"""simple docstring"""




def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Union[str, Any]     )	->				str:
  _lowerCAmelCase						:	List[str]        =					0

  # if input_string is "aba" than new_input_string become "a|b|a"
  _lowerCAmelCase						:	List[str]        =					""""""
  _lowerCAmelCase						:	Dict        =					""""""

  # append each character + "|" in new_string for range(0, length-1)
  for i in input_string[: len(_lowercase     ) - 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
  _lowerCAmelCase      ,     _lowerCAmelCase						:	List[Any]        =					0, 0

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

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

    _lowerCAmelCase						:	Dict        =					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:
      _lowerCAmelCase						:	Union[str, Any]        =					j - k + 1  # noqa: E741
      _lowerCAmelCase						:	List[Any]        =					j + k - 1

    # update max_length and start position
    if max_length < length[j]:
      _lowerCAmelCase						:	Optional[int]        =					length[j]
      _lowerCAmelCase						:	List[str]        =					j

    # create that string
  _lowerCAmelCase						:	List[str]        =					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()





"""simple docstring"""




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 PoolFormerImageProcessor
class 						__A							(			unittest.TestCase		):






     def __init__(		self    ,					a__    ,					a__=7    ,					a__=3    ,					a__=30    ,					a__=400    ,					a__=True    ,					a__=None    ,					a__=0.9    ,					a__=None    ,					a__=True    ,					a__=[0.5, 0.5, 0.5]    ,					a__=[0.5, 0.5, 0.5]    ,					):
       _lowerCAmelCase						:	int        =					size if size is not None else {"""shortest_edge""": 30}
       _lowerCAmelCase						:	Dict        =					crop_size if crop_size is not None else {"""height""": 30, """width""": 30}
       _lowerCAmelCase						:	List[Any]        =					parent
       _lowerCAmelCase						:	Optional[int]        =					batch_size
       _lowerCAmelCase						:	Any        =					num_channels
       _lowerCAmelCase						:	str        =					min_resolution
       _lowerCAmelCase						:	Dict        =					max_resolution
       _lowerCAmelCase						:	str        =					do_resize_and_center_crop
       _lowerCAmelCase						:	List[str]        =					size
       _lowerCAmelCase						:	int        =					crop_pct
       _lowerCAmelCase						:	int        =					crop_size
       _lowerCAmelCase						:	Union[str, Any]        =					do_normalize
       _lowerCAmelCase						:	Tuple        =					image_mean
       _lowerCAmelCase						:	Optional[Any]        =					image_std






     def    __A  (		self  ):
       return {
           "size": self.size,
           "do_resize_and_center_crop": self.do_resize_and_center_crop,
           "crop_pct": self.crop_pct,
           "crop_size": self.crop_size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }







@require_torch
@require_vision
class 						__A							(			SCREAMING_SNAKE_CASE_   ,     unittest.TestCase		):
     _UpperCamelCase							:      List[str]		 =		PoolFormerImageProcessor if is_vision_available() else None






     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					PoolFormerImageProcessingTester(self  )






     @property
     def    __A  (		self  ):
       return self.image_processor_tester.prepare_image_processor_dict()






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class(**self.image_processor_dict  )
       self.assertTrue(hasattr(a__    ,					"""do_resize_and_center_crop"""  )  )
       self.assertTrue(hasattr(a__    ,					"""size"""  )  )
       self.assertTrue(hasattr(a__    ,					"""crop_pct"""  )  )
       self.assertTrue(hasattr(a__    ,					"""do_normalize"""  )  )
       self.assertTrue(hasattr(a__    ,					"""image_mean"""  )  )
       self.assertTrue(hasattr(a__    ,					"""image_std"""  )  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class.from_dict(self.image_processor_dict  )
       self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 30}  )
       self.assertEqual(image_processor.crop_size    ,					{"""height""": 30, """width""": 30}  )

       _lowerCAmelCase						:	List[Any]        =					self.image_processing_class.from_dict(self.image_processor_dict    ,					size=42    ,					crop_size=84  )
       self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 42}  )
       self.assertEqual(image_processor.crop_size    ,					{"""height""": 84, """width""": 84}  )






     def    __A  (		self  ):
       pass






     def    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class(**self.image_processor_dict  )
       # create random PIL images
       _lowerCAmelCase						:	Tuple        =					prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__    ,					Image.Image  )

       # Test not batched input
       _lowerCAmelCase						:	Tuple        =					image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values

       self.assertEqual(
           encoded_images.shape    ,					(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)

       # Test batched
       _lowerCAmelCase						:	List[str]        =					image_processing(a__    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)






     def    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	Union[str, Any]        =					self.image_processing_class(**self.image_processor_dict  )
       # create random numpy tensors
       _lowerCAmelCase						:	Union[str, Any]        =					prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=a__    ,					numpify=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__    ,					np.ndarray  )

       # Test not batched input
       _lowerCAmelCase						:	Dict        =					image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)

       # Test batched
       _lowerCAmelCase						:	int        =					image_processing(a__    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)






     def    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	Any        =					self.image_processing_class(**self.image_processor_dict  )
       # create random PyTorch tensors
       _lowerCAmelCase						:	Any        =					prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=a__    ,					torchify=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__    ,					torch.Tensor  )

       # Test not batched input
       _lowerCAmelCase						:	str        =					image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)

       # Test batched
       _lowerCAmelCase						:	List[str]        =					image_processing(a__    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)




