



def 		UpperCamelCase     (       snake_case__	:   str     )     ->					str:
   UpperCamelCase   :  str						     =     0

   # if input_string is "aba" than new_input_string become "a|b|a"
   UpperCamelCase   :  str						     =     ''
   UpperCamelCase   :  Dict						     =     ''

   # append each character + "|" in new_string for range(0, length-1)
   for i in input_string[: len(snake_case__     ) - 1]:
      new_input_string += i + "|"
   # append last character
   new_input_string += input_string[-1]

   # we will store the starting and ending of previous furthest ending palindromic
   # substring
   UpperCamelCase     ,					UpperCamelCase   :  Tuple						     =     0, 0

   # length[i] shows the length of palindromic substring with center i
   UpperCamelCase   :  Tuple						     =     [1 for i in range(len(snake_case__     )     )]

   # for each character in new_string find corresponding palindromic string
   UpperCamelCase   :  Dict						     =     0
   for j in range(len(snake_case__     )     ):
      UpperCamelCase   :  Dict						     =     1 if j > r else min(length[l + r - j] // 2	,					r - j + 1     )
      while (
          j - k >= 0
          and j + k < len(snake_case__     )
          and new_input_string[k + j] == new_input_string[j - k]
      ):
         k += 1

      UpperCamelCase   :  Optional[Any]						     =     2 * k - 1

      # does this string is ending after the previously explored end (that is r) ?
      # if yes the update the new r to the last index of this
      if j + k - 1 > r:
         UpperCamelCase   :  Union[str, Any]						     =     j - k + 1  # noqa: E741
         UpperCamelCase   :  Tuple						     =     j + k - 1

      # update max_length and start position
      if max_length < length[j]:
         UpperCamelCase   :  List[Any]						     =     length[j]
         UpperCamelCase   :  Dict						     =     j

    # create that string
   UpperCamelCase   :  List[Any]						     =     new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
   for i in s:
      if i != "|":
         output_string += i

   return output_string


if __name__ == "__main__":
    import doctest

    doctest.testmod()








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, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor



class     lowerCAmelCase_		(  unittest.TestCase							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=13,      SCREAMING_SNAKE_CASE_=3,      SCREAMING_SNAKE_CASE_=224,      SCREAMING_SNAKE_CASE_=30,      SCREAMING_SNAKE_CASE_=400,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],      SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],      )					->		List[str]:
          UpperCamelCase   :  Optional[int]						     =     size if size is not None else {'height': 18, 'width': 18}
          UpperCamelCase   :  List[Any]						     =     parent
          UpperCamelCase   :  List[Any]						     =     batch_size
          UpperCamelCase   :  int						     =     num_channels
          UpperCamelCase   :  int						     =     image_size
          UpperCamelCase   :  List[Any]						     =     min_resolution
          UpperCamelCase   :  int						     =     max_resolution
          UpperCamelCase   :  Any						     =     do_resize
          UpperCamelCase   :  Optional[int]						     =     size
          UpperCamelCase   :  List[str]						     =     do_normalize
          UpperCamelCase   :  Optional[Any]						     =     image_mean
          UpperCamelCase   :  Tuple						     =     image_std






       def  snake_case_	(      self	)					->		List[Any]:
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
          }






@require_torch
@require_vision
class     lowerCAmelCase_		(  a__	,   unittest.TestCase							):
       UpperCAmelCase__    : Optional[Any]       					=  ViTImageProcessor if is_vision_available() else None
       def  snake_case_	(      self	)					->		Any:
          UpperCamelCase   :  Dict						     =     EfficientFormerImageProcessorTester(self	)
       @property
       def  snake_case_	(      self	)					->		List[Any]:
          return self.image_proc_tester.prepare_image_processor_dict()
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  Optional[int]						     =     self.image_processing_class(**self.image_processor_dict	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'image_mean'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'image_std'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'do_normalize'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'do_resize'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'size'	)	)
       def  snake_case_	(      self	)					->		Any:
          pass
       def  snake_case_	(      self	)					->		int:
          # Initialize image_processor
          UpperCamelCase   :  Union[str, Any]						     =     self.image_processing_class(**self.image_processor_dict	)
          # create random PIL images
          UpperCamelCase   :  List[str]						     =     prepare_image_inputs(self.image_proc_tester,      equal_resolution=SCREAMING_SNAKE_CASE_	)
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,      Image.Image	)

          # Test not batched input
          UpperCamelCase   :  str						     =     image_processor(image_inputs[0],      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )

          # Test batched
          UpperCamelCase   :  Optional[Any]						     =     image_processor(SCREAMING_SNAKE_CASE_,      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )
       def  snake_case_	(      self	)					->		str:
          # Initialize image_processor
          UpperCamelCase   :  List[str]						     =     self.image_processing_class(**self.image_processor_dict	)
          # create random numpy tensors
          UpperCamelCase   :  Union[str, Any]						     =     prepare_image_inputs(self.image_proc_tester,      equal_resolution=SCREAMING_SNAKE_CASE_,      numpify=SCREAMING_SNAKE_CASE_	)
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,      np.ndarray	)

          # Test not batched input
          UpperCamelCase   :  Dict						     =     image_processor(image_inputs[0],      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )

          # Test batched
          UpperCamelCase   :  Dict						     =     image_processor(SCREAMING_SNAKE_CASE_,      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )






       def  snake_case_	(      self	)					->		Tuple:
          # Initialize image_processor
          UpperCamelCase   :  Any						     =     self.image_processing_class(**self.image_processor_dict	)
          # create random PyTorch tensors
          UpperCamelCase   :  int						     =     prepare_image_inputs(self.image_proc_tester,      equal_resolution=SCREAMING_SNAKE_CASE_,      torchify=SCREAMING_SNAKE_CASE_	)
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,      torch.Tensor	)

          # Test not batched input
          UpperCamelCase   :  Optional[int]						     =     image_processor(image_inputs[0],      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  1,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )

          # Test batched
          UpperCamelCase   :  int						     =     image_processor(SCREAMING_SNAKE_CASE_,      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_images.shape,      (
                  self.image_proc_tester.batch_size,
                  self.image_proc_tester.num_channels,
                  self.image_proc_tester.size['height'],
                  self.image_proc_tester.size['width'],
              ),      )



