

'''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 TvltImageProcessor







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase=1_0 ,UpperCamelCase=1_0 ,UpperCamelCase=False ,UpperCamelCase=False	)     ->     int:
	UpperCAmelCase_		:   str													=       []
	for i in range(image_processor_tester.num_frames	):
		video.append(np.random.randint(2_5_5 ,size=(image_processor_tester.num_channels, width, height) ,dtype=np.uinta	)	)

	if not numpify and not torchify:
		# PIL expects the channel dimension as last dimension
		UpperCAmelCase_		:   Optional[int]													=       [Image.fromarray(np.moveaxis(UpperCamelCase ,0 ,-1	)	) for frame in video]

	if torchify:
		UpperCAmelCase_		:   Optional[int]													=       [torch.from_numpy(UpperCamelCase	) for frame in video]

	return video







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

	UpperCAmelCase_		:   str													=       []
	for i in range(image_processor_tester.batch_size	):
		if equal_resolution:
			UpperCAmelCase_		:   Optional[int]													=       image_processor_tester.max_resolution
		else:
			UpperCAmelCase_		:   Optional[Any]													=       np.random.choice(
			    np.arange(image_processor_tester.min_resolution ,image_processor_tester.max_resolution	) ,2	)
			UpperCAmelCase_		:   Tuple													=       prepare_video(
			    image_processor_tester=UpperCamelCase ,width=UpperCamelCase ,height=UpperCamelCase ,numpify=UpperCamelCase ,torchify=UpperCamelCase ,)
		video_inputs.append(UpperCamelCase	)

	return video_inputs



class        lowercase    (	unittest.TestCase			):


							def __init__(      self     ,      _snake_case     ,      _snake_case=7     ,      _snake_case=3     ,      _snake_case=4     ,      _snake_case=18     ,      _snake_case=30     ,      _snake_case=400     ,      _snake_case=True     ,      _snake_case=None     ,      _snake_case=True     ,      _snake_case=[0.5, 0.5, 0.5]     ,      _snake_case=[0.5, 0.5, 0.5]     ,      _snake_case=True     ,      _snake_case=None     ,      )				->					Union[str, Any]:
								UpperCAmelCase_		:   str													=       size if size is not None else {'''shortest_edge''': 18}
								UpperCAmelCase_		:   Any													=       crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

								UpperCAmelCase_		:   Union[str, Any]													=       parent
								UpperCAmelCase_		:   int													=       batch_size
								UpperCAmelCase_		:   Tuple													=       num_channels
								UpperCAmelCase_		:   Tuple													=       num_frames
								UpperCAmelCase_		:   Union[str, Any]													=       image_size
								UpperCAmelCase_		:   int													=       min_resolution
								UpperCAmelCase_		:   Union[str, Any]													=       max_resolution
								UpperCAmelCase_		:   Dict													=       do_resize
								UpperCAmelCase_		:   Any													=       size
								UpperCAmelCase_		:   Dict													=       do_normalize
								UpperCAmelCase_		:   List[Any]													=       image_mean
								UpperCAmelCase_		:   Dict													=       image_std
								UpperCAmelCase_		:   List[str]													=       do_center_crop
								UpperCAmelCase_		:   List[str]													=       crop_size





							def     _snake_case				(      self)				->					List[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,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								}



@require_torch
@require_vision
class        lowercase    (	lowerCamelCase__,       unittest.TestCase			):
							_lowerCamelCase		:   Any= TvltImageProcessor if is_vision_available() else None


							def     _snake_case				(      self)				->					List[Any]:
								UpperCAmelCase_		:   Any													=       TvltImageProcessorTester(self)


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


							def     _snake_case				(      self)				->					Any:
								UpperCAmelCase_		:   Union[str, Any]													=       self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(_snake_case     ,      'image_mean'))
								self.assertTrue(hasattr(_snake_case     ,      'image_std'))
								self.assertTrue(hasattr(_snake_case     ,      'do_normalize'))
								self.assertTrue(hasattr(_snake_case     ,      'do_resize'))
								self.assertTrue(hasattr(_snake_case     ,      'do_center_crop'))
								self.assertTrue(hasattr(_snake_case     ,      'size'))


							def     _snake_case				(      self)				->					int:
								UpperCAmelCase_		:   List[Any]													=       self.image_processing_class(**self.image_processor_dict)
								# create random PIL videos
								UpperCAmelCase_		:   Optional[int]													=       prepare_video_inputs(self.image_processor_tester     ,      equal_resolution=_snake_case)
								for video in video_inputs:
									self.assertIsInstance(_snake_case     ,      _snake_case)
									self.assertIsInstance(video[0]     ,      Image.Image)

								# Test not batched input
								UpperCAmelCase_		:   List[Any]													=       image_processor(video_inputs[0]     ,      return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_videos.shape     ,      (
								        1,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )     ,      )

								# Test batched
								UpperCAmelCase_		:   str													=       image_processor(_snake_case     ,      return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_videos.shape     ,      (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )     ,      )


							def     _snake_case				(      self)				->					Dict:
								UpperCAmelCase_		:   List[Any]													=       self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								UpperCAmelCase_		:   int													=       prepare_video_inputs(self.image_processor_tester     ,      equal_resolution=_snake_case     ,      numpify=_snake_case)
								for video in video_inputs:
									self.assertIsInstance(_snake_case     ,      _snake_case)
									self.assertIsInstance(video[0]     ,      np.ndarray)

								# Test not batched input
								UpperCAmelCase_		:   Dict													=       image_processor(video_inputs[0]     ,      return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_videos.shape     ,      (
								        1,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )     ,      )

								# Test batched
								UpperCAmelCase_		:   Optional[int]													=       image_processor(_snake_case     ,      return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_videos.shape     ,      (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )     ,      )





							def     _snake_case				(      self)				->					Optional[int]:
								UpperCAmelCase_		:   Any													=       self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								UpperCAmelCase_		:   Dict													=       prepare_video_inputs(self.image_processor_tester     ,      equal_resolution=_snake_case     ,      torchify=_snake_case)
								for video in video_inputs:
									self.assertIsInstance(_snake_case     ,      _snake_case)
									self.assertIsInstance(video[0]     ,      torch.Tensor)

								# Test not batched input
								UpperCAmelCase_		:   Dict													=       image_processor(video_inputs[0]     ,      return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_videos.shape     ,      (
								        1,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )     ,      )

								# Test batched
								UpperCAmelCase_		:   Union[str, Any]													=       image_processor(_snake_case     ,      return_tensors='pt').pixel_values
								self.assertEqual(
								    encoded_videos.shape     ,      (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )     ,      )









"""simple docstring"""
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import array, dot, pad, ravel, uinta, zeros







def       UpperCamelCase_					(     lowerCamelCase							:					Optional[int]     , lowerCamelCase							:					Tuple   )						->     str:



       """simple docstring"""







       __magic_name__       ,							__magic_name__			:		Optional[Any]        =     image.shape
       __magic_name__			:		str        =     cols - block_size[1] + 1
       __magic_name__			:		int        =     rows - block_size[0] + 1
       __magic_name__			:		Optional[Any]        =     zeros((dst_height * dst_width, block_size[1] * block_size[0])   )
       __magic_name__			:		List[Any]        =     0
       for i in range(0     , lowerCamelCase   ):
              for j in range(0     , lowerCamelCase   ):
                     __magic_name__			:		List[Any]        =     ravel(image[i : i + block_size[0], j : j + block_size[1]]   )
                     __magic_name__			:		str        =     window
                     row += 1

       return image_array







def       UpperCamelCase_					(     lowerCamelCase							:					str     , lowerCamelCase							:					List[str]   )						->     Tuple:



       """simple docstring"""







       __magic_name__       ,							__magic_name__			:		List[str]        =     image.shape[0], image.shape[1]
       __magic_name__			:		Any        =     filter_kernel.shape[0]
       __magic_name__			:		Optional[Any]        =     k_size // 2
       # Pads image with the edge values of array.
       __magic_name__			:		Union[str, Any]        =     pad(lowerCamelCase     , lowerCamelCase     , mode='''edge'''   )

       # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
       __magic_name__			:		List[Any]        =     imacol(lowerCamelCase     , (k_size, k_size)   )

       #  turn the kernel into shape(k*k, 1)
       __magic_name__			:		Tuple        =     ravel(lowerCamelCase   )
       # reshape and get the dst image
       __magic_name__			:		int        =     dot(lowerCamelCase     , lowerCamelCase   ).reshape(lowerCamelCase     , lowerCamelCase   )
       return dst


if __name__ == "__main__":
 # read original image
 A									=						imread(R"""../image_data/lena.jpg""")
 # turn image in gray scale value
 A									=						cvtColor(img, COLOR_BGR2GRAY)
 # Laplace operator
 A									=						array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
 A									=						img_convolve(gray, Laplace_kernel).astype(uinta)
 imshow("""Laplacian""", out)
 waitKey(0)
