
'''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()

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__		: Any  							=							0
a__		: List[Any]  							=							0
a__		: int  							=							0
a__		: Optional[int]  							=							0





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Optional[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
      __A													=      slow.encode(slow.decode(SCREAMING_SNAKE_CASE_						)						)
      __A													=      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 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      int:







      """simple docstring"""
      __A													=      fast.encode_plus(SCREAMING_SNAKE_CASE_						)[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 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Tuple:







      """simple docstring"""
      # 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(SCREAMING_SNAKE_CASE_   ,	SCREAMING_SNAKE_CASE_						)						):
            if spm_id != tok_id:
                  break
      __A													=      i
      for i, (spm_id, tok_id) in enumerate(zip(reversed(SCREAMING_SNAKE_CASE_						)   ,	reversed(SCREAMING_SNAKE_CASE_						)						)						):
            if spm_id != tok_id:
                  break
      __A													=      len(SCREAMING_SNAKE_CASE_						) - i

      __A													=      spm_ids[first:last]
      __A													=      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
            __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(SCREAMING_SNAKE_CASE_						)						):
                        __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]   ,	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

      __A													=      fast.decode(spm_ids[:first]						)
      __A													=      fast.decode(spm_ids[last:]						)
      __A													=      fast.decode(spm_ids[first:last]						)
      print()
      print(SCREAMING_SNAKE_CASE_						)
      return False





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Tuple:







      """simple docstring"""
      global perfect
      global imperfect
      global wrong
      global total

      __A													=      slow.encode(SCREAMING_SNAKE_CASE_						)
      __A													=      fast.encode(SCREAMING_SNAKE_CASE_						)

      __A													=      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_						):
                  __A													=      True
                  imperfect += 1
            else:
                  wrong += 1
      else:
            perfect += 1

      if total % 1_0_0_0_0 == 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 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase						)->      Union[str, 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__		: int  							=							list(slow_class.max_model_input_sizes.keys())
  for checkpoint in checkpoint_names:
   a__		: Dict  							=							0
   a__		: Any  							=							0
   a__		: Any  							=							0
   a__		: Union[str, Any]  							=							0

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












from typing import TYPE_CHECKING

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


UpperCamelCase__            =     {"""configuration_vit_hybrid""": ["""VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTHybridConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__            =     [
					    """VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """ViTHybridForImageClassification""",
					    """ViTHybridModel""",
					    """ViTHybridPreTrainedModel""",
					]

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__            =     ["""ViTHybridImageProcessor"""]


if TYPE_CHECKING:
					from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_vit_hybrid import (
										    VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ViTHybridForImageClassification,
										    ViTHybridModel,
										    ViTHybridPreTrainedModel,
										)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
					import sys

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




