
"""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()

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

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

UpperCamelCase_						   =   0
UpperCamelCase_						   =   0
UpperCamelCase_						   =   0
UpperCamelCase_						   =   0
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )						->Dict:

   """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    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )						->List[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    UpperCamelCase						(					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    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )						->Any:

   """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 % 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(UpperCAmelCase     )}\n{fast.tokenize(UpperCAmelCase     )}'''
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase     )						->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():
            UpperCamelCase_						   =   list(slow_class.max_model_input_sizes.keys())
            for checkpoint in checkpoint_names:
                  UpperCamelCase_						   =   0
                  UpperCamelCase_						   =   0
                  UpperCamelCase_						   =   0
                  UpperCamelCase_						   =   0

                  print(F"""========================== Checking {name}: {checkpoint} ==========================""")
                  UpperCamelCase_						   =   slow_class.from_pretrained(checkpoint, force_download=True)
                  UpperCamelCase_						   =   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_torch_available,
    is_vision_available,
)


UpperCamelCase_						   =   {
    'configuration_mobilevitv2': [
        'MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileViTV2Config',
        'MobileViTV2OnnxConfig',
    ],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase_						   =   [
          'MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST',
          'MobileViTV2ForImageClassification',
          'MobileViTV2ForSemanticSegmentation',
          'MobileViTV2Model',
          'MobileViTV2PreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_mobilevitva import (
          MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
          MobileViTVaConfig,
          MobileViTVaOnnxConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mobilevitva import (
                MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                MobileViTVaForImageClassification,
                MobileViTVaForSemanticSegmentation,
                MobileViTVaModel,
                MobileViTVaPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase_						   =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)