






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

_UpperCAmelCase					:						Optional[Any]         =     datasets.load_dataset('xnli', split='test+validation')

_UpperCAmelCase					:						List[Any]         =     0
_UpperCAmelCase					:						List[str]         =     0
_UpperCAmelCase					:						str         =     0
_UpperCAmelCase					:						Optional[int]         =     0






def  UpperCAmelCase__						(    lowerCamelCase	:			Optional[int]			,lowerCamelCase	:			Optional[int]			,lowerCamelCase	:			Tuple			,lowerCamelCase	:			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
   lowerCamelCase									=slow.encode(slow.decode(lowerCamelCase    )    )
   lowerCamelCase									=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  UpperCAmelCase__						(    lowerCamelCase	:			Tuple			,lowerCamelCase	:			str			,lowerCamelCase	:			Union[str, Any]    ):
   lowerCamelCase									=fast.encode_plus(lowerCamelCase    )[0]
   lowerCamelCase									=enc.offsets
   lowerCamelCase				,       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  UpperCAmelCase__						(    lowerCamelCase	:			Optional[int]			,lowerCamelCase	:			Any			,lowerCamelCase	:			Optional[int]			,lowerCamelCase	:			Optional[Any]			,lowerCamelCase	:			Optional[Any]    ):
   # 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
   lowerCamelCase									=i
   for i, (spm_id, tok_id) in enumerate(zip(reversed(lowerCamelCase    )			,reversed(lowerCamelCase    )    )    ):
      if spm_id != tok_id:
         break
   lowerCamelCase									=len(lowerCamelCase    ) - i

   lowerCamelCase									=spm_ids[first:last]
   lowerCamelCase									=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
      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(lowerCamelCase    )    ):
            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]			,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

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






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

   lowerCamelCase									=slow.encode(lowerCamelCase    )
   lowerCamelCase									=fast.encode(lowerCamelCase    )

   lowerCamelCase									=False
   total += 1

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

   if total % 1_00_00 == 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  UpperCAmelCase__						(    lowerCamelCase	:			Union[str, Any]			,lowerCamelCase	:			Union[str, 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():
      _UpperCAmelCase					:						int         =     list(slow_class.max_model_input_sizes.keys())
      for checkpoint in checkpoint_names:
         _UpperCAmelCase					:						Optional[Any]         =     0
         _UpperCAmelCase					:						Optional[Any]         =     0
         _UpperCAmelCase					:						str         =     0
         _UpperCAmelCase					:						str         =     0

         print(f"========================== Checking {name}: {checkpoint} ==========================")
         _UpperCAmelCase					:						List[Any]         =     slow_class.from_pretrained(checkpoint, force_download=True)
         _UpperCAmelCase					:						Union[str, Any]         =     fast_class.from_pretrained(checkpoint, force_download=True)
         test_tokenizer(slow, fast)
         print(f"Accuracy {perfect * 100 / total:.2f}")







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import SwinaSRImageProcessor
   from transformers.image_transforms import get_image_size


class 		lowerCAmelCase_				(  unittest.TestCase ):





     def __init__(	self							, _UpperCAmelCase							, _UpperCAmelCase=7							, _UpperCAmelCase=3							, _UpperCAmelCase=18							, _UpperCAmelCase=30							, _UpperCAmelCase=400							, _UpperCAmelCase=True							, _UpperCAmelCase=1 / 255							, _UpperCAmelCase=True							, _UpperCAmelCase=8							, )		->				Tuple:
        lowerCamelCase									=parent
        lowerCamelCase									=batch_size
        lowerCamelCase									=num_channels
        lowerCamelCase									=image_size
        lowerCamelCase									=min_resolution
        lowerCamelCase									=max_resolution
        lowerCamelCase									=do_rescale
        lowerCamelCase									=rescale_factor
        lowerCamelCase									=do_pad
        lowerCamelCase									=pad_size





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        return {
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_pad": self.do_pad,
            "pad_size": self.pad_size,
        }





     def 						lowerCamelCase							(	self							, _UpperCAmelCase=False							, _UpperCAmelCase=False							, _UpperCAmelCase=False  )		->				int:
        assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

        if equal_resolution:
           lowerCamelCase									=[]
           for i in range(self.batch_size  ):
              image_inputs.append(
                  np.random.randint(
                      255							, size=(self.num_channels, self.max_resolution, self.max_resolution)							, dtype=np.uinta  )  )
        else:
           lowerCamelCase									=[]
           for i in range(self.batch_size  ):
              lowerCamelCase				,       lowerCamelCase									=np.random.choice(np.arange(self.min_resolution							, self.max_resolution  )							, 2  )
              image_inputs.append(np.random.randint(255							, size=(self.num_channels, width, height)							, dtype=np.uinta  )  )

        if not numpify and not torchify:
           # PIL expects the channel dimension as last dimension
           lowerCamelCase									=[Image.fromarray(np.moveaxis(_UpperCAmelCase							, 0							, -1  )  ) for x in image_inputs]

        if torchify:
           lowerCamelCase									=[torch.from_numpy(_UpperCAmelCase  ) for x in image_inputs]

        return image_inputs




@require_torch
@require_vision
class 		lowerCAmelCase_				(  UpperCamelCase				,     unittest.TestCase ):
     __lowerCamelCase       :    Optional[Any] 	=     SwinaSRImageProcessor if is_vision_available() else None





     def 						lowerCamelCase							(	self  )		->				Dict:
        lowerCamelCase									=SwinaSRImageProcessingTester(self  )





     @property
     def 						lowerCamelCase							(	self  )		->				Any:
        return self.image_processor_tester.prepare_image_processor_dict()





     def 						lowerCamelCase							(	self  )		->				Any:
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_rescale"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """rescale_factor"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """do_pad"""  )  )
        self.assertTrue(hasattr(_UpperCAmelCase							, """pad_size"""  )  )





     def 						lowerCamelCase							(	self  )		->				Any:
        pass





     def 						lowerCamelCase							(	self							, _UpperCAmelCase  )		->				int:
        lowerCamelCase				,       lowerCamelCase									=get_image_size(_UpperCAmelCase  )
        lowerCamelCase									=self.image_processor_tester.pad_size

        lowerCamelCase									=(old_height // size + 1) * size - old_height
        lowerCamelCase									=(old_width // size + 1) * size - old_width
        return old_height + pad_height, old_width + pad_width





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, Image.Image  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        lowerCamelCase				,       lowerCamelCase									=self.calculate_expected_size(np.array(image_inputs[0]  )  )
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							, )





     def 						lowerCamelCase							(	self  )		->				Any:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase							, numpify=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, np.ndarray  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        lowerCamelCase				,       lowerCamelCase									=self.calculate_expected_size(image_inputs[0]  )
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							, )





     def 						lowerCamelCase							(	self  )		->				Dict:
        # Initialize image_processor
        lowerCamelCase									=self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        lowerCamelCase									=self.image_processor_tester.prepare_inputs(equal_resolution=_UpperCAmelCase							, torchify=_UpperCAmelCase  )
        for image in image_inputs:
           self.assertIsInstance(_UpperCAmelCase							, torch.Tensor  )

        # Test not batched input
        lowerCamelCase									=image_processor(image_inputs[0]							, return_tensors="""pt"""  ).pixel_values
        lowerCamelCase				,       lowerCamelCase									=self.calculate_expected_size(image_inputs[0]  )
        self.assertEqual(
            encoded_images.shape							, (
                1,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )							, )