

'''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			:	Union[str, Any]								=  {
    name: (getattr(transformers, name), getattr(transformers, name + 'Fast')) for name in SLOW_TO_FAST_CONVERTERS
}

_a			:	Optional[int]								=  datasets.load_dataset('xnli', split='test+validation')

_a			:	List[Any]								=  0
_a			:	List[Any]								=  0
_a			:	Optional[Any]								=  0
_a			:	List[Any]								=  0


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Optional[int]    ,				SCREAMING_SNAKE_CASE :				List[str]    ,				SCREAMING_SNAKE_CASE :				List[str]    ,				SCREAMING_SNAKE_CASE :				int  ):
  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
  UpperCAmelCase  							=		slow.encode(slow.decode(SCREAMING_SNAKE_CASE  )  )
  UpperCAmelCase  							=		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  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Dict    ,				SCREAMING_SNAKE_CASE :				Optional[int]    ,				SCREAMING_SNAKE_CASE :				int  ):
  UpperCAmelCase  							=		fast.encode_plus(SCREAMING_SNAKE_CASE  )[0]
  UpperCAmelCase  							=		enc.offsets
  UpperCAmelCase							, UpperCAmelCase  							=		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__						(  SCREAMING_SNAKE_CASE :				List[Any]    ,				SCREAMING_SNAKE_CASE :				Union[str, Any]    ,				SCREAMING_SNAKE_CASE :				str    ,				SCREAMING_SNAKE_CASE :				Any    ,				SCREAMING_SNAKE_CASE :				List[str]  ):
  # 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
  UpperCAmelCase  							=		i
  for i, (spm_id, tok_id) in enumerate(zip(reversed(SCREAMING_SNAKE_CASE  )    ,				reversed(SCREAMING_SNAKE_CASE  )  )  ):
    if spm_id != tok_id:
      break
  UpperCAmelCase  							=		len(SCREAMING_SNAKE_CASE  ) - i

  UpperCAmelCase  							=		spm_ids[first:last]
  UpperCAmelCase  							=		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
    UpperCAmelCase  							=		Counter(spm_ids[first:last]  )
    UpperCAmelCase  							=		Counter(tok_ids[first:last]  )

    UpperCAmelCase  							=		{spm_ for (spm_, si) in spms.items() if toks.get(spm_    ,				0  ) == si}
    UpperCAmelCase  							=		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  )  ):
        UpperCAmelCase  							=		[
            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

  UpperCAmelCase  							=		fast.decode(spm_ids[:first]  )
  UpperCAmelCase  							=		fast.decode(spm_ids[last:]  )
  UpperCAmelCase  							=		fast.decode(spm_ids[first:last]  )
  print()
  print(SCREAMING_SNAKE_CASE  )
  return False


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Optional[int]    ,				SCREAMING_SNAKE_CASE :				str    ,				SCREAMING_SNAKE_CASE :				Dict  ):
  global perfect
  global imperfect
  global wrong
  global total

  UpperCAmelCase  							=		slow.encode(SCREAMING_SNAKE_CASE  )
  UpperCAmelCase  							=		fast.encode(SCREAMING_SNAKE_CASE  )

  UpperCAmelCase  							=		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  ):
      UpperCAmelCase  							=		True
      imperfect += 1
    else:
      wrong += 1
  else:
    perfect += 1

  if total % 1_0000 == 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  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Union[str, Any]    ,				SCREAMING_SNAKE_CASE :				str  ):
  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			:	Any								=  list(slow_class.max_model_input_sizes.keys())
    for checkpoint in checkpoint_names:
      _a			:	int								=  0
      _a			:	List[Any]								=  0
      _a			:	Any								=  0
      _a			:	Any								=  0

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





'''simple docstring'''




from typing import TypeVar

from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset


_a			:	Optional[int]								=  TypeVar('DatasetType', Dataset, IterableDataset)


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				DatasetType    ,				SCREAMING_SNAKE_CASE :				int    ,				SCREAMING_SNAKE_CASE :				int  ):
  if isinstance(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  ):
    return _split_by_node_map_style_dataset(SCREAMING_SNAKE_CASE    ,				rank=SCREAMING_SNAKE_CASE    ,				world_size=SCREAMING_SNAKE_CASE  )
  else:
    return _split_by_node_iterable_dataset(SCREAMING_SNAKE_CASE    ,				rank=SCREAMING_SNAKE_CASE    ,				world_size=SCREAMING_SNAKE_CASE  )


