
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_video_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import VideoMAEImageProcessor
class 			lowercase__(    unittest.TestCase							):



	"""simple docstring"""




	def __init__(	self  :      str	,							SCREAMING_SNAKE_CASE_  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      List[Any]=7	,							SCREAMING_SNAKE_CASE_  :      Any=3	,							SCREAMING_SNAKE_CASE_  :      List[str]=1_0	,							SCREAMING_SNAKE_CASE_  :      Any=1_8	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=3_0	,							SCREAMING_SNAKE_CASE_  :      Any=4_0_0	,							SCREAMING_SNAKE_CASE_  :      Optional[Any]=True	,							SCREAMING_SNAKE_CASE_  :      List[str]=None	,							SCREAMING_SNAKE_CASE_  :      List[Any]=True	,							SCREAMING_SNAKE_CASE_  :      int=[0.5, 0.5, 0.5]	,							SCREAMING_SNAKE_CASE_  :      str=[0.5, 0.5, 0.5]	,							SCREAMING_SNAKE_CASE_  :      Dict=None	,							)	->					str:
					lowercase_					  = size if size is not None else {'''shortest_edge''': 1_8}
					lowercase_					  = crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}

					lowercase_					  = parent
					lowercase_					  = batch_size
					lowercase_					  = num_channels
					lowercase_					  = num_frames
					lowercase_					  = image_size
					lowercase_					  = min_resolution
					lowercase_					  = max_resolution
					lowercase_					  = do_resize
					lowercase_					  = size
					lowercase_					  = do_normalize
					lowercase_					  = image_mean
					lowercase_					  = image_std
					lowercase_					  = crop_size




	def 						_lowercase  (	self  :      List[str]	)	->					Optional[Any]:
					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "crop_size": self.crop_size,
					}

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



	"""simple docstring"""




	a      :List[str]   					=     VideoMAEImageProcessor if is_vision_available() else None
	def 						_lowercase  (	self  :      Union[str, Any]	)	->					int:
					lowercase_					  = VideoMAEImageProcessingTester(self	)
	@property
	def 						_lowercase  (	self  :      Optional[Any]	)	->					Any:
					return self.image_processor_tester.prepare_image_processor_dict()
	def 						_lowercase  (	self  :      int	)	->					str:
					lowercase_					  = self.image_processing_class(**self.image_processor_dict	)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,							'''image_mean'''	)	)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,							'''image_std'''	)	)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,							'''do_normalize'''	)	)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,							'''do_resize'''	)	)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,							'''do_center_crop'''	)	)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,							'''size'''	)	)
	def 						_lowercase  (	self  :      Optional[Any]	)	->					Dict:
					lowercase_					  = self.image_processing_class.from_dict(self.image_processor_dict	)
					self.assertEqual(image_processor.size	,							{'''shortest_edge''': 1_8}	)
					self.assertEqual(image_processor.crop_size	,							{'''height''': 1_8, '''width''': 1_8}	)

					lowercase_					  = self.image_processing_class.from_dict(self.image_processor_dict	,							size=4_2	,							crop_size=8_4	)
					self.assertEqual(image_processor.size	,							{'''shortest_edge''': 4_2}	)
					self.assertEqual(image_processor.crop_size	,							{'''height''': 8_4, '''width''': 8_4}	)
	def 						_lowercase  (	self  :      List[str]	)	->					List[str]:
					pass
	def 						_lowercase  (	self  :      Dict	)	->					Union[str, Any]:
					# Initialize image_processing
					lowercase_					  = self.image_processing_class(**self.image_processor_dict	)
					# create random PIL videos
					lowercase_					  = prepare_video_inputs(self.image_processor_tester	,							equal_resolution=SCREAMING_SNAKE_CASE_	)
					for video in video_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	)
									self.assertIsInstance(video[0]	,							Image.Image	)

					# Test not batched input
					lowercase_					  = image_processing(video_inputs[0]	,							return_tensors='''pt'''	).pixel_values
					self.assertEqual(
					    encoded_videos.shape	,							(
					        1,
					        self.image_processor_tester.num_frames,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['''height'''],
					        self.image_processor_tester.crop_size['''width'''],
					    )	,							)

					# Test batched
					lowercase_					  = image_processing(SCREAMING_SNAKE_CASE_	,							return_tensors='''pt'''	).pixel_values
					self.assertEqual(
					    encoded_videos.shape	,							(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_frames,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['''height'''],
					        self.image_processor_tester.crop_size['''width'''],
					    )	,							)
	def 						_lowercase  (	self  :      Union[str, Any]	)	->					Tuple:
					# Initialize image_processing
					lowercase_					  = self.image_processing_class(**self.image_processor_dict	)
					# create random numpy tensors
					lowercase_					  = prepare_video_inputs(self.image_processor_tester	,							equal_resolution=SCREAMING_SNAKE_CASE_	,							numpify=SCREAMING_SNAKE_CASE_	)
					for video in video_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	)
									self.assertIsInstance(video[0]	,							np.ndarray	)

					# Test not batched input
					lowercase_					  = image_processing(video_inputs[0]	,							return_tensors='''pt'''	).pixel_values
					self.assertEqual(
					    encoded_videos.shape	,							(
					        1,
					        self.image_processor_tester.num_frames,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['''height'''],
					        self.image_processor_tester.crop_size['''width'''],
					    )	,							)

					# Test batched
					lowercase_					  = image_processing(SCREAMING_SNAKE_CASE_	,							return_tensors='''pt'''	).pixel_values
					self.assertEqual(
					    encoded_videos.shape	,							(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_frames,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['''height'''],
					        self.image_processor_tester.crop_size['''width'''],
					    )	,							)




	def 						_lowercase  (	self  :      Tuple	)	->					List[Any]:
					# Initialize image_processing
					lowercase_					  = self.image_processing_class(**self.image_processor_dict	)
					# create random PyTorch tensors
					lowercase_					  = prepare_video_inputs(self.image_processor_tester	,							equal_resolution=SCREAMING_SNAKE_CASE_	,							torchify=SCREAMING_SNAKE_CASE_	)
					for video in video_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	)
									self.assertIsInstance(video[0]	,							torch.Tensor	)

					# Test not batched input
					lowercase_					  = image_processing(video_inputs[0]	,							return_tensors='''pt'''	).pixel_values
					self.assertEqual(
					    encoded_videos.shape	,							(
					        1,
					        self.image_processor_tester.num_frames,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['''height'''],
					        self.image_processor_tester.crop_size['''width'''],
					    )	,							)

					# Test batched
					lowercase_					  = image_processing(SCREAMING_SNAKE_CASE_	,							return_tensors='''pt'''	).pixel_values
					self.assertEqual(
					    encoded_videos.shape	,							(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_frames,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size['''height'''],
					        self.image_processor_tester.crop_size['''width'''],
					    )	,							)






from __future__ import annotations

__a        = 'Alexander Joslin'




def     a						(							snake_case__: int ):


				'''simple docstring'''

				if number <= 0:
								lowercase_					  = F'''n must be greater than 0. Got n = {number}'''
								raise ValueError(snake_case__ )

				lowercase_					  = [number + 1] * (number + 1)

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


if __name__ == "__main__":
						import doctest

						doctest.testmod()




