAMING_SNAKE_CASE	:		Any   =   datasets.load_dataset('''xnli''', split='''test+validation''')

_SCREAMING_SNAKE_CASE	:		Optional[Any]   =   0
_SCREAMING_SNAKE_CASE	:		int   =   0
_SCREAMING_SNAKE_CASE	:		List[Any]   =   0
_SCREAMING_SNAKE_CASE	:		Optional[Any]   =   0



def       A__    (	lowercase_     :	Optional[int]      , lowercase_     :	Optional[Any]      , lowercase_     :	List[Any]      , lowercase_     :	Dict						)  ->							List[Any]:
   if spm_diff == list(reversed(lowercase_						)						):
      # AAA -> AA+A vs A+AA case.
      return True
   elif len(lowercase_						) == len(lowercase_						) and fast.decode(lowercase_						) == fast.decode(lowercase_						):
      # Second order OK
      # Barrich -> Barr + ich vs Bar + rich
      return True
   __lowerCamelCase    		=					slow.encode(slow.decode(lowercase_						)						)
   __lowerCamelCase    		=					fast.encode(fast.decode(lowercase_						)						)
   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__    (	lowercase_     :	Dict      , lowercase_     :	str      , lowercase_     :	Union[str, Any]						)  ->							Dict:
   __lowerCamelCase    		=					fast.encode_plus(lowercase_						)[0]
   __lowerCamelCase    		=					enc.offsets
   __lowerCamelCase		,__lowerCamelCase    		=					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__    (	lowercase_     :	Union[str, Any]      , lowercase_     :	Optional[Any]      , lowercase_     :	Union[str, Any]      , lowercase_     :	Union[str, Any]      , lowercase_     :	Any						)  ->							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(lowercase_      , lowercase_						)						):
      if spm_id != tok_id:
         break
   __lowerCamelCase    		=					i
   for i, (spm_id, tok_id) in enumerate(zip(reversed(lowercase_						)      , reversed(lowercase_						)						)						):
      if spm_id != tok_id:
         break
   __lowerCamelCase    		=					len(lowercase_						) - i

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

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

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

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

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

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

   __lowerCamelCase    		=					fast.decode(spm_ids[:first]						)
   __lowerCamelCase    		=					fast.decode(spm_ids[last:]						)
   __lowerCamelCase    		=					fast.decode(spm_ids[first:last]						)
   print()
   print(lowercase_						)
   return False



def       A__    (	lowercase_     :	str      , lowercase_     :	Dict      , lowercase_     :	Union[str, Any]						)  ->							Any:
   global perfect
   global imperfect
   global wrong
   global total

   __lowerCamelCase    		=					slow.encode(lowercase_						)
   __lowerCamelCase    		=					fast.encode(lowercase_						)

   __lowerCamelCase    		=					False
   total += 1

   if slow_ids != fast_ids:
      if check_details(lowercase_      , lowercase_      , lowercase_      , lowercase_      , lowercase_						):
         __lowerCamelCase    		=					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(lowercase_						)}\n{fast.tokenize(lowercase_						)}'''



def       A__    (	lowercase_     :	Union[str, Any]      , lowercase_     :	Tuple						)  ->							Tuple:
   global batch_total
   for i in range(len(lowercase_						)						):
      # premise, all languages
      for text in dataset[i]["premise"].values():
         test_string(lowercase_      , lowercase_      , lowercase_						)

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


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

               print(F"========================== Checking {name}: {checkpoint} ==========================")
               _SCREAMING_SNAKE_CASE	:		Union[str, Any]   =   slow_class.from_pretrained(checkpoint, force_download=True)
               _SCREAMING_SNAKE_CASE	:		Dict   =   fast_class.from_pretrained(checkpoint, force_download=True)
               test_tokenizer(slow, fast)
               print(F"Accuracy {perfect * 1_0_0 / total:.2f}")







"""simple docstring"""







from ..utils import DummyObject, requires_backends






class      _SCREAMING_SNAKE_CASE							(			metaclass=UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Union[str, Any]    =    ["torch", "librosa"]







   def __init__(	self ,				*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      requires_backends(self ,				['torch', 'librosa']	)







   @classmethod
   def    _A				(	cls ,				*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      requires_backends(cls ,				['torch', 'librosa']	)







   @classmethod
   def    _A				(	cls ,				*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      requires_backends(cls ,				['torch', 'librosa']	)






class      _SCREAMING_SNAKE_CASE							(			metaclass=UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Dict    =    ["torch", "librosa"]







   def __init__(	self ,				*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      requires_backends(self ,				['torch', 'librosa']	)







   @classmethod
   def    _A				(	cls ,				*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      requires_backends(cls ,				['torch', 'librosa']	)







   @classmethod
   def    _A				(	cls ,				*_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      requires_backends(cls ,				['torch', 'librosa']	)






