
"""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 EfficientNetImageProcessor







class 		lowerCamelCase__					(					unittest.TestCase				):





			'''simple docstring'''



			def __init__(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase=13     ,     __UpperCAmelCase=3     ,     __UpperCAmelCase=18     ,     __UpperCAmelCase=30     ,     __UpperCAmelCase=4_00     ,     __UpperCAmelCase=True     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase=True     ,     __UpperCAmelCase=[0.5, 0.5, 0.5]     ,     __UpperCAmelCase=[0.5, 0.5, 0.5]     ,     ) ->     Dict:
				_lowerCAmelCase								=size if size is not None else {"""height""": 18, """width""": 18}
				_lowerCAmelCase								=parent
				_lowerCAmelCase								=batch_size
				_lowerCAmelCase								=num_channels
				_lowerCAmelCase								=image_size
				_lowerCAmelCase								=min_resolution
				_lowerCAmelCase								=max_resolution
				_lowerCAmelCase								=do_resize
				_lowerCAmelCase								=size
				_lowerCAmelCase								=do_normalize
				_lowerCAmelCase								=image_mean
				_lowerCAmelCase								=image_std



			def 					_lowerCAmelCase		(							self  ) ->     int:
				return {
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_normalize": self.do_normalize,
				    "do_resize": self.do_resize,
				    "size": self.size,
				}







@require_torch
@require_vision
class 		lowerCamelCase__					(					__magic_name__ ,	unittest.TestCase				):





			'''simple docstring'''
			lowerCamelCase  					=		EfficientNetImageProcessor if is_vision_available() else None



			def 					_lowerCAmelCase		(							self  ) ->     Tuple:
				_lowerCAmelCase								=EfficientNetImageProcessorTester(self  )



			@property
			def 					_lowerCAmelCase		(							self  ) ->     Optional[int]:
				return self.image_processor_tester.prepare_image_processor_dict()



			def 					_lowerCAmelCase		(							self  ) ->     List[str]:
				_lowerCAmelCase								=self.image_processing_class(**self.image_processor_dict  )
				self.assertTrue(hasattr(__UpperCAmelCase     ,     """image_mean"""  )  )
				self.assertTrue(hasattr(__UpperCAmelCase     ,     """image_std"""  )  )
				self.assertTrue(hasattr(__UpperCAmelCase     ,     """do_normalize"""  )  )
				self.assertTrue(hasattr(__UpperCAmelCase     ,     """do_resize"""  )  )
				self.assertTrue(hasattr(__UpperCAmelCase     ,     """size"""  )  )



			def 					_lowerCAmelCase		(							self  ) ->     List[Any]:
				_lowerCAmelCase								=self.image_processing_class.from_dict(self.image_processor_dict  )
				self.assertEqual(image_processor.size     ,     {"""height""": 18, """width""": 18}  )

				_lowerCAmelCase								=self.image_processing_class.from_dict(self.image_processor_dict     ,     size=42  )
				self.assertEqual(image_processor.size     ,     {"""height""": 42, """width""": 42}  )



			def 					_lowerCAmelCase		(							self  ) ->     Tuple:
				# Initialize image_processing
				_lowerCAmelCase								=self.image_processing_class(**self.image_processor_dict  )
				# create random PIL images
				_lowerCAmelCase								=prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=__UpperCAmelCase  )
				for image in image_inputs:
					self.assertIsInstance(__UpperCAmelCase     ,     Image.Image  )

				# Test not batched input
				_lowerCAmelCase								=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.size["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )     ,     )

				# Test batched
				_lowerCAmelCase								=image_processing(__UpperCAmelCase     ,     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  ) ->     Union[str, Any]:
				# Initialize image_processing
				_lowerCAmelCase								=self.image_processing_class(**self.image_processor_dict  )
				# create random numpy tensors
				_lowerCAmelCase								=prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=__UpperCAmelCase     ,     numpify=__UpperCAmelCase  )
				for image in image_inputs:
					self.assertIsInstance(__UpperCAmelCase     ,     np.ndarray  )

				# Test not batched input
				_lowerCAmelCase								=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.size["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )     ,     )

				# Test batched
				_lowerCAmelCase								=image_processing(__UpperCAmelCase     ,     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  ) ->     Any:
				# Initialize image_processing
				_lowerCAmelCase								=self.image_processing_class(**self.image_processor_dict  )
				# create random PyTorch tensors
				_lowerCAmelCase								=prepare_image_inputs(self.image_processor_tester     ,     equal_resolution=__UpperCAmelCase     ,     torchify=__UpperCAmelCase  )
				for image in image_inputs:
					self.assertIsInstance(__UpperCAmelCase     ,     torch.Tensor  )

				# Test not batched input
				_lowerCAmelCase								=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.size["""height"""],
				        self.image_processor_tester.size["""width"""],
				    )     ,     )

				# Test batched
				_lowerCAmelCase								=image_processing(__UpperCAmelCase     ,     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"""],
				    )     ,     )





'''simple docstring'''
from typing import SupportsIndex

import numpy as np
from scipy.ndimage import convolve







def 						A_(			A		:						np.ndarray						,			A		:						np.ndarray						,			A		:						np.ndarray):
							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_(			A		:						np.ndarray						,			A		:						np.ndarray						,			A		:						SupportsIndex						,			A		:						float | None = None						,			):
							if alpha is None:
														UpperCamelCase					       =			0.1

							# Initialize flow
							UpperCamelCase					       =			np.zeros_like(A)
							UpperCamelCase					       =			np.zeros_like(A)

							# 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 / 12, 1 / 6, 1 / 12], [1 / 6, 0, 1 / 6], [1 / 12, 1 / 6, 1 / 12]])

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

														UpperCamelCase					       =			convolve(A						,			A)
														UpperCamelCase					       =			convolve(A						,			A)

														# 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()




