
'''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 		__UpperCAmelCase   (      _lowerCamelCase		,     unittest.TestCase		):
							__lowercase   	= KandinskyVaaInpaintPipeline
							__lowercase   	= ["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
							__lowercase   	= [
							    """image_embeds""",
							    """negative_image_embeds""",
							    """image""",
							    """mask_image""",
							]
							__lowercase   	= [
							    """generator""",
							    """height""",
							    """width""",
							    """latents""",
							    """guidance_scale""",
							    """num_inference_steps""",
							    """return_dict""",
							    """guidance_scale""",
							    """num_images_per_prompt""",
							    """output_type""",
							    """return_dict""",
							]
							__lowercase   	= False


							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return 32


							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return 32


							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return self.time_input_dim


							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return self.time_input_dim * 4


							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return 1_00


							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								torch.manual_seed(0						)

								_snake_case     		=	{
								    '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     		=	UNetaDConditionModel(**lowerCAmelCase_						)
								return model


							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								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 				lowerCamelCase       ( self						):





								"""simple docstring"""






								torch.manual_seed(0						)
								_snake_case     		=	VQModel(**self.dummy_movq_kwargs						)
								return model


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.dummy_unet
								_snake_case     		=	self.dummy_movq

								_snake_case     		=	DDIMScheduler(
								    num_train_timesteps=10_00					,     beta_schedule='linear'					,     beta_start=0.00085					,     beta_end=0.012					,     clip_sample=lowerCAmelCase_					,     set_alpha_to_one=lowerCAmelCase_					,     steps_offset=1					,     prediction_type='epsilon'					,     thresholding=lowerCAmelCase_					,     )

								_snake_case     		=	{
								    'unet': unet,
								    'scheduler': scheduler,
								    'movq': movq,
								}

								return components


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_=0						):





								"""simple docstring"""






								_snake_case     		=	floats_tensor((1, self.text_embedder_hidden_size)					,     rng=random.Random(lowerCAmelCase_						)						).to(lowerCAmelCase_						)
								_snake_case     		=	floats_tensor((1, self.text_embedder_hidden_size)					,     rng=random.Random(seed + 1						)						).to(
								    lowerCAmelCase_						)
								# create init_image
								_snake_case     		=	floats_tensor((1, 3, 64, 64)					,     rng=random.Random(lowerCAmelCase_						)						).to(lowerCAmelCase_						)
								_snake_case     		=	image.cpu().permute(0					,     2					,     3					,     1						)[0]
								_snake_case     		=	Image.fromarray(np.uinta(lowerCAmelCase_						)						).convert('RGB'						).resize((2_56, 2_56)						)
								# create mask
								_snake_case     		=	np.ones((64, 64)					,     dtype=np.floataa						)
								_snake_case     		=	0

								if str(lowerCAmelCase_						).startswith('mps'						):
									_snake_case     		=	torch.manual_seed(lowerCAmelCase_						)
								else:
									_snake_case     		=	torch.Generator(device=lowerCAmelCase_						).manual_seed(lowerCAmelCase_						)
								_snake_case     		=	{
								    '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 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	'cpu'

								_snake_case     		=	self.get_dummy_components()

								_snake_case     		=	self.pipeline_class(**lowerCAmelCase_						)
								_snake_case     		=	pipe.to(lowerCAmelCase_						)

								pipe.set_progress_bar_config(disable=lowerCAmelCase_						)

								_snake_case     		=	pipe(**self.get_dummy_inputs(lowerCAmelCase_						)						)
								_snake_case     		=	output.images

								_snake_case     		=	pipe(
								    **self.get_dummy_inputs(lowerCAmelCase_						)					,     return_dict=lowerCAmelCase_					,     )[0]

								_snake_case     		=	image[0, -3:, -3:, -1]
								_snake_case     		=	image_from_tuple[0, -3:, -3:, -1]

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

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

								_snake_case     		=	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 				lowerCamelCase       ( self						):





								"""simple docstring"""






								super().test_inference_batch_single_identical(expected_max_diff=3E-3						)


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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'						)

								_snake_case     		=	load_image(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'						)
								_snake_case     		=	np.ones((7_68, 7_68)					,     dtype=np.floataa						)
								_snake_case     		=	0

								_snake_case     		=	'a hat'

								_snake_case     		=	KandinskyVaaPriorPipeline.from_pretrained(
								    'kandinsky-community/kandinsky-2-2-prior'					,     torch_dtype=torch.floataa						)
								pipe_prior.to(lowerCAmelCase_						)

								_snake_case     		=	KandinskyVaaInpaintPipeline.from_pretrained(
								    'kandinsky-community/kandinsky-2-2-decoder-inpaint'					,     torch_dtype=torch.floataa						)
								_snake_case     		=	pipeline.to(lowerCAmelCase_						)
								pipeline.set_progress_bar_config(disable=lowerCAmelCase_						)

								_snake_case     		=	torch.Generator(device='cpu'						).manual_seed(0						)
								_snake_case		,				_snake_case     		=	pipe_prior(
								    lowerCAmelCase_					,     generator=lowerCAmelCase_					,     num_inference_steps=5					,     negative_prompt=''					,     ).to_tuple()

								_snake_case     		=	pipeline(
								    image=lowerCAmelCase_					,     mask_image=lowerCAmelCase_					,     image_embeds=lowerCAmelCase_					,     negative_image_embeds=lowerCAmelCase_					,     generator=lowerCAmelCase_					,     num_inference_steps=1_00					,     height=7_68					,     width=7_68					,     output_type='np'					,     )

								_snake_case     		=	output.images[0]

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

								assert_mean_pixel_difference(lowerCAmelCase_					,     lowerCAmelCase_						)



'''simple docstring'''







from __future__ import annotations







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			int:
	if not nums:
		return 0
	_snake_case     		=	nums[0]
	_snake_case     		=	0
	for num in nums[1:]:
		_snake_case		,				_snake_case     		=	(
		    max_excluding + num,
		    max(__A    ,       __A			),
		)
	return max(__A    ,       __A			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()

