



"""simple docstring"""

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class  __lowercase  (       _UpperCAmelCase       ,     unittest.TestCase):







						"""simple docstring"""







						_A		:				Any											=		KandinskyVaaInpaintPipeline
						_A		:				str											=		["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
						_A		:				List[str]											=		[
						    """image_embeds""",
						    """negative_image_embeds""",
						    """image""",
						    """mask_image""",
						]
						_A		:				int											=		[
						    """generator""",
						    """height""",
						    """width""",
						    """latents""",
						    """guidance_scale""",
						    """num_inference_steps""",
						    """return_dict""",
						    """guidance_scale""",
						    """num_images_per_prompt""",
						    """output_type""",
						    """return_dict""",
						]
						_A		:				Any											=		False







						@property
						def     __UpperCamelCase				(self							):
											return 32







						@property
						def     __UpperCamelCase				(self							):
											return 32







						@property
						def     __UpperCamelCase				(self							):
											return self.time_input_dim







						@property
						def     __UpperCamelCase				(self							):
											return self.time_input_dim * 4







						@property
						def     __UpperCamelCase				(self							):
											return 1_00







						@property
						def     __UpperCamelCase				(self							):
											torch.manual_seed(0							)

											snake_case_       :		str			       =       {
											    'in_channels': 9,
											    # Out channels is double in channels because predicts mean and variance
											    'out_channels': 8,
											    'addition_embed_type': 'image',
											    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
											    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
											    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
											    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
											    'layers_per_block': 1,
											    'encoder_hid_dim': self.text_embedder_hidden_size,
											    'encoder_hid_dim_type': 'image_proj',
											    'cross_attention_dim': self.cross_attention_dim,
											    'attention_head_dim': 4,
											    'resnet_time_scale_shift': 'scale_shift',
											    'class_embed_type': None,
											}

											snake_case_       :		List[Any]			       =       UNetaDConditionModel(**a_							)
											return model







						@property
						def     __UpperCamelCase				(self							):
											return {
											    "block_out_channels": [32, 64],
											    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
											    "in_channels": 3,
											    "latent_channels": 4,
											    "layers_per_block": 1,
											    "norm_num_groups": 8,
											    "norm_type": "spatial",
											    "num_vq_embeddings": 12,
											    "out_channels": 3,
											    "up_block_types": [
											        "AttnUpDecoderBlock2D",
											        "UpDecoderBlock2D",
											    ],
											    "vq_embed_dim": 4,
											}







						@property
						def     __UpperCamelCase				(self							):
											torch.manual_seed(0							)
											snake_case_       :		Optional[Any]			       =       VQModel(**self.dummy_movq_kwargs							)
											return model







						def     __UpperCamelCase				(self							):
											snake_case_       :		Any			       =       self.dummy_unet
											snake_case_       :		str			       =       self.dummy_movq

											snake_case_       :		str			       =       DDIMScheduler(
											    num_train_timesteps=10_00 ,		beta_schedule="""linear""" ,		beta_start=0.00085 ,		beta_end=0.012 ,		clip_sample=a_ ,		set_alpha_to_one=a_ ,		steps_offset=1 ,		prediction_type="""epsilon""" ,		thresholding=a_ ,		)

											snake_case_       :		str			       =       {
											    'unet': unet,
											    'scheduler': scheduler,
											    'movq': movq,
											}

											return components







						def     __UpperCamelCase				(self ,		lowercase__ ,		lowercase__=0							):
											snake_case_       :		int			       =       floats_tensor((1, self.text_embedder_hidden_size) ,		rng=random.Random(a_							)							).to(a_							)
											snake_case_       :		int			       =       floats_tensor((1, self.text_embedder_hidden_size) ,		rng=random.Random(seed + 1							)							).to(
											    a_							)
											# create init_image
											snake_case_       :		int			       =       floats_tensor((1, 3, 64, 64) ,		rng=random.Random(a_							)							).to(a_							)
											snake_case_       :		Union[str, Any]			       =       image.cpu().permute(0 ,		2 ,		3 ,		1							)[0]
											snake_case_       :		Union[str, Any]			       =       Image.fromarray(np.uinta(a_							)							).convert("""RGB"""							).resize((2_56, 2_56)							)
											# create mask
											snake_case_       :		Dict			       =       np.ones((64, 64) ,		dtype=np.floataa							)
											snake_case_       :		Tuple			       =       0

											if str(a_							).startswith("""mps"""							):
																snake_case_       :		Dict			       =       torch.manual_seed(a_							)
											else:
																snake_case_       :		int			       =       torch.Generator(device=a_							).manual_seed(a_							)
											snake_case_       :		Tuple			       =       {
											    'image': init_image,
											    'mask_image': mask,
											    'image_embeds': image_embeds,
											    'negative_image_embeds': negative_image_embeds,
											    'generator': generator,
											    'height': 64,
											    'width': 64,
											    'num_inference_steps': 2,
											    'guidance_scale': 4.0,
											    'output_type': 'np',
											}
											return inputs







						def     __UpperCamelCase				(self							):
											snake_case_       :		Optional[int]			       =       'cpu'

											snake_case_       :		Optional[int]			       =       self.get_dummy_components()

											snake_case_       :		Any			       =       self.pipeline_class(**a_							)
											snake_case_       :		Optional[Any]			       =       pipe.to(a_							)

											pipe.set_progress_bar_config(disable=a_							)

											snake_case_       :		str			       =       pipe(**self.get_dummy_inputs(a_							)							)
											snake_case_       :		Dict			       =       output.images

											snake_case_       :		Any			       =       pipe(
											    **self.get_dummy_inputs(a_							) ,		return_dict=a_ ,		)[0]

											snake_case_       :		Optional[int]			       =       image[0, -3:, -3:, -1]
											snake_case_       :		Union[str, Any]			       =       image_from_tuple[0, -3:, -3:, -1]

											print(f'image.shape {image.shape}'							)

											assert image.shape == (1, 64, 64, 3)

											snake_case_       :		int			       =       np.array(
											    [0.50775903, 0.49527195, 0.48824543, 0.50192237, 0.48644906, 0.49373814, 0.4780598, 0.47234827, 0.48327848]							)

											assert (
											    np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
											), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
											assert (
											    np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2
											), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'







						def     __UpperCamelCase				(self							):
											super().test_inference_batch_single_identical(expected_max_diff=3e-3							)







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







						"""simple docstring"""







						def     __UpperCamelCase				(self							):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()







						def     __UpperCamelCase				(self							):
											snake_case_       :		Optional[Any]			       =       load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"""							)

											snake_case_       :		Union[str, Any]			       =       load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""							)
											snake_case_       :		str			       =       np.ones((7_68, 7_68) ,		dtype=np.floataa							)
											snake_case_       :		Any			       =       0

											snake_case_       :		str			       =       'a hat'

											snake_case_       :		Any			       =       KandinskyVaaPriorPipeline.from_pretrained(
											    """kandinsky-community/kandinsky-2-2-prior""" ,		torch_dtype=torch.floataa							)
											pipe_prior.to(a_							)

											snake_case_       :		Optional[int]			       =       KandinskyVaaInpaintPipeline.from_pretrained(
											    """kandinsky-community/kandinsky-2-2-decoder-inpaint""" ,		torch_dtype=torch.floataa							)
											snake_case_       :		List[str]			       =       pipeline.to(a_							)
											pipeline.set_progress_bar_config(disable=a_							)

											snake_case_       :		str			       =       torch.Generator(device="""cpu"""							).manual_seed(0							)
											snake_case_       :		List[str]			       =       pipe_prior(
											    a_ ,		generator=a_ ,		num_inference_steps=5 ,		negative_prompt="""""" ,		).to_tuple()

											snake_case_       :		List[Any]			       =       pipeline(
											    image=a_ ,		mask_image=a_ ,		image_embeds=a_ ,		negative_image_embeds=a_ ,		generator=a_ ,		num_inference_steps=1_00 ,		height=7_68 ,		width=7_68 ,		output_type="""np""" ,		)

											snake_case_       :		str			       =       output.images[0]

											assert image.shape == (7_68, 7_68, 3)

											assert_mean_pixel_difference(a_ ,		a_							)









'''simple docstring'''

def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					list:





			"""simple docstring"""







			snake_case:    Optional[int]      							=len(__UpperCAmelCase					)
			snake_case:    Optional[int]      							=[[0] * n for i in range(__UpperCAmelCase					)]
			for i in range(__UpperCAmelCase					):
						snake_case:    Optional[int]      							=y_points[i]

			for i in range(2				,							__UpperCAmelCase					):
						for j in range(__UpperCAmelCase				,							__UpperCAmelCase					):
									snake_case:    List[str]      							=(
									    (xa - x_points[j - i + 1]) * q[j][i - 1]
									    - (xa - x_points[j]) * q[j - 1][i - 1]
									) / (x_points[j] - x_points[j - i + 1])

			return [q[n - 1][n - 1], q]


if __name__ == "__main__":
	import doctest

	doctest.testmod()




