
"""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 LevitImageProcessor
class        _SCREAMING_SNAKE_CASE  (  unittest.TestCase							):






				'''simple docstring'''



				def __init__(					self	:			Optional[int]				,     UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			int=7				,     UpperCAmelCase_	:			Optional[int]=3				,     UpperCAmelCase_	:			List[Any]=18				,     UpperCAmelCase_	:			Any=30				,     UpperCAmelCase_	:			List[str]=400				,     UpperCAmelCase_	:			Any=True				,     UpperCAmelCase_	:			Union[str, Any]=None				,     UpperCAmelCase_	:			List[str]=True				,     UpperCAmelCase_	:			Tuple=None				,     UpperCAmelCase_	:			Optional[int]=True				,     UpperCAmelCase_	:			int=[0.5, 0.5, 0.5]				,     UpperCAmelCase_	:			str=[0.5, 0.5, 0.5]				,     )		->  Any:







											"""simple docstring"""




											_lowerCAmelCase  	=			size if size is not None else {'shortest_edge': 18}
											_lowerCAmelCase  	=			crop_size if crop_size is not None else {'height': 18, 'width': 18}
											_lowerCAmelCase  	=			parent
											_lowerCAmelCase  	=			batch_size
											_lowerCAmelCase  	=			num_channels
											_lowerCAmelCase  	=			image_size
											_lowerCAmelCase  	=			min_resolution
											_lowerCAmelCase  	=			max_resolution
											_lowerCAmelCase  	=			do_resize
											_lowerCAmelCase  	=			size
											_lowerCAmelCase  	=			do_center_crop
											_lowerCAmelCase  	=			crop_size
											_lowerCAmelCase  	=			do_normalize
											_lowerCAmelCase  	=			image_mean
											_lowerCAmelCase  	=			image_std







				def 				__lowerCamelCase	(					self	:			Dict  )		->  List[Any]:







											"""simple docstring"""




											return {
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_normalize": self.do_normalize,
											    "do_resize": self.do_resize,
											    "do_center_crop": self.do_center_crop,
											    "size": self.size,
											    "crop_size": self.crop_size,
											}






@require_torch
@require_vision
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase      ,				unittest.TestCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Dict							=					LevitImageProcessor if is_vision_available() else None


				def 				__lowerCamelCase	(					self	:			Tuple  )		->  Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase  	=			LevitImageProcessingTester(self  )


				@property
				def 				__lowerCamelCase	(					self	:			Tuple  )		->  Optional[Any]:







											"""simple docstring"""




											return self.image_processor_tester.prepare_image_processor_dict()


				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  Any:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.image_processing_class(**self.image_processor_dict  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'image_mean'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'image_std'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'do_normalize'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'do_resize'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'do_center_crop'  )  )
											self.assertTrue(hasattr(UpperCAmelCase_				,     'size'  )  )


				def 				__lowerCamelCase	(					self	:			str  )		->  Optional[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.image_processing_class.from_dict(self.image_processor_dict  )
											self.assertEqual(image_processor.size				,     {'shortest_edge': 18}  )
											self.assertEqual(image_processor.crop_size				,     {'height': 18, 'width': 18}  )

											_lowerCAmelCase  	=			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 				__lowerCamelCase	(					self	:			Optional[int]  )		->  List[str]:







											"""simple docstring"""




											pass


				def 				__lowerCamelCase	(					self	:			Tuple  )		->  Optional[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.image_processing_class(**self.image_processor_dict  )
											# create random PIL images
											_lowerCAmelCase  	=			prepare_image_inputs(self.image_processor_tester				,     equal_resolution=UpperCAmelCase_  )
											for image in image_inputs:
																		self.assertIsInstance(UpperCAmelCase_				,     Image.Image  )

											# Test not batched input
											_lowerCAmelCase  	=			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  	=			image_processing(UpperCAmelCase_				,     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 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  Dict:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.image_processing_class(**self.image_processor_dict  )
											# create random numpy tensors
											_lowerCAmelCase  	=			prepare_image_inputs(self.image_processor_tester				,     equal_resolution=UpperCAmelCase_				,     numpify=UpperCAmelCase_  )
											for image in image_inputs:
																		self.assertIsInstance(UpperCAmelCase_				,     np.ndarray  )

											# Test not batched input
											_lowerCAmelCase  	=			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  	=			image_processing(UpperCAmelCase_				,     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 				__lowerCamelCase	(					self	:			Tuple  )		->  Union[str, Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			self.image_processing_class(**self.image_processor_dict  )
											# create random PyTorch tensors
											_lowerCAmelCase  	=			prepare_image_inputs(self.image_processor_tester				,     equal_resolution=UpperCAmelCase_				,     torchify=UpperCAmelCase_  )
											for image in image_inputs:
																		self.assertIsInstance(UpperCAmelCase_				,     torch.Tensor  )

											# Test not batched input
											_lowerCAmelCase  	=			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  	=			image_processing(UpperCAmelCase_				,     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'],
											    )				,     )








"""simple docstring"""



def 	__snake_case  (    SCREAMING_SNAKE_CASE:					str						):






							"""simple docstring"""



							_lowerCAmelCase  	=			0

							# if input_string is "aba" than new_input_string become "a|b|a"
							_lowerCAmelCase  	=			''
							_lowerCAmelCase  	=			''

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

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

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

														_lowerCAmelCase  	=			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  	=			j - k + 1  # noqa: E741
																					_lowerCAmelCase  	=			j + k - 1

														# update max_length and start position
														if max_length < length[j]:
																					_lowerCAmelCase  	=			length[j]
																					_lowerCAmelCase  	=			j

    # create that string
							_lowerCAmelCase  	=			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()






