



"""simple docstring"""
from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

__SCREAMING_SNAKE_CASE     :  List[str]										=					{
    '''A''': ['''B''', '''C'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F'''],
    '''D''': ['''B'''],
    '''E''': ['''B''', '''F'''],
    '''F''': ['''C''', '''E'''],
}



def 				lowerCAmelCase_(      lowercase_   :							List[Any]  ,      lowercase_   :							Optional[Any]							)		->				Tuple:
				_lowerCamelCase					     = {start}
				_lowerCamelCase					     = [start]
				_lowerCamelCase					     = Queue()
				queue.put(_snake_case							)
				while not queue.empty():
								_lowerCamelCase					     = queue.get()
								for w in graph[v]:
												if w not in explored:
																explored.add(_snake_case							)
																result.append(_snake_case							)
																queue.put(_snake_case							)
				return result



def 				lowerCAmelCase_(      lowercase_   :							List[Any]  ,      lowercase_   :							List[str]							)		->				str:
				_lowerCamelCase					     = {start}
				_lowerCamelCase					     = [start]
				_lowerCamelCase					     = deque([start]							)
				while queue:
								_lowerCamelCase					     = queue.popleft()
								for child in graph[v]:
												if child not in visited:
																visited.add(_snake_case							)
																result.append(_snake_case							)
																queue.append(_snake_case							)
				return result



def 				lowerCAmelCase_(      lowercase_   :							str							)		->				int:
				_lowerCamelCase					     = F"""from __main__ import G, {name}"""
				_lowerCamelCase					     = 1_00_00
				_lowerCamelCase					     = timeit(F"""{name}(G, \'A\')"""  ,      setup=_snake_case  ,      number=_snake_case							)
				print(F"""{name:<35} finished {number} runs in {res:.5f} seconds"""							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			benchmark_function('''breadth_first_search''')
			benchmark_function('''breadth_first_search_with_deque''')
			# breadth_first_search                finished 10000 runs in 0.20999 seconds
			# breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds








"""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 _a (		_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      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
      UpperCAmelCase							=       slow.encode(slow.decode(_snake_case							)							)
      UpperCAmelCase							=       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 _a (		_snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      UpperCAmelCase							=       fast.encode_plus(_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 _a (		_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      for i, (spm_id, tok_id) in enumerate(zip(_snake_case      ,       _snake_case							)							):
            if spm_id != tok_id:
                  break
      UpperCAmelCase							=       i
      for i, (spm_id, tok_id) in enumerate(zip(reversed(_snake_case							)      ,       reversed(_snake_case							)							)							):
            if spm_id != tok_id:
                  break
      UpperCAmelCase							=       len(_snake_case							) - i

      UpperCAmelCase							=       spm_ids[first:last]
      UpperCAmelCase							=       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
            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(_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]      ,       _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

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

def _a (		_snake_case      ,       _snake_case      ,       _snake_case							):







      """simple docstring"""






      global perfect
      global imperfect
      global wrong
      global total

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

      UpperCAmelCase							=       False
      total += 1

      if slow_ids != fast_ids:
            if check_details(_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case      ,       _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(_snake_case							)}\n{fast.tokenize(_snake_case							)}'''

def _a (		_snake_case      ,       _snake_case							):







      """simple docstring"""






      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():
    _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}""")



