

"""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						):


		def __init__(self :		Optional[Any]     ,     __SCREAMING_SNAKE_CASE :		List[Any]     ,     __SCREAMING_SNAKE_CASE :		int=7     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]=3     ,     __SCREAMING_SNAKE_CASE :		str=1_8     ,     __SCREAMING_SNAKE_CASE :		List[str]=3_0     ,     __SCREAMING_SNAKE_CASE :		Dict=4_0_0     ,     __SCREAMING_SNAKE_CASE :		List[str]=True     ,     __SCREAMING_SNAKE_CASE :		List[Any]=None     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]=2     ,     __SCREAMING_SNAKE_CASE :		Tuple=True     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=[0.5, 0.5, 0.5]     ,     __SCREAMING_SNAKE_CASE :		int=[0.5, 0.5, 0.5]     ,     ):
							A			=					size if size is not None else {"shortest_edge": 3_0}
							A			=					parent
							A			=					batch_size
							A			=					num_channels
							A			=					image_size
							A			=					min_resolution
							A			=					max_resolution
							A			=					do_resize
							A			=					size
							A			=					size_divisor
							A			=					do_normalize
							A			=					image_mean
							A			=					image_std


		def 					SCREAMING_SNAKE_CASE__   (self :		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,
							    "size_divisor": self.size_divisor,
							}


		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]     ,     __SCREAMING_SNAKE_CASE :		Any     ,     __SCREAMING_SNAKE_CASE :		Any=False):



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

												A			=					int((1_3_3_3 / 8_0_0) * size)
												if max(__A     ,     __A) > max_size:
																	A			=					max_size / max(__A     ,     __A)
																	A			=					newh * scale
																	A			=					neww * scale

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

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

							return expected_height, expected_width





@require_torch
@require_vision
class 			__UpperCamelCase (   UpperCamelCase__     ,		unittest.TestCase						):
		SCREAMING_SNAKE_CASE								=    ViltImageProcessor if is_vision_available() else None


		def 					SCREAMING_SNAKE_CASE__   (self :		Any):
							A			=					ViltImageProcessingTester(self)


		@property
		def 					SCREAMING_SNAKE_CASE__   (self :		int):
							return self.image_processor_tester.prepare_image_processor_dict()


		def 					SCREAMING_SNAKE_CASE__   (self :		Tuple):
							A			=					self.image_processing_class(**self.image_processor_dict)
							self.assertTrue(hasattr(__A     ,     "image_mean"))
							self.assertTrue(hasattr(__A     ,     "image_std"))
							self.assertTrue(hasattr(__A     ,     "do_normalize"))
							self.assertTrue(hasattr(__A     ,     "do_resize"))
							self.assertTrue(hasattr(__A     ,     "size"))
							self.assertTrue(hasattr(__A     ,     "size_divisor"))


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[int]):
							A			=					self.image_processing_class.from_dict(self.image_processor_dict)
							self.assertEqual(image_processor.size     ,     {"shortest_edge": 3_0})

							A			=					self.image_processing_class.from_dict(self.image_processor_dict     ,     size=4_2)
							self.assertEqual(image_processor.size     ,     {"shortest_edge": 4_2})


		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[int]):
							pass


		def 					SCREAMING_SNAKE_CASE__   (self :		str):
							A			=					self.image_processing_class(**self.image_processor_dict)
							# create random PIL images
							A			=					prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=__A)
							for image in image_inputs:
												self.assertIsInstance(__A     ,     Image.Image)

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

							A			,    A			=					self.image_processor_tester.get_expected_values(__A)
							self.assertEqual(
							    encoded_images.shape     ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,     )

							# Test batched
							A			=					image_processing(__A     ,     return_tensors="pt").pixel_values

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


		def 					SCREAMING_SNAKE_CASE__   (self :		Any):
							A			=					self.image_processing_class(**self.image_processor_dict)
							# create random numpy tensors
							A			=					prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=__A     ,     numpify=__A)
							for image in image_inputs:
												self.assertIsInstance(__A     ,     np.ndarray)

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

							A			,    A			=					self.image_processor_tester.get_expected_values(__A)
							self.assertEqual(
							    encoded_images.shape     ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,     )

							# Test batched
							A			=					image_processing(__A     ,     return_tensors="pt").pixel_values

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


		def 					SCREAMING_SNAKE_CASE__   (self :		Union[str, Any]):
							A			=					self.image_processing_class(**self.image_processor_dict)
							# create random PyTorch tensors
							A			=					prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=__A     ,     torchify=__A)
							for image in image_inputs:
												self.assertIsInstance(__A     ,     torch.Tensor)

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

							A			,    A			=					self.image_processor_tester.get_expected_values(__A)
							self.assertEqual(
							    encoded_images.shape     ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,     )

							# Test batched
							A			=					image_processing(__A     ,     return_tensors="pt").pixel_values

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












def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							str			):
		SCREAMING_SNAKE_CASE__								=					SCREAMING_SNAKE_CASE__								=					0
		for letter in data:
				SCREAMING_SNAKE_CASE__								=					(hash_ << 4) + ord(UpperCamelCase__			)
				SCREAMING_SNAKE_CASE__								=					hash_ & 0XF_0_0_0_0_0_0_0
				if x != 0:
						hash_ ^= x >> 24
				hash_ &= ~x
		return hash_


if __name__ == "__main__":
	import doctest

	doctest.testmod()