import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 			lowerCamelCase_  (     UpperCAmelCase_		,			unittest.TestCase				):






    '''simple docstring'''






    a__			:		Optional[Any]      			=   DDIMPipeline
    a__			:		List[Any]      			=   UNCONDITIONAL_IMAGE_GENERATION_PARAMS
    a__			:		Tuple      			=   PipelineTesterMixin.required_optional_params - {
        """num_images_per_prompt""",
        """latents""",
        """callback""",
        """callback_steps""",
    }
    a__			:		Union[str, Any]      			=   UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
    a__			:		List[Any]      			=   False







    def     UpperCamelCase__     (			self) ->			List[str]:
        torch.manual_seed(0)
        __UpperCamelCase  :Tuple    						=	UNetaDModel(
            block_out_channels=(32, 64)				,				layers_per_block=2				,				sample_size=32				,				in_channels=3				,				out_channels=3				,				down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')				,				up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')				,				)
        __UpperCamelCase  :List[Any]    						=	DDIMScheduler()
        __UpperCamelCase  :Any    						=	{'''unet''': unet, '''scheduler''': scheduler}
        return components







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase=0) ->			Union[str, Any]:
        if str(__lowercase).startswith('''mps'''):
            __UpperCamelCase  :Optional[Any]    						=	torch.manual_seed(__lowercase)
        else:
            __UpperCamelCase  :Tuple    						=	torch.Generator(device=__lowercase).manual_seed(__lowercase)
        __UpperCamelCase  :Any    						=	{
            '''batch_size''': 1,
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''output_type''': '''numpy''',
        }
        return inputs







    def     UpperCamelCase__     (			self) ->			List[str]:
        __UpperCamelCase  :Optional[Any]    						=	'''cpu'''

        __UpperCamelCase  :Union[str, Any]    						=	self.get_dummy_components()
        __UpperCamelCase  :Optional[Any]    						=	self.pipeline_class(**__lowercase)
        pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase  :Union[str, Any]    						=	self.get_dummy_inputs(__lowercase)
        __UpperCamelCase  :Union[str, Any]    						=	pipe(**__lowercase).images
        __UpperCamelCase  :str    						=	image[0, -3:, -3:, -1]

        self.assertEqual(image.shape				,				(1, 32, 32, 3))
        __UpperCamelCase  :int    						=	np.array(
            [1.0_0_0E0_0, 5.7_1_7E-0_1, 4.7_1_7E-0_1, 1.0_0_0E0_0, 0.0_0_0E0_0, 1.0_0_0E0_0, 3.0_0_0E-0_4, 0.0_0_0E0_0, 9.0_0_0E-0_4])
        __UpperCamelCase  :List[Any]    						=	np.abs(image_slice.flatten() - expected_slice).max()
        self.assertLessEqual(__lowercase				,				1E-3)







    def     UpperCamelCase__     (			self) ->			Any:
        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3)







    def     UpperCamelCase__     (			self) ->			List[str]:
        super().test_save_load_local(expected_max_difference=3E-3)







    def     UpperCamelCase__     (			self) ->			Tuple:
        super().test_save_load_optional_components(expected_max_difference=3E-3)







    def     UpperCamelCase__     (			self) ->			List[Any]:
        super().test_inference_batch_single_identical(expected_max_diff=3E-3)


@slow
@require_torch_gpu
class 			lowerCamelCase_  (     unittest.TestCase				):






    '''simple docstring'''






    def     UpperCamelCase__     (			self) ->			Any:
        __UpperCamelCase  :Union[str, Any]    						=	'''google/ddpm-cifar10-32'''

        __UpperCamelCase  :List[Any]    						=	UNetaDModel.from_pretrained(__lowercase)
        __UpperCamelCase  :List[str]    						=	DDIMScheduler()

        __UpperCamelCase  :Any    						=	DDIMPipeline(unet=__lowercase				,				scheduler=__lowercase)
        ddim.to(__lowercase)
        ddim.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase  :Any    						=	torch.manual_seed(0)
        __UpperCamelCase  :Tuple    						=	ddim(generator=__lowercase				,				eta=0.0				,				output_type='''numpy''').images

        __UpperCamelCase  :str    						=	image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        __UpperCamelCase  :List[str]    						=	np.array([0.17_23, 0.16_17, 0.16_00, 0.16_26, 0.14_97, 0.15_13, 0.15_05, 0.14_42, 0.14_53])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2







    def     UpperCamelCase__     (			self) ->			Tuple:
        __UpperCamelCase  :Any    						=	'''google/ddpm-ema-bedroom-256'''

        __UpperCamelCase  :Union[str, Any]    						=	UNetaDModel.from_pretrained(__lowercase)
        __UpperCamelCase  :Optional[Any]    						=	DDIMScheduler.from_pretrained(__lowercase)

        __UpperCamelCase  :Any    						=	DDIMPipeline(unet=__lowercase				,				scheduler=__lowercase)
        ddpm.to(__lowercase)
        ddpm.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase  :Optional[int]    						=	torch.manual_seed(0)
        __UpperCamelCase  :Optional[int]    						=	ddpm(generator=__lowercase				,				output_type='''numpy''').images

        __UpperCamelCase  :Union[str, Any]    						=	image[0, -3:, -3:, -1]

        assert image.shape == (1, 256, 256, 3)
        __UpperCamelCase  :int    						=	np.array([0.00_60, 0.02_01, 0.03_44, 0.00_24, 0.00_18, 0.00_02, 0.00_22, 0.00_00, 0.00_69])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2






import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

__lowercase											=      imread(r'''digital_image_processing/image_data/lena_small.jpg''')
__lowercase											=      cvtColor(img, COLOR_BGR2GRAY)



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Optional[Any]    						=	cn.convert_to_negative(SCREAMING_SNAKE_CASE			)
    # assert negative_img array for at least one True
    assert negative_img.any()



def lowerCamelCase						(     ):
    '''simple docstring'''







    with Image.open('''digital_image_processing/image_data/lena_small.jpg'''			) as img:
        # Work around assertion for response
        assert str(cc.change_contrast(SCREAMING_SNAKE_CASE						,    110			)			).startswith(
            '''<PIL.Image.Image image mode=RGB size=100x100 at'''			)



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :List[Any]    						=	canny.gen_gaussian_kernel(9						,    sigma=1.4			)
    # Assert ambiguous array
    assert resp.all()



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :List[str]    						=	imread('''digital_image_processing/image_data/lena_small.jpg'''						,    0			)
    # assert ambiguous array for all == True
    assert canny_img.all()
    __UpperCamelCase  :str    						=	canny.canny(SCREAMING_SNAKE_CASE			)
    # assert canny array for at least one True
    assert canny_array.any()



def lowerCamelCase						(     ):
    '''simple docstring'''







    assert gg.gaussian_filter(SCREAMING_SNAKE_CASE						,    5						,    sigma=0.9			).all()



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :List[str]    						=	array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]			)
    __UpperCamelCase  :Optional[Any]    						=	conv.img_convolve(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			).astype(SCREAMING_SNAKE_CASE			)
    assert res.any()



def lowerCamelCase						(     ):
    '''simple docstring'''







    assert med.median_filter(SCREAMING_SNAKE_CASE						,    3			).any()



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase						, __UpperCamelCase  :Dict    						=	sob.sobel_filter(SCREAMING_SNAKE_CASE			)
    assert grad.any() and theta.any()



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	sp.make_sepia(SCREAMING_SNAKE_CASE						,    20			)
    assert sepia.all()



def lowerCamelCase						(     SCREAMING_SNAKE_CASE = "digital_image_processing/image_data/lena_small.jpg"			):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	bs.Burkes(imread(SCREAMING_SNAKE_CASE						,    1			)						,    120			)
    burkes.process()
    assert burkes.output_img.any()



def lowerCamelCase						(     SCREAMING_SNAKE_CASE = "digital_image_processing/image_data/lena_small.jpg"						,    ):
    '''simple docstring'''







    __UpperCamelCase  :List[str]    						=	rs.NearestNeighbour(imread(SCREAMING_SNAKE_CASE						,    1			)						,    400						,    200			)
    nn.process()
    assert nn.output.any()



def lowerCamelCase						(     ):
    '''simple docstring'''







    __UpperCamelCase  :Union[str, Any]    						=	'''digital_image_processing/image_data/lena.jpg'''

    # Reading the image and converting it to grayscale.
    __UpperCamelCase  :Optional[int]    						=	imread(SCREAMING_SNAKE_CASE						,    0			)

    # Test for get_neighbors_pixel function() return not None
    __UpperCamelCase  :Optional[int]    						=	0
    __UpperCamelCase  :List[Any]    						=	0
    __UpperCamelCase  :Dict    						=	image[x_coordinate][y_coordinate]

    __UpperCamelCase  :int    						=	lbp.get_neighbors_pixel(
        SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    assert neighbors_pixels is not None

    # Test for local_binary_pattern function()
    # Create a numpy array as the same height and width of read image
    __UpperCamelCase  :Tuple    						=	np.zeros((image.shape[0], image.shape[1])			)

    # Iterating through the image and calculating the local binary pattern value
    # for each pixel.
    for i in range(0						,    image.shape[0]			):
        for j in range(0						,    image.shape[1]			):
            __UpperCamelCase  :Optional[int]    						=	lbp.local_binary_value(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    assert lbp_image.any()





