






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

   lowercase__						:	List[str]         = spm_ids[first:last]
   lowercase__						:	Union[str, Any]         = tok_ids[first:last]

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

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

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

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

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

   lowercase__						:	Optional[int]         = fast.decode(spm_ids[:first]					)
   lowercase__						:	str         = fast.decode(spm_ids[last:]					)
   lowercase__						:	Optional[int]         = fast.decode(spm_ids[first:last]					)
   print()
   print(A					)
   return False
def       a__						(  A		,				A		,				A					):
   global perfect
   global imperfect
   global wrong
   global total

   lowercase__						:	Any         = slow.encode(A					)
   lowercase__						:	int         = fast.encode(A					)

   lowercase__						:	Tuple         = False
   total += 1

   if slow_ids != fast_ids:
      if check_details(A		,				A		,				A		,				A		,				A					):
         lowercase__						:	int         = 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(A					)}\n{fast.tokenize(A					)}'''
def       a__						(  A		,				A					):
   global batch_total
   for i in range(len(A					)					):
      # premise, all languages
      for text in dataset[i]["premise"].values():
         test_string(A		,				A		,				A					)

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


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 * 100 / total:.2f}')










from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve
def       a__						(  A		,				A		,				A					):
   lowercase__						:	Dict         = np.stack((horizontal_flow, vertical_flow)		,				2					)

   # Create a grid of all pixel coordinates and subtract the flow to get the
   # target pixels coordinates
   lowercase__						:	Tuple         = np.stack(
       np.meshgrid(np.arange(0		,				image.shape[1]					)		,				np.arange(0		,				image.shape[0]					)					)		,				2					)
   lowercase__						:	Union[str, Any]         = np.round(grid - flow					).astype(np.intaa					)

   # Find the locations outside of the original image
   lowercase__						:	Optional[Any]         = (grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]]					))
   lowercase__						:	int         = 0

   lowercase__						:	Tuple         = image[grid[:, :, 1], grid[:, :, 0]]

   # Set pixels at invalid locations to 0
   lowercase__						:	str         = 0

   return warped
def       a__						(  A		,				A		,				A		,				A = None		,				):
   if alpha is None:
      lowercase__						:	Tuple         = 0.1

   # Initialize flow
   lowercase__						:	Optional[int]         = np.zeros_like(A					)
   lowercase__						:	Tuple         = np.zeros_like(A					)

   # Prepare kernels for the calculation of the derivatives and the average velocity
   lowercase__						:	Tuple         = np.array([[-1, 1], [-1, 1]]					) * 0.2_5
   lowercase__						:	int         = np.array([[-1, -1], [1, 1]]					) * 0.2_5
   lowercase__						:	Optional[int]         = np.array([[1, 1], [1, 1]]					) * 0.2_5
   lowercase__						:	Optional[Any]         = np.array(
       [[1 / 12, 1 / 6, 1 / 12], [1 / 6, 0, 1 / 6], [1 / 12, 1 / 6, 1 / 12]]					)

   # Iteratively refine the flow
   for _ in range(A					):
      lowercase__						:	Optional[int]         = warp(A		,				A		,				A					)
      lowercase__						:	Union[str, Any]         = convolve(A		,				A					) + convolve(A		,				A					)
      lowercase__						:	Union[str, Any]         = convolve(A		,				A					) + convolve(A		,				A					)
      lowercase__						:	str         = convolve(A		,				A					) + convolve(A		,				-kernel_t					)

      lowercase__						:	str         = convolve(A		,				A					)
      lowercase__						:	Tuple         = convolve(A		,				A					)

      # This updates the flow as proposed in the paper (Step 12)
      lowercase__						:	List[Any]         = (
          derivative_x * avg_horizontal_velocity
          + derivative_y * avg_vertical_velocity
          + derivative_t
      )
      lowercase__						:	Union[str, Any]         = update / (alpha**2 + derivative_x**2 + derivative_y**2)

      lowercase__						:	Optional[int]         = avg_horizontal_velocity - derivative_x * update
      lowercase__						:	int         = avg_vertical_velocity - derivative_y * update

   return horizontal_flow, vertical_flow


if __name__ == "__main__":
 import doctest

 doctest.testmod()


