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


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import CLIPImageProcessor



class    lowercase_    (     unittest.TestCase		):



							def __init__(						self      ,				lowercase_      ,				lowercase_=7      ,				lowercase_=3      ,				lowercase_=18      ,				lowercase_=30      ,				lowercase_=400      ,				lowercase_=True      ,				lowercase_=None      ,				lowercase_=True      ,				lowercase_=None      ,				lowercase_=True      ,				lowercase_=[0.48_145_466, 0.4_578_275, 0.40_821_073]      ,				lowercase_=[0.26_862_954, 0.26_130_258, 0.27_577_711]      ,				lowercase_=True      ,				):
													_snake_case						:     Optional[Any]				=    size if size is not None else {"shortest_edge": 20}
													_snake_case						:     List[str]				=    crop_size if crop_size is not None else {"height": 18, "width": 18}
													_snake_case						:     Union[str, Any]				=    parent
													_snake_case						:     str				=    batch_size
													_snake_case						:     Optional[int]				=    num_channels
													_snake_case						:     str				=    image_size
													_snake_case						:     str				=    min_resolution
													_snake_case						:     Any				=    max_resolution
													_snake_case						:     Union[str, Any]				=    do_resize
													_snake_case						:     Optional[Any]				=    size
													_snake_case						:     List[Any]				=    do_center_crop
													_snake_case						:     List[Any]				=    crop_size
													_snake_case						:     Optional[Any]				=    do_normalize
													_snake_case						:     Any				=    image_mean
													_snake_case						:     List[str]				=    image_std
													_snake_case						:     Optional[Any]				=    do_convert_rgb



							def        UpperCamelCase       (						self				):
													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,
													}



							def        UpperCamelCase       (						self      ,				lowercase_=False      ,				lowercase_=False      ,				lowercase_=False				):
													assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

													if equal_resolution:
																			_snake_case						:     Optional[Any]				=    []
																			for i in range(self.batch_size				):
																									image_inputs.append(
																									    np.random.randint(
																									        255      ,				size=(self.num_channels, self.max_resolution, self.max_resolution)      ,				dtype=np.uinta				)				)
													else:
																			_snake_case						:     Optional[Any]				=    []
																			for i in range(self.batch_size				):
																									_snake_case				,_snake_case						:     str				=    np.random.choice(np.arange(self.min_resolution      ,				self.max_resolution				)      ,				2				)
																									image_inputs.append(np.random.randint(255      ,				size=(self.num_channels, width, height)      ,				dtype=np.uinta				)				)

													if not numpify and not torchify:
																			# PIL expects the channel dimension as last dimension
																			_snake_case						:     List[str]				=    [Image.fromarray(np.moveaxis(lowercase_      ,				0      ,				-1				)				) for x in image_inputs]

													if torchify:
																			_snake_case						:     Optional[Any]				=    [torch.from_numpy(lowercase_				) for x in image_inputs]

													return image_inputs




@require_torch
@require_vision
class    lowercase_    (     __snake_case						, unittest.TestCase		):
							_lowerCamelCase						=		CLIPImageProcessor if is_vision_available() else None



							def        UpperCamelCase       (						self				):
													_snake_case						:     int				=    CLIPImageProcessingTester(self				)



							@property
							def        UpperCamelCase       (						self				):
													return self.image_processor_tester.prepare_image_processor_dict()



							def        UpperCamelCase       (						self				):
													_snake_case						:     str				=    self.image_processing_class(**self.image_processor_dict				)
													self.assertTrue(hasattr(lowercase_      ,				"do_resize"				)				)
													self.assertTrue(hasattr(lowercase_      ,				"size"				)				)
													self.assertTrue(hasattr(lowercase_      ,				"do_center_crop"				)				)
													self.assertTrue(hasattr(lowercase_      ,				"center_crop"				)				)
													self.assertTrue(hasattr(lowercase_      ,				"do_normalize"				)				)
													self.assertTrue(hasattr(lowercase_      ,				"image_mean"				)				)
													self.assertTrue(hasattr(lowercase_      ,				"image_std"				)				)
													self.assertTrue(hasattr(lowercase_      ,				"do_convert_rgb"				)				)



							def        UpperCamelCase       (						self				):
													_snake_case						:     Dict				=    self.image_processing_class.from_dict(self.image_processor_dict				)
													self.assertEqual(image_processor.size      ,				{"shortest_edge": 20}				)
													self.assertEqual(image_processor.crop_size      ,				{"height": 18, "width": 18}				)

													_snake_case						:     Union[str, 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        UpperCamelCase       (						self				):
													pass



							def        UpperCamelCase       (						self				):
													# Initialize image_processing
													_snake_case						:     str				=    self.image_processing_class(**self.image_processor_dict				)
													# create random PIL images
													_snake_case						:     Optional[Any]				=    self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_				)
													for image in image_inputs:
																			self.assertIsInstance(lowercase_      ,				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						:     str				=    image_processing(lowercase_      ,				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        UpperCamelCase       (						self				):
													# Initialize image_processing
													_snake_case						:     Any				=    self.image_processing_class(**self.image_processor_dict				)
													# create random numpy tensors
													_snake_case						:     Union[str, Any]				=    self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_      ,				numpify=lowercase_				)
													for image in image_inputs:
																			self.assertIsInstance(lowercase_      ,				np.ndarray				)

													# Test not batched input
													_snake_case						:     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
													_snake_case						:     Dict				=    image_processing(lowercase_      ,				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        UpperCamelCase       (						self				):
													# Initialize image_processing
													_snake_case						:     Dict				=    self.image_processing_class(**self.image_processor_dict				)
													# create random PyTorch tensors
													_snake_case						:     str				=    self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_      ,				torchify=lowercase_				)
													for image in image_inputs:
																			self.assertIsInstance(lowercase_      ,				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						:     str				=    image_processing(lowercase_      ,				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"],
													    )      ,				)




@require_torch
@require_vision
class    lowercase_    (     __snake_case						, unittest.TestCase		):
							_lowerCamelCase						=		CLIPImageProcessor if is_vision_available() else None



							def        UpperCamelCase       (						self				):
													_snake_case						:     Optional[Any]				=    CLIPImageProcessingTester(self      ,				num_channels=4				)
													_snake_case						:     Optional[Any]				=    3



							@property
							def        UpperCamelCase       (						self				):
													return self.image_processor_tester.prepare_image_processor_dict()



							def        UpperCamelCase       (						self				):
													_snake_case						:     List[Any]				=    self.image_processing_class(**self.image_processor_dict				)
													self.assertTrue(hasattr(lowercase_      ,				"do_resize"				)				)
													self.assertTrue(hasattr(lowercase_      ,				"size"				)				)
													self.assertTrue(hasattr(lowercase_      ,				"do_center_crop"				)				)
													self.assertTrue(hasattr(lowercase_      ,				"center_crop"				)				)
													self.assertTrue(hasattr(lowercase_      ,				"do_normalize"				)				)
													self.assertTrue(hasattr(lowercase_      ,				"image_mean"				)				)
													self.assertTrue(hasattr(lowercase_      ,				"image_std"				)				)
													self.assertTrue(hasattr(lowercase_      ,				"do_convert_rgb"				)				)



							def        UpperCamelCase       (						self				):
													pass



							def        UpperCamelCase       (						self				):
													# Initialize image_processing
													_snake_case						:     List[Any]				=    self.image_processing_class(**self.image_processor_dict				)
													# create random PIL images
													_snake_case						:     Any				=    self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_				)
													for image in image_inputs:
																			self.assertIsInstance(lowercase_      ,				Image.Image				)

													# Test not batched input
													_snake_case						:     Union[str, Any]				=    image_processing(image_inputs[0]      ,				return_tensors="pt"				).pixel_values
													self.assertEqual(
													    encoded_images.shape      ,				(
													        1,
													        self.expected_encoded_image_num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )      ,				)

													# Test batched
													_snake_case						:     Tuple				=    image_processing(lowercase_      ,				return_tensors="pt"				).pixel_values
													self.assertEqual(
													    encoded_images.shape      ,				(
													        self.image_processor_tester.batch_size,
													        self.expected_encoded_image_num_channels,
													        self.image_processor_tester.crop_size["height"],
													        self.image_processor_tester.crop_size["width"],
													    )      ,				)
def 					snake_case						(__lowercase						)       ->       int:

						'''simple docstring'''



						if not isinstance(__lowercase					,			__lowercase						) or number < 0:
												raise ValueError("Input must be a non-negative integer"						)

						_snake_case						:     List[Any]				=    number & ~(number - 1)
						_snake_case						:     Union[str, Any]				=    0
						while intermediate:
												intermediate >>= 1
												index += 1
						return index - 1


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)