




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 DeiTImageProcessor





class 		A__				(   unittest.TestCase ):






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




						"""simple docstring"""






						__magic_name__					:							Any		   =							size if size is not None else {'''height''': 20, '''width''': 20}
						__magic_name__					:							List[str]		   =							crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

						__magic_name__					:							Union[str, Any]		   =							parent
						__magic_name__					:							int		   =							batch_size
						__magic_name__					:							Optional[Any]		   =							num_channels
						__magic_name__					:							Any		   =							image_size
						__magic_name__					:							Optional[Any]		   =							min_resolution
						__magic_name__					:							Any		   =							max_resolution
						__magic_name__					:							int		   =							do_resize
						__magic_name__					:							Optional[Any]		   =							size
						__magic_name__					:							Optional[Any]		   =							do_center_crop
						__magic_name__					:							Dict		   =							crop_size
						__magic_name__					:							int		   =							do_normalize
						__magic_name__					:							Tuple		   =							image_mean
						__magic_name__					:							Tuple		   =							image_std






	def  lowercase      (	self						)      ->   Any:




						"""simple docstring"""






						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,
						}





@require_torch
@require_vision
class 		A__				(   __SCREAMING_SNAKE_CASE		,     unittest.TestCase ):
	lowerCamelCase__		: Optional[int]				    =DeiTImageProcessor if is_vision_available() else None
	lowerCamelCase__		: List[Any]				    =True






	def  lowercase      (	self						)      ->   str:




						"""simple docstring"""






						__magic_name__					:							Optional[int]		   =							DeiTImageProcessingTester(self						)






	@property
	def  lowercase      (	self						)      ->   List[Any]:




						"""simple docstring"""






						return self.image_processor_tester.prepare_image_processor_dict()






	def  lowercase      (	self						)      ->   List[str]:




						"""simple docstring"""






						__magic_name__					:							List[Any]		   =							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'''						)						)






	def  lowercase      (	self						)      ->   Any:




						"""simple docstring"""






						__magic_name__					:							int		   =							self.image_processing_class.from_dict(self.image_processor_dict						)
						self.assertEqual(image_processor.size				,     {'''height''': 20, '''width''': 20}						)
						self.assertEqual(image_processor.crop_size				,     {'''height''': 18, '''width''': 18}						)

						__magic_name__					:							str		   =							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  lowercase      (	self						)      ->   List[str]:




						"""simple docstring"""






						pass






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




						"""simple docstring"""






						__magic_name__					:							Dict		   =							self.image_processing_class(**self.image_processor_dict						)
						# create random PIL images
						__magic_name__					:							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
						__magic_name__					:							List[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
						__magic_name__					:							Optional[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  lowercase      (	self						)      ->   Dict:




						"""simple docstring"""






						__magic_name__					:							int		   =							self.image_processing_class(**self.image_processor_dict						)
						# create random numpy tensors
						__magic_name__					:							Tuple		   =							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
						__magic_name__					:							List[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
						__magic_name__					:							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  lowercase      (	self						)      ->   Optional[int]:




						"""simple docstring"""






						__magic_name__					:							Optional[Any]		   =							self.image_processing_class(**self.image_processor_dict						)
						# create random PyTorch tensors
						__magic_name__					:							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
						__magic_name__					:							Optional[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
						__magic_name__					:							Union[str, 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'''],
						    )				,     )











"""simple docstring"""







import math
from decimal import Decimal, getcontext





def 						lowercase__(A = 4_000_000						)				->int:




						"""simple docstring"""



						try:
												lowercase__				:      int=       int(A						)
						except (TypeError, ValueError):
												raise TypeError("Parameter n must be int or castable to int."						)
						if n <= 0:
												raise ValueError("Parameter n must be greater than or equal to one."						)
						lowercase__				:      Dict=       100
						lowercase__				:      Optional[Any]=       (Decimal(5						) ** Decimal(0.5						) + 1) / Decimal(2						)

						lowercase__				:      str=       (math.floor(math.log(n * (phi + 2)					,		A						) - 1						) // 3) * 3 + 2
						lowercase__				:      Optional[Any]=       Decimal(round(phi ** Decimal(index + 1						)						)						) / (phi + 2)
						lowercase__				:      Tuple=       num // 2
						return int(A						)


if __name__ == "__main__":
			print(F"""{solution() = }""")






