


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

A   :  Tuple       		=				datasets.load_dataset('''xnli''', split='''test+validation''')

A   :  Optional[int]       		=				0
A   :  List[str]       		=				0
A   :  Union[str, Any]       		=				0
A   :  Any       		=				0






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

       _A  :			str											=      spm_ids[first:last]
       _A  :			int											=      tok_ids[first:last]

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

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

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

              _A  :			Optional[int]											=      {spm_ for (spm_, si) in spms.items() if toks.get(spm_						,0     ) == si}
              _A  :			Dict											=      3
              for i in range(last - first - min_width     ):
                     if all(spm_ids[first + i + j] in removable_tokens for j in range(lowerCamelCase     )     ):
                            _A  :			Union[str, Any]											=      [
                                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]						,lowerCamelCase						,lowerCamelCase     ) and check_details(
                                       lowerCamelCase						,spm_ids[first + i : last]						,tok_ids[first + j : last]						,lowerCamelCase						,lowerCamelCase						,):
                                          return True

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

       _A  :			List[Any]											=      fast.decode(spm_ids[:first]     )
       _A  :			Tuple											=      fast.decode(spm_ids[last:]     )
       _A  :			List[str]											=      fast.decode(spm_ids[first:last]     )
       print()
       print(lowerCamelCase     )
       return False






def    lowerCAmelCase__ (       lowerCamelCase  :    Optional[int]						,lowerCamelCase  :    Optional[int]						,lowerCamelCase  :    List[Any]     ):
       global perfect
       global imperfect
       global wrong
       global total

       _A  :			str											=      slow.encode(lowerCamelCase     )
       _A  :			Dict											=      fast.encode(lowerCamelCase     )

       _A  :			Optional[Any]											=      False
       total += 1

       if slow_ids != fast_ids:
              if check_details(lowerCamelCase						,lowerCamelCase						,lowerCamelCase						,lowerCamelCase						,lowerCamelCase     ):
                     _A  :			Optional[Any]											=      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(lowerCamelCase     )}\n{fast.tokenize(lowerCamelCase     )}'






def    lowerCAmelCase__ (       lowerCamelCase  :    Tuple						,lowerCamelCase  :    Optional[Any]     ):
       global batch_total
       for i in range(len(lowerCamelCase     )     ):
              # premise, all languages
              for text in dataset[i]["premise"].values():
                     test_string(lowerCamelCase						,lowerCamelCase						,lowerCamelCase     )

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


if __name__ == "__main__":
   for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
      A   :  List[str]       		=				list(slow_class.max_model_input_sizes.keys())
      for checkpoint in checkpoint_names:
         A   :  str       		=				0
         A   :  Optional[Any]       		=				0
         A   :  List[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[Any]       		=				fast_class.from_pretrained(checkpoint, force_download=True)
         test_tokenizer(slow, fast)
         print(f"""Accuracy {perfect * 100 / total:.2f}""")










'''simple docstring'''







from unittest.mock import patch

import datasets
from datasets import Dataset






def    lowerCAmelCase__ (       ):
       _A  :			Optional[int]											=      Dataset.from_dict({'col_1': [3, 2, 0, 1]}     )

       with patch('tqdm.auto.tqdm'     ) as mock_tqdm:
              datasets.disable_progress_bar()
              dset.map(lambda lowerCamelCase     : {"col_2": x["col_1"] + 1}     )
              mock_tqdm.assert_not_called()

              mock_tqdm.reset_mock()

              datasets.enable_progress_bar()
              dset.map(lambda lowerCamelCase     : {"col_2": x["col_1"] + 1}     )
              mock_tqdm.assert_called()






