





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]							, __A			:List[Any]							, __A			:int=7							, __A			:Optional[Any]=3							, __A			:str=18							, __A			:List[str]=30							, __A			:Dict=400							, __A			:List[str]=True							, __A			:List[Any]=None							, __A			:Optional[Any]=2							, __A			:Tuple=True							, __A			:Optional[int]=[0.5, 0.5, 0.5]							, __A			:int=[0.5, 0.5, 0.5]							, )      ->      Dict:



									"""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    _snake_case     (							self			:Any						)      ->      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    _snake_case     (							self			:Union[str, Any]							, __A			:Any							, __A			:Any=False						)      ->      Dict:



									"""simple docstring"""



									if not batched:
											SCREAMING_SNAKE_CASE__								=					self.size["""shortest_edge"""]
											SCREAMING_SNAKE_CASE__								=					image_inputs[0]
											if isinstance(__A							, 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(__A							, __A						)
											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(__A							, __A						) > max_size:
													SCREAMING_SNAKE_CASE__								=					max_size / max(__A							, __A						)
													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(__A							, key=lambda __A						: item[0]						)[0]
											SCREAMING_SNAKE_CASE__								=					max(__A							, key=lambda __A						: item[1]						)[1]

									return expected_height, expected_width




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


							def    _snake_case     (							self			:Any						)      ->      Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					ViltImageProcessingTester(self						)


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



									"""simple docstring"""



									return self.image_processor_tester.prepare_image_processor_dict()


							def    _snake_case     (							self			:Tuple						)      ->      Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					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    _snake_case     (							self			:Optional[int]						)      ->      Tuple:



									"""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    _snake_case     (							self			:Optional[int]						)      ->      Union[str, Any]:



									"""simple docstring"""



									pass


							def    _snake_case     (							self			:str						)      ->      int:



									"""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=__A						)
									for image in image_inputs:
											self.assertIsInstance(__A							, 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(__A						)
									self.assertEqual(
									    encoded_images.shape							, (1, self.image_processor_tester.num_channels, expected_height, expected_width)							, )

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

									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					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    _snake_case     (							self			:Any						)      ->      Union[str, Any]:



									"""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=__A							, numpify=__A						)
									for image in image_inputs:
											self.assertIsInstance(__A							, 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(__A						)
									self.assertEqual(
									    encoded_images.shape							, (1, self.image_processor_tester.num_channels, expected_height, expected_width)							, )

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

									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					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    _snake_case     (							self			:Union[str, Any]						)      ->      Optional[int]:



									"""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=__A							, torchify=__A						)
									for image in image_inputs:
											self.assertIsInstance(__A							, 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(__A						)
									self.assertEqual(
									    encoded_images.shape							, (1, self.image_processor_tester.num_channels, expected_height, expected_width)							, )

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

									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					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()