



"""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 OwlViTImageProcessor

class 			__SCREAMING_SNAKE_CASE      ( unittest.TestCase  ):


					'''simple docstring'''





					def __init__(			self				:    Any,    lowerCamelCase				:    Optional[Any],    lowerCamelCase				:    Dict=7,    lowerCamelCase				:    Dict=3,    lowerCamelCase				:    Optional[int]=18,    lowerCamelCase				:    int=30,    lowerCamelCase				:    Any=400,    lowerCamelCase				:    str=True,    lowerCamelCase				:    Dict=None,    lowerCamelCase				:    Optional[int]=True,    lowerCamelCase				:    List[str]=None,    lowerCamelCase				:    List[str]=True,    lowerCamelCase				:    Dict=[0.48_145_466, 0.4_578_275, 0.40_821_073],    lowerCamelCase				:    int=[0.26_862_954, 0.26_130_258, 0.27_577_711],    lowerCamelCase				:    Dict=True,    )->   Tuple:
								lowerCamelCase__						:						str						      =parent
								lowerCamelCase__						:						Dict						      =batch_size
								lowerCamelCase__						:						Optional[Any]						      =num_channels
								lowerCamelCase__						:						List[str]						      =image_size
								lowerCamelCase__						:						Tuple						      =min_resolution
								lowerCamelCase__						:						Dict						      =max_resolution
								lowerCamelCase__						:						Tuple						      =do_resize
								lowerCamelCase__						:						List[str]						      =size if size is not None else {'''height''': 18, '''width''': 18}
								lowerCamelCase__						:						Optional[Any]						      =do_center_crop
								lowerCamelCase__						:						int						      =crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
								lowerCamelCase__						:						Dict						      =do_normalize
								lowerCamelCase__						:						List[Any]						      =image_mean
								lowerCamelCase__						:						Tuple						      =image_std
								lowerCamelCase__						:						Any						      =do_convert_rgb




					def 						snake_case			(			self				:    Optional[int]	)->   Optional[int]:
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_convert_rgb": self.do_convert_rgb,
								}





@require_torch
@require_vision
class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_						,  unittest.TestCase  ):


					'''simple docstring'''





					_a       =				OwlViTImageProcessor if is_vision_available() else None



					def 						snake_case			(			self				:    Optional[Any]	)->   Tuple:
								lowerCamelCase__						:						List[Any]						      =OwlViTImageProcessingTester(self	)



					@property
					def 						snake_case			(			self				:    int	)->   List[Any]:
								return self.image_processor_tester.prepare_image_processor_dict()



					def 						snake_case			(			self				:    Tuple	)->   int:
								lowerCamelCase__						:						str						      =self.image_processing_class(**self.image_processor_dict	)
								self.assertTrue(hasattr(lowerCamelCase,    '''do_resize'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''size'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''do_center_crop'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''center_crop'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''do_normalize'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''image_mean'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''image_std'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''do_convert_rgb'''	)	)



					def 						snake_case			(			self				:    List[Any]	)->   str:
								lowerCamelCase__						:						Any						      =self.image_processing_class.from_dict(self.image_processor_dict	)
								self.assertEqual(image_processor.size,    {'''height''': 18, '''width''': 18}	)
								self.assertEqual(image_processor.crop_size,    {'''height''': 18, '''width''': 18}	)

								lowerCamelCase__						:						Any						      =self.image_processing_class.from_dict(self.image_processor_dict,    size=42,    crop_size=84	)
								self.assertEqual(image_processor.size,    {'''height''': 42, '''width''': 42}	)
								self.assertEqual(image_processor.crop_size,    {'''height''': 84, '''width''': 84}	)



					def 						snake_case			(			self				:    Optional[Any]	)->   Union[str, Any]:
								# Initialize image_processing
								lowerCamelCase__						:						Dict						      =self.image_processing_class(**self.image_processor_dict	)
								# create random PIL images
								lowerCamelCase__						:						Optional[Any]						      =prepare_image_inputs(self.image_processor_tester,    equal_resolution=lowerCamelCase	)

								for image in image_inputs:
											self.assertIsInstance(lowerCamelCase,    Image.Image	)

								# 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__						:						Dict						      =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 						snake_case			(			self				:    Optional[Any]	)->   Dict:
								# Initialize image_processing
								lowerCamelCase__						:						int						      =self.image_processing_class(**self.image_processor_dict	)
								# create random numpy tensors
								lowerCamelCase__						:						Dict						      =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
								lowerCamelCase__						:						Optional[int]						      =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[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 						snake_case			(			self				:    Union[str, Any]	)->   Tuple:
								# Initialize image_processing
								lowerCamelCase__						:						Optional[Any]						      =self.image_processing_class(**self.image_processor_dict	)
								# create random PyTorch tensors
								lowerCamelCase__						:						int						      =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
								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(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'''],
								    ),    )









'''simple docstring'''

from __future__ import annotations

_A					:     Union[str, Any]      ='''Alexander Joslin'''







def     __UpperCamelCase					(				_lowercase       )			->      int:

	if number <= 0:
		_lowercase       :							Optional[Any]  	=		f'''n must be greater than 0. Got n = {number}'''
		raise ValueError(_lowercase       )

	_lowercase       :							List[str]  	=		[number + 1] * (number + 1)

	# starting position
	_lowercase       :							str  	=		0
	for i in range(1,					_lowercase       ):
		_lowercase       :							Union[str, Any]  	=		min(table[i + 1],					table[i] + 1       )
		# check if out of bounds
		if i * 2 <= number:
			_lowercase       :							Optional[Any]  	=		min(table[i * 2],					table[i] + 1       )
		# check if out of bounds
		if i * 3 <= number:
			_lowercase       :							Any  	=		min(table[i * 3],					table[i] + 1       )
	return table[number]


if __name__ == "__main__":
		import doctest

		doctest.testmod()
