"""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


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import BlipImageProcessor
class     snake_case	(			unittest.TestCase    ):




					"""simple docstring"""

					def __init__(    self       :     Optional[int]   ,lowerCamelCase__       :     Optional[int]   ,lowerCamelCase__       :     int=7   ,lowerCamelCase__       :     Optional[int]=3   ,lowerCamelCase__       :     List[str]=18   ,lowerCamelCase__       :     List[Any]=30   ,lowerCamelCase__       :     Dict=400   ,lowerCamelCase__       :     Optional[int]=True   ,lowerCamelCase__       :     Tuple=None   ,lowerCamelCase__       :     Union[str, Any]=True   ,lowerCamelCase__       :     List[str]=False   ,lowerCamelCase__       :     List[str]=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]   ,lowerCamelCase__       :     int=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]   ,lowerCamelCase__       :     str=True   ,):
									UpperCAmelCase__					=							size if size is not None else {'height': 20, 'width': 20}
									UpperCAmelCase__					=							parent
									UpperCAmelCase__					=							batch_size
									UpperCAmelCase__					=							num_channels
									UpperCAmelCase__					=							image_size
									UpperCAmelCase__					=							min_resolution
									UpperCAmelCase__					=							max_resolution
									UpperCAmelCase__					=							do_resize
									UpperCAmelCase__					=							size
									UpperCAmelCase__					=							do_normalize
									UpperCAmelCase__					=							image_mean
									UpperCAmelCase__					=							image_std
									UpperCAmelCase__					=							do_pad
									UpperCAmelCase__					=							do_convert_rgb







					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_convert_rgb": self.do_convert_rgb,
									    "do_pad": self.do_pad,
									}







					def 		__lowerCAmelCase						(    self       :     Dict   ,lowerCamelCase__       :     str=False   ,lowerCamelCase__       :     Tuple=False   ,lowerCamelCase__       :     Union[str, Any]=False      ):
									assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

									if equal_resolution:
													UpperCAmelCase__					=							[]
													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:
													UpperCAmelCase__					=							[]
													for i in range(self.batch_size      ):
																	UpperCAmelCase__				,     UpperCAmelCase__					=							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
													UpperCAmelCase__					=							[Image.fromarray(np.moveaxis(lowerCamelCase__   ,0   ,-1      )      ) for x in image_inputs]

									if torchify:
													UpperCAmelCase__					=							[torch.from_numpy(lowerCamelCase__      ) for x in image_inputs]

									return image_inputs






@require_torch
@require_vision
class     snake_case	(			__UpperCAmelCase				,   unittest.TestCase    ):




					"""simple docstring"""

					snake_case__       =			BlipImageProcessor if is_vision_available() else None







					def 		__lowerCAmelCase						(    self       :     List[str]      ):
									UpperCAmelCase__					=							BlipImageProcessingTester(self      )







					@property
					def 		__lowerCAmelCase						(    self       :     Dict      ):
									return self.image_processor_tester.prepare_image_processor_dict()







					def 		__lowerCAmelCase						(    self       :     List[str]      ):
									UpperCAmelCase__					=							self.image_processing_class(**self.image_processor_dict      )
									self.assertTrue(hasattr(lowerCamelCase__   ,'do_resize'      )      )
									self.assertTrue(hasattr(lowerCamelCase__   ,'size'      )      )
									self.assertTrue(hasattr(lowerCamelCase__   ,'do_normalize'      )      )
									self.assertTrue(hasattr(lowerCamelCase__   ,'image_mean'      )      )
									self.assertTrue(hasattr(lowerCamelCase__   ,'image_std'      )      )
									self.assertTrue(hasattr(lowerCamelCase__   ,'do_convert_rgb'      )      )







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







					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									# Initialize image_processor
									UpperCAmelCase__					=							self.image_processing_class(**self.image_processor_dict      )
									# create random PIL images
									UpperCAmelCase__					=							self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__      )
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__   ,Image.Image      )

									# Test not batched input
									UpperCAmelCase__					=							image_processor(image_inputs[0]   ,return_tensors='pt'      ).pixel_values
									self.assertEqual(
									    encoded_images.shape   ,(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size['height'],
									        self.image_processor_tester.size['width'],
									    )   ,)

									# Test batched
									UpperCAmelCase__					=							image_processor(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.size['height'],
									        self.image_processor_tester.size['width'],
									    )   ,)







					def 		__lowerCAmelCase						(    self       :     List[Any]      ):
									# Initialize image_processor
									UpperCAmelCase__					=							self.image_processing_class(**self.image_processor_dict      )
									# create random numpy tensors
									UpperCAmelCase__					=							self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__   ,numpify=lowerCamelCase__      )
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__   ,np.ndarray      )

									# Test not batched input
									UpperCAmelCase__					=							image_processor(image_inputs[0]   ,return_tensors='pt'      ).pixel_values
									self.assertEqual(
									    encoded_images.shape   ,(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size['height'],
									        self.image_processor_tester.size['width'],
									    )   ,)

									# Test batched
									UpperCAmelCase__					=							image_processor(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.size['height'],
									        self.image_processor_tester.size['width'],
									    )   ,)







					def 		__lowerCAmelCase						(    self       :     int      ):
									# Initialize image_processor
									UpperCAmelCase__					=							self.image_processing_class(**self.image_processor_dict      )
									# create random PyTorch tensors
									UpperCAmelCase__					=							self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__   ,torchify=lowerCamelCase__      )
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__   ,torch.Tensor      )

									# Test not batched input
									UpperCAmelCase__					=							image_processor(image_inputs[0]   ,return_tensors='pt'      ).pixel_values
									self.assertEqual(
									    encoded_images.shape   ,(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size['height'],
									        self.image_processor_tester.size['width'],
									    )   ,)

									# Test batched
									UpperCAmelCase__					=							image_processor(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.size['height'],
									        self.image_processor_tester.size['width'],
									    )   ,)






@require_torch
@require_vision
class     snake_case	(			__UpperCAmelCase				,   unittest.TestCase    ):




					"""simple docstring"""

					snake_case__       =			BlipImageProcessor if is_vision_available() else None







					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									UpperCAmelCase__					=							BlipImageProcessingTester(self   ,num_channels=4      )
									UpperCAmelCase__					=							3







					@property
					def 		__lowerCAmelCase						(    self       :     Union[str, Any]      ):
									return self.image_processor_tester.prepare_image_processor_dict()







					def 		__lowerCAmelCase						(    self       :     str      ):
									UpperCAmelCase__					=							self.image_processing_class(**self.image_processor_dict      )
									self.assertTrue(hasattr(lowerCamelCase__   ,'do_resize'      )      )
									self.assertTrue(hasattr(lowerCamelCase__   ,'size'      )      )
									self.assertTrue(hasattr(lowerCamelCase__   ,'do_normalize'      )      )
									self.assertTrue(hasattr(lowerCamelCase__   ,'image_mean'      )      )
									self.assertTrue(hasattr(lowerCamelCase__   ,'image_std'      )      )
									self.assertTrue(hasattr(lowerCamelCase__   ,'do_convert_rgb'      )      )







					def 		__lowerCAmelCase						(    self       :     Union[str, Any]      ):
									pass







					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									# Initialize image_processor
									UpperCAmelCase__					=							self.image_processing_class(**self.image_processor_dict      )
									# create random PIL images
									UpperCAmelCase__					=							self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__      )
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__   ,Image.Image      )

									# Test not batched input
									UpperCAmelCase__					=							image_processor(image_inputs[0]   ,return_tensors='pt'      ).pixel_values
									self.assertEqual(
									    encoded_images.shape   ,(
									        1,
									        self.expected_encoded_image_num_channels,
									        self.image_processor_tester.size['height'],
									        self.image_processor_tester.size['width'],
									    )   ,)

									# Test batched
									UpperCAmelCase__					=							image_processor(lowerCamelCase__   ,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.size['height'],
									        self.image_processor_tester.size['width'],
									    )   ,)




"""simple docstring"""






from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve



def   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__					=							np.stack((horizontal_flow, vertical_flow) ,      2	)

				# Create a grid of all pixel coordinates and subtract the flow to get the
				# target pixels coordinates
				UpperCAmelCase__					=							np.stack(
				    np.meshgrid(np.arange(0 ,      image.shape[1]	) ,      np.arange(0 ,      image.shape[0]	)	) ,      2	)
				UpperCAmelCase__					=							np.round(grid - flow	).astype(np.intaa	)

				# Find the locations outside of the original image
				UpperCAmelCase__					=							(grid < 0) | (grid >= np.array([image.shape[1], image.shape[0]]	))
				UpperCAmelCase__					=							0

				UpperCAmelCase__					=							image[grid[:, :, 1], grid[:, :, 0]]

				# Set pixels at invalid locations to 0
				UpperCAmelCase__					=							0

				return warped



def   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase = None ,      ):
				if alpha is None:
								UpperCAmelCase__					=							0.1

				# Initialize flow
				UpperCAmelCase__					=							np.zeros_like(lowerCamelCase	)
				UpperCAmelCase__					=							np.zeros_like(lowerCamelCase	)

				# Prepare kernels for the calculation of the derivatives and the average velocity
				UpperCAmelCase__					=							np.array([[-1, 1], [-1, 1]]	) * 0.25
				UpperCAmelCase__					=							np.array([[-1, -1], [1, 1]]	) * 0.25
				UpperCAmelCase__					=							np.array([[1, 1], [1, 1]]	) * 0.25
				UpperCAmelCase__					=							np.array(
				    [[1 / 1_2, 1 / 6, 1 / 1_2], [1 / 6, 0, 1 / 6], [1 / 1_2, 1 / 6, 1 / 1_2]]	)

				# Iteratively refine the flow
				for _ in range(lowerCamelCase	):
								UpperCAmelCase__					=							warp(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	)
								UpperCAmelCase__					=							convolve(lowerCamelCase ,      lowerCamelCase	) + convolve(lowerCamelCase ,      lowerCamelCase	)
								UpperCAmelCase__					=							convolve(lowerCamelCase ,      lowerCamelCase	) + convolve(lowerCamelCase ,      lowerCamelCase	)
								UpperCAmelCase__					=							convolve(lowerCamelCase ,      lowerCamelCase	) + convolve(lowerCamelCase ,      -kernel_t	)

								UpperCAmelCase__					=							convolve(lowerCamelCase ,      lowerCamelCase	)
								UpperCAmelCase__					=							convolve(lowerCamelCase ,      lowerCamelCase	)

								# This updates the flow as proposed in the paper (Step 12)
								UpperCAmelCase__					=							(
								    derivative_x * avg_horizontal_velocity
								    + derivative_y * avg_vertical_velocity
								    + derivative_t
								)
								UpperCAmelCase__					=							update / (alpha**2 + derivative_x**2 + derivative_y**2)

								UpperCAmelCase__					=							avg_horizontal_velocity - derivative_x * update
								UpperCAmelCase__					=							avg_vertical_velocity - derivative_y * update

				return horizontal_flow, vertical_flow


if __name__ == "__main__":
						import doctest

						doctest.testmod()



