

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

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

__A     						=      0
__A     						=      0
__A     						=      0
__A     						=      0



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)				->      str:



  """simple docstring"""






  if spm_diff == list(reversed(_UpperCamelCase	)	):
    # AAA -> AA+A vs A+AA case.
    return True
  elif len(_UpperCamelCase	) == len(_UpperCamelCase	) and fast.decode(_UpperCamelCase	) == fast.decode(_UpperCamelCase	):
    # Second order OK
    # Barrich -> Barr + ich vs Bar + rich
    return True
  _snake_case		=				slow.encode(slow.decode(_UpperCamelCase	)	)
  _snake_case		=				fast.encode(fast.decode(_UpperCamelCase	)	)
  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	)				->      Dict:



  """simple docstring"""






  _snake_case		=				fast.encode_plus(_UpperCamelCase	)[0]
  _snake_case		=				enc.offsets
  _snake_case,	_snake_case		=				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	)				->      List[Any]:



  """simple docstring"""






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

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

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

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

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

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

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

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



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	)				->      int:



  """simple docstring"""






  global perfect
  global imperfect
  global wrong
  global total

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

  _snake_case		=				False
  total += 1

  if slow_ids != fast_ids:
    if check_details(_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase					,			_UpperCamelCase	):
      _snake_case		=				True
      imperfect += 1
    else:
      wrong += 1
  else:
    perfect += 1

  if total % 10_000 == 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(_UpperCamelCase	)}\n{fast.tokenize(_UpperCamelCase	)}"""



def  snake_case_(_UpperCamelCase					,			_UpperCamelCase	)				->      Dict:



  """simple docstring"""






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

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


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

   print(f'''========================== Checking {name}: {checkpoint} ==========================''')
   __A     						=      slow_class.from_pretrained(checkpoint, force_download=True)
   __A     						=      fast_class.from_pretrained(checkpoint, force_download=True)
   test_tokenizer(slow, fast)
   print(f'''Accuracy {perfect * 1_00 / total:.2f}''')




import functools
from math import sqrt
from time import time



def  snake_case_(_UpperCamelCase					,			*_UpperCamelCase					,			**_UpperCamelCase	)				->      str:



  """simple docstring"""






  _snake_case		=				time()
  _snake_case		=				func(*_UpperCamelCase					,			**_UpperCamelCase	)
  _snake_case		=				time()
  if int(end - start	) > 0:
    print(F"""{func.__name__} runtime: {(end - start):0.4f} s"""	)
  else:
    print(F"""{func.__name__} runtime: {(end - start) * 1_000:0.4f} ms"""	)
  return output



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  if n < 0:
    raise Exception('''n is negative'''	)
  if n == 0:
    return [0]
  _snake_case		=				[0, 1]
  for _ in range(n - 1	):
    fib.append(fib[-1] + fib[-2]	)
  return fib



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  def fib_recursive_term(_UpperCamelCase	) -> int:
    if i < 0:
      raise Exception('''n is negative'''	)
    if i < 2:
      return i
    return fib_recursive_term(i - 1	) + fib_recursive_term(i - 2	)

  if n < 0:
    raise Exception('''n is negative'''	)
  return [fib_recursive_term(_UpperCamelCase	) for i in range(n + 1	)]



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  @functools.cache
  def fib_recursive_term(_UpperCamelCase	) -> int:
    if i < 0:
      raise Exception('''n is negative'''	)
    if i < 2:
      return i
    return fib_recursive_term(i - 1	) + fib_recursive_term(i - 2	)

  if n < 0:
    raise Exception('''n is negative'''	)
  return [fib_recursive_term(_UpperCamelCase	) for i in range(n + 1	)]



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  if n < 0:
    raise Exception('''n is negative'''	)
  # Cache must be outside recursuive function
  # other it will reset every time it calls itself.
  _snake_case		=				{0: 0, 1: 1, 2: 1}  # Prefilled cache

  def rec_fn_memoized(_UpperCamelCase	) -> int:
    if num in cache:
      return cache[num]

    _snake_case		=				rec_fn_memoized(num - 1	) + rec_fn_memoized(num - 2	)
    _snake_case		=				value
    return value

  return [rec_fn_memoized(_UpperCamelCase	) for i in range(n + 1	)]



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  if n < 0:
    raise Exception('''n is negative'''	)
  if n >= 1_475:
    raise Exception('''n is too large'''	)
  _snake_case		=				sqrt(5	)
  _snake_case		=				(1 + sqrt_a) / 2
  return [round(phi**i / sqrt_a	) for i in range(n + 1	)]


if __name__ == "__main__":
 __A     						=      30
 time_func(fib_iterative, num)
 time_func(fib_recursive, num)  # Around 3s runtime
 time_func(fib_recursive_cached, num)  # Around 0ms runtime
 time_func(fib_memoization, num)
 time_func(fib_binet, num)

