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()

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

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

lowerCAmelCase__					=				0
lowerCAmelCase__					=				0
lowerCAmelCase__					=				0
lowerCAmelCase__					=				0






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

       lowerCAmelCase__  			=      spm_ids[first:last]
       lowerCAmelCase__  			=      tok_ids[first:last]

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

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

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

              lowerCAmelCase__  			=      {spm_ for (spm_, si) in spms.items() if toks.get(spm_			,						0			) == si}
              lowerCAmelCase__  			=      3
              for i in range(last - first - min_width			):
                     if all(spm_ids[first + i + j] in removable_tokens for j in range(snake_case_			)			):
                            lowerCAmelCase__  			=      [
                                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]			,						snake_case_			,						snake_case_			) and check_details(
                                       snake_case_			,						spm_ids[first + i : last]			,						tok_ids[first + j : last]			,						snake_case_			,						snake_case_			,						):
                                          return True

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

       lowerCAmelCase__  			=      fast.decode(spm_ids[:first]			)
       lowerCAmelCase__  			=      fast.decode(spm_ids[last:]			)
       lowerCAmelCase__  			=      fast.decode(spm_ids[first:last]			)
       print()
       print(snake_case_			)
       return False






def    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			):
       global perfect
       global imperfect
       global wrong
       global total

       lowerCAmelCase__  			=      slow.encode(snake_case_			)
       lowerCAmelCase__  			=      fast.encode(snake_case_			)

       lowerCAmelCase__  			=      False
       total += 1

       if slow_ids != fast_ids:
              if check_details(snake_case_			,						snake_case_			,						snake_case_			,						snake_case_			,						snake_case_			):
                     lowerCAmelCase__  			=      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(snake_case_			)}\n{fast.tokenize(snake_case_			)}"""






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

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


if __name__ == "__main__":
    for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
        lowerCAmelCase__					=				list(slow_class.max_model_input_sizes.keys())
        for checkpoint in checkpoint_names:
            lowerCAmelCase__					=				0
            lowerCAmelCase__					=				0
            lowerCAmelCase__					=				0
            lowerCAmelCase__					=				0

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

def    __lowerCamelCase	(		lowerCAmelCase__			):
       return " ".join([word[::-1] for word in input_str.split()]			)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
