
"""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 ViltImageProcessor




class      UpperCamelCase	(       unittest.TestCase      ):

				"""simple docstring"""



				def __init__(				self		,	lowercase__		,	lowercase__=7		,	lowercase__=3		,	lowercase__=18		,	lowercase__=30		,	lowercase__=400		,	lowercase__=True		,	lowercase__=None		,	lowercase__=2		,	lowercase__=True		,	lowercase__=[0.5, 0.5, 0.5]		,	lowercase__=[0.5, 0.5, 0.5]		,	)    ->    Optional[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	size if size is not None else {'shortest_edge': 30}
									SCREAMING_SNAKE_CASE				=	parent
									SCREAMING_SNAKE_CASE				=	batch_size
									SCREAMING_SNAKE_CASE				=	num_channels
									SCREAMING_SNAKE_CASE				=	image_size
									SCREAMING_SNAKE_CASE				=	min_resolution
									SCREAMING_SNAKE_CASE				=	max_resolution
									SCREAMING_SNAKE_CASE				=	do_resize
									SCREAMING_SNAKE_CASE				=	size
									SCREAMING_SNAKE_CASE				=	size_divisor
									SCREAMING_SNAKE_CASE				=	do_normalize
									SCREAMING_SNAKE_CASE				=	image_mean
									SCREAMING_SNAKE_CASE				=	image_std


				def 							A					(				self			)    ->    Union[str, Any]:
									"""simple docstring"""







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




				def 							A					(				self		,	lowercase__		,	lowercase__=False			)    ->    Optional[int]:
									"""simple docstring"""







									if not batched:
														SCREAMING_SNAKE_CASE				=	self.size['shortest_edge']
														SCREAMING_SNAKE_CASE				=	image_inputs[0]
														if isinstance(lowercase__		,	Image.Image			):
																			SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	image.size
														else:
																			SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	image.shape[1], image.shape[2]
														SCREAMING_SNAKE_CASE				=	size / min(lowercase__		,	lowercase__			)
														if h < w:
																			SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	size, scale * w
														else:
																			SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	scale * h, size

														SCREAMING_SNAKE_CASE				=	int((1333 / 800) * size			)
														if max(lowercase__		,	lowercase__			) > max_size:
																			SCREAMING_SNAKE_CASE				=	max_size / max(lowercase__		,	lowercase__			)
																			SCREAMING_SNAKE_CASE				=	newh * scale
																			SCREAMING_SNAKE_CASE				=	neww * scale

														SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	int(newh + 0.5			), int(neww + 0.5			)
														SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	(
														    newh // self.size_divisor * self.size_divisor,
														    neww // self.size_divisor * self.size_divisor,
														)

									else:
														SCREAMING_SNAKE_CASE				=	[]
														for image in image_inputs:
																			SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	self.get_expected_values([image]			)
																			expected_values.append((expected_height, expected_width)			)
														SCREAMING_SNAKE_CASE				=	max(lowercase__		,	key=lambda lowercase__			: item[0]			)[0]
														SCREAMING_SNAKE_CASE				=	max(lowercase__		,	key=lambda lowercase__			: item[1]			)[1]

									return expected_height, expected_width






@require_torch
@require_vision
class      UpperCamelCase	(       __magic_name__  ,      unittest.TestCase      ):

				"""simple docstring"""



				UpperCAmelCase_				:      List[Any]						  =				ViltImageProcessor if is_vision_available() else None


				def 							A					(				self			)    ->    Dict:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	ViltImageProcessingTester(self			)


				@property
				def 							A					(				self			)    ->    List[Any]:
									"""simple docstring"""







									return self.image_processor_tester.prepare_image_processor_dict()


				def 							A					(				self			)    ->    Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self.image_processing_class(**self.image_processor_dict			)
									self.assertTrue(hasattr(lowercase__		,	'image_mean'			)			)
									self.assertTrue(hasattr(lowercase__		,	'image_std'			)			)
									self.assertTrue(hasattr(lowercase__		,	'do_normalize'			)			)
									self.assertTrue(hasattr(lowercase__		,	'do_resize'			)			)
									self.assertTrue(hasattr(lowercase__		,	'size'			)			)
									self.assertTrue(hasattr(lowercase__		,	'size_divisor'			)			)


				def 							A					(				self			)    ->    Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	self.image_processing_class.from_dict(self.image_processor_dict			)
									self.assertEqual(image_processor.size		,	{'shortest_edge': 30}			)

									SCREAMING_SNAKE_CASE				=	self.image_processing_class.from_dict(self.image_processor_dict		,	size=42			)
									self.assertEqual(image_processor.size		,	{'shortest_edge': 42}			)


				def 							A					(				self			)    ->    str:
									"""simple docstring"""







									pass


				def 							A					(				self			)    ->    str:
									"""simple docstring"""







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

									# Test not batched input
									SCREAMING_SNAKE_CASE				=	image_processing(image_inputs[0]		,	return_tensors='pt'			).pixel_values

									SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	self.image_processor_tester.get_expected_values(lowercase__			)
									self.assertEqual(
									    encoded_images.shape		,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)		,	)

									# Test batched
									SCREAMING_SNAKE_CASE				=	image_processing(lowercase__		,	return_tensors='pt'			).pixel_values

									SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	self.image_processor_tester.get_expected_values(lowercase__		,	batched=lowercase__			)
									self.assertEqual(
									    encoded_images.shape		,	(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )		,	)


				def 							A					(				self			)    ->    int:
									"""simple docstring"""







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

									# Test not batched input
									SCREAMING_SNAKE_CASE				=	image_processing(image_inputs[0]		,	return_tensors='pt'			).pixel_values

									SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	self.image_processor_tester.get_expected_values(lowercase__			)
									self.assertEqual(
									    encoded_images.shape		,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)		,	)

									# Test batched
									SCREAMING_SNAKE_CASE				=	image_processing(lowercase__		,	return_tensors='pt'			).pixel_values

									SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	self.image_processor_tester.get_expected_values(lowercase__		,	batched=lowercase__			)
									self.assertEqual(
									    encoded_images.shape		,	(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )		,	)




				def 							A					(				self			)    ->    Dict:
									"""simple docstring"""







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

									# Test not batched input
									SCREAMING_SNAKE_CASE				=	image_processing(image_inputs[0]		,	return_tensors='pt'			).pixel_values

									SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	self.image_processor_tester.get_expected_values(lowercase__			)
									self.assertEqual(
									    encoded_images.shape		,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)		,	)

									# Test batched
									SCREAMING_SNAKE_CASE				=	image_processing(lowercase__		,	return_tensors='pt'			).pixel_values

									SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE				=	self.image_processor_tester.get_expected_values(lowercase__		,	batched=lowercase__			)
									self.assertEqual(
									    encoded_images.shape		,	(
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )		,	)


"""simple docstring"""




from functools import lru_cache

@lru_cache
def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					if not isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										raise TypeError('k must be an integer.'			)
					if k < 0:
										raise ValueError('k integer must be greater or equal to zero.'			)
					if k == 0:
										return 0
					elif k == 1:
										return 1
					else:
										return fibonacci(k - 1			) + fibonacci(k - 2			)

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	len(SCREAMING_SNAKE_CASE_			)
					# Find m such that F_m >= n where F_i is the i_th fibonacci number.
					SCREAMING_SNAKE_CASE				=	0
					while True:
										if fibonacci(SCREAMING_SNAKE_CASE_			) >= len_list:
															SCREAMING_SNAKE_CASE				=	i
															break
										i += 1
					SCREAMING_SNAKE_CASE				=	0
					while fibb_k > 0:
										SCREAMING_SNAKE_CASE				=	min(
										    offset + fibonacci(fibb_k - 1			),     len_list - 1			)  # Prevent out of range
										SCREAMING_SNAKE_CASE				=	arr[index_k]
										if item_k_1 == val:
															return index_k
										elif val < item_k_1:
															fibb_k -= 1
										elif val > item_k_1:
															offset += fibonacci(fibb_k - 1			)
															fibb_k -= 2
					else:
										return -1


if __name__ == "__main__":
	import doctest

	doctest.testmod()
