"""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()

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

__magic_name__    				=					datasets.load_dataset("xnli", split="test+validation")

__magic_name__    				=					0
__magic_name__    				=					0
__magic_name__    				=					0
__magic_name__    				=					0





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__					)							->     List[Any]:


   '''simple docstring'''


   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__     				=		slow.encode(slow.decode(UpperCAmelCase__					)					)
   a__     				=		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   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__					)							->     Any:


   '''simple docstring'''


   a__     				=		fast.encode_plus(UpperCAmelCase__					)[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 ( UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__					)							->     Optional[Any]:


   '''simple docstring'''


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

   a__     				=		spm_ids[first:last]
   a__     				=		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__     				=		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(UpperCAmelCase__					)					):
            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],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__     				=		fast.decode(spm_ids[:first]					)
   a__     				=		fast.decode(spm_ids[last:]					)
   a__     				=		fast.decode(spm_ids[first:last]					)
   print()
   print(UpperCAmelCase__					)
   return False





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__					)							->     List[str]:


   '''simple docstring'''


   global perfect
   global imperfect
   global wrong
   global total

   a__     				=		slow.encode(UpperCAmelCase__					)
   a__     				=		fast.encode(UpperCAmelCase__					)

   a__     				=		False
   total += 1

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





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__					)							->     List[Any]:


   '''simple docstring'''


   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():
          __magic_name__    				=					list(slow_class.max_model_input_sizes.keys())
          for checkpoint in checkpoint_names:
               __magic_name__    				=					0
               __magic_name__    				=					0
               __magic_name__    				=					0
               __magic_name__    				=					0

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



"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__magic_name__    				=					{"configuration_hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__    				=					[
         "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "HubertForCTC",
         "HubertForSequenceClassification",
         "HubertModel",
         "HubertPreTrainedModel",
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __magic_name__    				=					[
         "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFHubertForCTC",
         "TFHubertModel",
         "TFHubertPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_hubert import (
              HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              HubertForCTC,
              HubertForSequenceClassification,
              HubertModel,
              HubertPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_hubert import (
              TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFHubertForCTC,
              TFHubertModel,
              TFHubertPreTrainedModel,
          )


else:
     import sys

     __magic_name__    				=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


