





"""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  __lowerCAmelCase	(      unittest.TestCase					):






			def __init__(self ,							__magic_name__ ,							__magic_name__=7 ,							__magic_name__=3 ,							__magic_name__=18 ,							__magic_name__=30 ,							__magic_name__=400 ,							__magic_name__=True ,							__magic_name__=None ,							__magic_name__=True ,							__magic_name__=None ,							__magic_name__=True ,							__magic_name__=[0.5, 0.5, 0.5] ,							__magic_name__=[0.5, 0.5, 0.5] ,							)					->       Union[str, Any]:


							'''simple docstring'''



							snake_case_						:   Any           =  size if size is not None else {'''shortest_edge''': 18}
							snake_case_						:   Tuple           =  crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
							snake_case_						:   Any           =  parent
							snake_case_						:   Optional[Any]           =  batch_size
							snake_case_						:   Optional[int]           =  num_channels
							snake_case_						:   int           =  image_size
							snake_case_						:   Any           =  min_resolution
							snake_case_						:   List[Any]           =  max_resolution
							snake_case_						:   Union[str, Any]           =  do_resize
							snake_case_						:   List[str]           =  size
							snake_case_						:   int           =  do_center_crop
							snake_case_						:   Dict           =  crop_size
							snake_case_						:   Any           =  do_normalize
							snake_case_						:   List[str]           =  image_mean
							snake_case_						:   Union[str, Any]           =  image_std






			def       lowerCamelCase  (self   )					->       Tuple:


							'''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  __lowerCAmelCase	(      A__,					unittest.TestCase					):
			lowerCamelCase_					:					Union[str, Any]	      =				LevitImageProcessor if is_vision_available() else None






			def       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  LevitImageProcessingTester(self   )






			@property
			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()






			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							snake_case_						:   int           =  self.image_processing_class(**self.image_processor_dict   )
							self.assertTrue(hasattr(lowerCamelCase__ ,							'''image_mean'''   )   )
							self.assertTrue(hasattr(lowerCamelCase__ ,							'''image_std'''   )   )
							self.assertTrue(hasattr(lowerCamelCase__ ,							'''do_normalize'''   )   )
							self.assertTrue(hasattr(lowerCamelCase__ ,							'''do_resize'''   )   )
							self.assertTrue(hasattr(lowerCamelCase__ ,							'''do_center_crop'''   )   )
							self.assertTrue(hasattr(lowerCamelCase__ ,							'''size'''   )   )






			def       lowerCamelCase  (self   )					->       Tuple:


							'''simple docstring'''



							snake_case_						:   str           =  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}   )

							snake_case_						:   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       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							pass






			def       lowerCamelCase  (self   )					->       Optional[int]:


							'''simple docstring'''



							snake_case_						:   str           =  self.image_processing_class(**self.image_processor_dict   )
							# create random PIL images
							snake_case_						:   Optional[int]           =  prepare_image_inputs(self.image_processor_tester ,							equal_resolution=lowerCamelCase__   )
							for image in image_inputs:
											self.assertIsInstance(lowerCamelCase__ ,							Image.Image   )

							# Test not batched input
							snake_case_						:   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
							snake_case_						:   Any           =  image_processing(lowerCamelCase__ ,							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   )					->       Optional[int]:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  self.image_processing_class(**self.image_processor_dict   )
							# create random numpy tensors
							snake_case_						:   Any           =  prepare_image_inputs(self.image_processor_tester ,							equal_resolution=lowerCamelCase__ ,							numpify=lowerCamelCase__   )
							for image in image_inputs:
											self.assertIsInstance(lowerCamelCase__ ,							np.ndarray   )

							# Test not batched input
							snake_case_						:   Any           =  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
							snake_case_						:   Tuple           =  image_processing(lowerCamelCase__ ,							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   )					->       Optional[int]:


							'''simple docstring'''



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

							# Test not batched input
							snake_case_						:   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
							snake_case_						:   int           =  image_processing(lowerCamelCase__ ,							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_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				if not isinstance(_UpperCamelCase			,    _UpperCamelCase			):
								raise ValueError('''multiplicative_persistence() only accepts integral values'''			)
				if num < 0:
								raise ValueError('''multiplicative_persistence() does not accept negative values'''			)

				snake_case_						:   List[Any]           =  0
				snake_case_						:   Tuple           =  str(_UpperCamelCase			)

				while len(_UpperCamelCase			) != 1:
								snake_case_						:   Tuple           =  [int(_UpperCamelCase			) for i in num_string]

								snake_case_						:   Dict           =  1
								for i in range(0			,    len(_UpperCamelCase			)			):
												total *= numbers[i]

								snake_case_						:   str           =  str(_UpperCamelCase			)

								steps += 1
				return steps






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				if not isinstance(_UpperCamelCase			,    _UpperCamelCase			):
								raise ValueError('''additive_persistence() only accepts integral values'''			)
				if num < 0:
								raise ValueError('''additive_persistence() does not accept negative values'''			)

				snake_case_						:   Any           =  0
				snake_case_						:   Tuple           =  str(_UpperCamelCase			)

				while len(_UpperCamelCase			) != 1:
								snake_case_						:   List[str]           =  [int(_UpperCamelCase			) for i in num_string]

								snake_case_						:   Optional[int]           =  0
								for i in range(0			,    len(_UpperCamelCase			)			):
												total += numbers[i]

								snake_case_						:   Tuple           =  str(_UpperCamelCase			)

								steps += 1
				return steps


if __name__ == "__main__":
	import doctest

	doctest.testmod()
