





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 					__A    (    lowerCAmelCase			,				unittest.TestCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						KandinskyVaaInpaintPipeline
			lowerCAmelCase_							     =						["""image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
			lowerCAmelCase_							     =						[
			    """image_embeds""",
			    """negative_image_embeds""",
			    """image""",
			    """mask_image""",
			]
			lowerCAmelCase_							     =						[
			    """generator""",
			    """height""",
			    """width""",
			    """latents""",
			    """guidance_scale""",
			    """num_inference_steps""",
			    """return_dict""",
			    """guidance_scale""",
			    """num_images_per_prompt""",
			    """output_type""",
			    """return_dict""",
			]
			lowerCAmelCase_							     =						False





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return 3_2





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return 3_2





			@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_0_0





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				torch.manual_seed(0							)

				lowerCamelCase__								=		{
				    '''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,
				}

				lowerCamelCase__								=		UNetaDConditionModel(**__lowerCAmelCase							)
				return model





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return {
				    "block_out_channels": [3_2, 6_4],
				    "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": 1_2,
				    "out_channels": 3,
				    "up_block_types": [
				        "AttnUpDecoderBlock2D",
				        "UpDecoderBlock2D",
				    ],
				    "vq_embed_dim": 4,
				}





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.dummy_unet
				lowerCamelCase__								=		self.dummy_movq

				lowerCamelCase__								=		DDIMScheduler(
				    num_train_timesteps=1_0_0_0       ,  beta_schedule='''linear'''       ,  beta_start=0.0_0085       ,  beta_end=0.012       ,  clip_sample=__lowerCAmelCase       ,  set_alpha_to_one=__lowerCAmelCase       ,  steps_offset=1       ,  prediction_type='''epsilon'''       ,  thresholding=__lowerCAmelCase       ,  )

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

				return components





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase=0							):



				'''simple docstring'''







				lowerCamelCase__								=		floats_tensor((1, self.text_embedder_hidden_size)       ,  rng=random.Random(__lowerCAmelCase							)							).to(__lowerCAmelCase							)
				lowerCamelCase__								=		floats_tensor((1, self.text_embedder_hidden_size)       ,  rng=random.Random(seed + 1							)							).to(
				    __lowerCAmelCase							)
				# create init_image
				lowerCamelCase__								=		floats_tensor((1, 3, 6_4, 6_4)       ,  rng=random.Random(__lowerCAmelCase							)							).to(__lowerCAmelCase							)
				lowerCamelCase__								=		image.cpu().permute(0       ,  2       ,  3       ,  1							)[0]
				lowerCamelCase__								=		Image.fromarray(np.uinta(__lowerCAmelCase							)							).convert('''RGB'''							).resize((2_5_6, 2_5_6)							)
				# create mask
				lowerCamelCase__								=		np.ones((6_4, 6_4)       ,  dtype=np.floataa							)
				lowerCamelCase__								=		0

				if str(__lowerCAmelCase							).startswith('''mps'''							):
					lowerCamelCase__								=		torch.manual_seed(__lowerCAmelCase							)
				else:
					lowerCamelCase__								=		torch.Generator(device=__lowerCAmelCase							).manual_seed(__lowerCAmelCase							)
				lowerCamelCase__								=		{
				    '''image''': init_image,
				    '''mask_image''': mask,
				    '''image_embeds''': image_embeds,
				    '''negative_image_embeds''': negative_image_embeds,
				    '''generator''': generator,
				    '''height''': 6_4,
				    '''width''': 6_4,
				    '''num_inference_steps''': 2,
				    '''guidance_scale''': 4.0,
				    '''output_type''': '''np''',
				}
				return inputs





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		'''cpu'''

				lowerCamelCase__								=		self.get_dummy_components()

				lowerCamelCase__								=		self.pipeline_class(**__lowerCAmelCase							)
				lowerCamelCase__								=		pipe.to(__lowerCAmelCase							)

				pipe.set_progress_bar_config(disable=__lowerCAmelCase							)

				lowerCamelCase__								=		pipe(**self.get_dummy_inputs(__lowerCAmelCase							)							)
				lowerCamelCase__								=		output.images

				lowerCamelCase__								=		pipe(
				    **self.get_dummy_inputs(__lowerCAmelCase							)       ,  return_dict=__lowerCAmelCase       ,  )[0]

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

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

				assert image.shape == (1, 6_4, 6_4, 3)

				lowerCamelCase__								=		np.array(
				    [0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848]							)

				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 					__A    (    unittest.TestCase ):

			'''simple docstring'''







			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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

				lowerCamelCase__								=		load_image(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''							)
				lowerCamelCase__								=		np.ones((7_6_8, 7_6_8)       ,  dtype=np.floataa							)
				lowerCamelCase__								=		0

				lowerCamelCase__								=		'''a hat'''

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

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

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

				lowerCamelCase__								=		pipeline(
				    image=__lowerCAmelCase       ,  mask_image=__lowerCAmelCase       ,  image_embeds=__lowerCAmelCase       ,  negative_image_embeds=__lowerCAmelCase       ,  generator=__lowerCAmelCase       ,  num_inference_steps=1_0_0       ,  height=7_6_8       ,  width=7_6_8       ,  output_type='''np'''       ,  )

				lowerCamelCase__								=		output.images[0]

				assert image.shape == (7_6_8, 7_6_8, 3)

				assert_mean_pixel_difference(__lowerCAmelCase       ,  __lowerCAmelCase							)










from __future__ import annotations

from random import random






class 					__A    :

			'''simple docstring'''







			def __init__(    self       ,  __lowerCAmelCase = None							):



				'''simple docstring'''







				lowerCamelCase__								=		value
				lowerCamelCase__								=		random()
				lowerCamelCase__								=		None
				lowerCamelCase__								=		None





			def __repr__(    self							):



				'''simple docstring'''







				from pprint import pformat

				if self.left is None and self.right is None:
					return F'\'{self.value}: {self.prior:.5}\''
				else:
					return pformat(
					    {F'{self.value}: {self.prior:.5}': (self.left, self.right)}       ,  indent=1							)





			def __str__(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		str(self.value							) + ''' '''
				lowerCamelCase__								=		str(self.left or ''''''							)
				lowerCamelCase__								=		str(self.right or ''''''							)
				return value + left + right





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      tuple[Node | None, Node | None]:







	'''simple docstring'''







	if root is None:  # None tree is split into 2 Nones
		return None, None
	elif root.value is None:
		return None, None
	else:
		if value < root.value:
			lowerCamelCase__				,	lowerCamelCase__								=		split(root.left       ,__snake_case							)
			return left, root
		else:
			lowerCamelCase__				,	lowerCamelCase__								=		split(root.right       ,__snake_case							)
			return root, right





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      Node | None:







	'''simple docstring'''







	if (not left) or (not right):  # If one node is None, return the other
		return left or right
	elif left.prior < right.prior:
		lowerCamelCase__								=		merge(left.right       ,__snake_case							)
		return left
	else:
		lowerCamelCase__								=		merge(__snake_case       ,right.left							)
		return right





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      Node | None:







	'''simple docstring'''







	lowerCamelCase__								=		Node(__snake_case							)
	lowerCamelCase__				,	lowerCamelCase__								=		split(__snake_case       ,__snake_case							)
	return merge(merge(__snake_case       ,__snake_case							)       ,__snake_case							)





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      Node | None:







	'''simple docstring'''







	lowerCamelCase__				,	lowerCamelCase__								=		split(__snake_case       ,value - 1							)
	lowerCamelCase__				,	lowerCamelCase__								=		split(__snake_case       ,__snake_case							)
	return merge(__snake_case       ,__snake_case							)





def lowerCAmelCase__(__snake_case							)				->      None:







	'''simple docstring'''







	if not root:  # None
		return
	else:
		inorder(root.left							)
		print(root.value       ,end=''','''							)
		inorder(root.right							)





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      Node | None:







	'''simple docstring'''







	for arg in args.split():
		if arg[0] == "+":
			lowerCamelCase__								=		insert(__snake_case       ,int(arg[1:]							)							)

		elif arg[0] == "-":
			lowerCamelCase__								=		erase(__snake_case       ,int(arg[1:]							)							)

		else:
			print('''Unknown command'''							)

	return root





def lowerCAmelCase__()				->      None:







	'''simple docstring'''







	lowerCamelCase__								=		None
	print(
	    '''enter numbers to create a tree, + value to add value into treap, '''
	    '''- value to erase all nodes with value. \'q\' to quit. '''							)

	lowerCamelCase__								=		input()
	while args != "q":
		lowerCamelCase__								=		interact_treap(__snake_case       ,__snake_case							)
		print(__snake_case							)
		lowerCamelCase__								=		input()

	print('''good by!'''							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()



