


'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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 _lowerCamelCase	(						lowerCamelCase_		,					unittest.TestCase							):



		'''simple docstring'''







		A_     :      Dict				   =    KandinskyVaaControlnetImgaImgPipeline
		A_     :      Any				   =    ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
		A_     :      Dict				   =    ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
		A_     :      Union[str, Any]				   =    [
		    '''generator''',
		    '''height''',
		    '''width''',
		    '''strength''',
		    '''guidance_scale''',
		    '''num_inference_steps''',
		    '''return_dict''',
		    '''guidance_scale''',
		    '''num_images_per_prompt''',
		    '''output_type''',
		    '''return_dict''',
		]
		A_     :      str				   =    False







		@property
		def        __lowerCAmelCase		( self		:						Tuple      )  ->			Tuple:
			return 32







		@property
		def        __lowerCAmelCase		( self		:						Optional[int]      )  ->			Tuple:
			return 32







		@property
		def        __lowerCAmelCase		( self		:						Optional[int]      )  ->			List[Any]:
			return self.time_input_dim







		@property
		def        __lowerCAmelCase		( self		:						Union[str, Any]      )  ->			Dict:
			return self.time_input_dim * 4







		@property
		def        __lowerCAmelCase		( self		:						List[str]      )  ->			Any:
			return 100







		@property
		def        __lowerCAmelCase		( self		:						Dict      )  ->			Optional[Any]:
			torch.manual_seed(0      )

			__magic_name__	:   str					     =					{
			    'in_channels': 8,
			    # Out channels is double in channels because predicts mean and variance
			    'out_channels': 8,
			    'addition_embed_type': 'image_hint',
			    '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,
			}

			__magic_name__	:   Any					     =					UNetaDConditionModel(**__snake_case      )
			return model







		@property
		def        __lowerCAmelCase		( self		:						Union[str, Any]      )  ->			Optional[Any]:
			return {
			    "block_out_channels": [32, 32, 64, 64],
			    "down_block_types": [
			        "DownEncoderBlock2D",
			        "DownEncoderBlock2D",
			        "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
			    "vq_embed_dim": 4,
			}







		@property
		def        __lowerCAmelCase		( self		:						int      )  ->			Optional[int]:
			torch.manual_seed(0      )
			__magic_name__	:   Union[str, Any]					     =					VQModel(**self.dummy_movq_kwargs      )
			return model







		def        __lowerCAmelCase		( self		:						str      )  ->			Optional[int]:
			__magic_name__	:   Optional[Any]					     =					self.dummy_unet
			__magic_name__	:   List[str]					     =					self.dummy_movq

			__magic_name__	:   Optional[int]					     =					{
			    'num_train_timesteps': 1000,
			    'beta_schedule': 'linear',
			    'beta_start': 0.0_0085,
			    'beta_end': 0.012,
			    'clip_sample': False,
			    'set_alpha_to_one': False,
			    'steps_offset': 0,
			    'prediction_type': 'epsilon',
			    'thresholding': False,
			}

			__magic_name__	:   Optional[Any]					     =					DDIMScheduler(**__snake_case      )

			__magic_name__	:   Optional[int]					     =					{
			    'unet': unet,
			    'scheduler': scheduler,
			    'movq': movq,
			}

			return components







		def        __lowerCAmelCase		( self		:						Tuple  ,					_A		:						Optional[int]  ,					_A		:						str=0      )  ->			Optional[Any]:
			__magic_name__	:   Dict					     =					floats_tensor((1, self.text_embedder_hidden_size)  ,					rng=random.Random(__snake_case      )      ).to(__snake_case      )
			__magic_name__	:   Any					     =					floats_tensor((1, self.text_embedder_hidden_size)  ,					rng=random.Random(seed + 1      )      ).to(
			    __snake_case      )
			# create init_image
			__magic_name__	:   List[str]					     =					floats_tensor((1, 3, 64, 64)  ,					rng=random.Random(__snake_case      )      ).to(__snake_case      )
			__magic_name__	:   List[str]					     =					image.cpu().permute(0  ,					2  ,					3  ,					1      )[0]
			__magic_name__	:   int					     =					Image.fromarray(np.uinta(__snake_case      )      ).convert('RGB'      ).resize((256, 256)      )
			# create hint
			__magic_name__	:   Optional[Any]					     =					floats_tensor((1, 3, 64, 64)  ,					rng=random.Random(__snake_case      )      ).to(__snake_case      )

			if str(__snake_case      ).startswith('mps'      ):
				__magic_name__	:   List[Any]					     =					torch.manual_seed(__snake_case      )
			else:
				__magic_name__	:   Dict					     =					torch.Generator(device=__snake_case      ).manual_seed(__snake_case      )
			__magic_name__	:   List[str]					     =					{
			    'image': init_image,
			    'image_embeds': image_embeds,
			    'negative_image_embeds': negative_image_embeds,
			    'hint': hint,
			    'generator': generator,
			    'height': 64,
			    'width': 64,
			    'num_inference_steps': 10,
			    'guidance_scale': 7.0,
			    'strength': 0.2,
			    'output_type': 'np',
			}
			return inputs







		def        __lowerCAmelCase		( self		:						Tuple      )  ->			Optional[int]:
			__magic_name__	:   str					     =					'cpu'

			__magic_name__	:   Any					     =					self.get_dummy_components()

			__magic_name__	:   Tuple					     =					self.pipeline_class(**__snake_case      )
			__magic_name__	:   Optional[Any]					     =					pipe.to(__snake_case      )

			pipe.set_progress_bar_config(disable=__snake_case      )

			__magic_name__	:   Optional[int]					     =					pipe(**self.get_dummy_inputs(__snake_case      )      )
			__magic_name__	:   Optional[Any]					     =					output.images

			__magic_name__	:   Optional[int]					     =					pipe(
			    **self.get_dummy_inputs(__snake_case      )  ,					return_dict=__snake_case  ,					)[0]

			__magic_name__	:   str					     =					image[0, -3:, -3:, -1]
			__magic_name__	:   List[str]					     =					image_from_tuple[0, -3:, -3:, -1]

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

			__magic_name__	:   Union[str, Any]					     =					np.array(
			    [0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736]      )
			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()}'







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



		'''simple docstring'''







		def        __lowerCAmelCase		( self		:						int      )  ->			Optional[Any]:
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()







		def        __lowerCAmelCase		( self		:						int      )  ->			List[Any]:
			__magic_name__	:   Any					     =					load_numpy(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'      )

			__magic_name__	:   Optional[Any]					     =					load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'      )
			__magic_name__	:   Tuple					     =					init_image.resize((512, 512)      )

			__magic_name__	:   int					     =					load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/kandinskyv22/hint_image_cat.png'      )
			__magic_name__	:   List[Any]					     =					torch.from_numpy(np.array(__snake_case      )      ).float() / 255.0
			__magic_name__	:   Optional[int]					     =					hint.permute(2  ,					0  ,					1      ).unsqueeze(0      )

			__magic_name__	:   Dict					     =					'A robot, 4k photo'

			__magic_name__	:   Union[str, Any]					     =					KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
			    'kandinsky-community/kandinsky-2-2-prior'  ,					torch_dtype=torch.floataa      )
			pipe_prior.to(__snake_case      )

			__magic_name__	:   Optional[Any]					     =					KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
			    'kandinsky-community/kandinsky-2-2-controlnet-depth'  ,					torch_dtype=torch.floataa      )
			__magic_name__	:   Union[str, Any]					     =					pipeline.to(__snake_case      )

			pipeline.set_progress_bar_config(disable=__snake_case      )

			__magic_name__	:   Dict					     =					torch.Generator(device='cpu'      ).manual_seed(0      )

			__magic_name__     ,    __magic_name__	:   Any					     =					pipe_prior(
			    __snake_case  ,					image=__snake_case  ,					strength=0.85  ,					generator=__snake_case  ,					negative_prompt=''  ,					).to_tuple()

			__magic_name__	:   Optional[Any]					     =					pipeline(
			    image=__snake_case  ,					image_embeds=__snake_case  ,					negative_image_embeds=__snake_case  ,					hint=__snake_case  ,					generator=__snake_case  ,					num_inference_steps=100  ,					height=512  ,					width=512  ,					strength=0.5  ,					output_type='np'  ,					)

			__magic_name__	:   List[str]					     =					output.images[0]

			assert image.shape == (512, 512, 3)

			assert_mean_pixel_difference(__snake_case  ,					__snake_case      )



'''simple docstring'''

from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

lowerCAmelCase				:Any        =      TypeVar('''T''')





def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	return (position - 1) // 2





def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	return (2 * position) + 1





def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	return (2 * position) + 2
class _lowerCamelCase	(						Generic[T]							):



		'''simple docstring'''







		def __init__( self		:						Union[str, Any]      )  ->			None:
			__magic_name__	:   list[tuple[T, int]]					     =					[]
			__magic_name__	:   dict[T, int]					     =					{}
			__magic_name__	:   int					     =					0







		def __len__( self		:						Optional[int]      )  ->			int:
			return self.elements







		def __repr__( self		:						Optional[Any]      )  ->			str:
			return str(self.heap      )







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			bool:
			# Check if the priority queue is empty
			return self.elements == 0







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						T  ,					_A		:						int      )  ->			None:
			# Add an element with given priority to the queue
			self.heap.append((elem, weight)      )
			__magic_name__	:   List[str]					     =					self.elements
			self.elements += 1
			self._bubble_up(_A      )







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			T:
			# Remove and return the element with lowest weight (highest priority)
			if self.elements > 1:
				self._swap_nodes(0  ,					self.elements - 1      )
			__magic_name__     ,    __magic_name__	:   Optional[Any]					     =					self.heap.pop()
			del self.position_map[elem]
			self.elements -= 1
			if self.elements > 0:
				__magic_name__     ,    __magic_name__	:   Tuple					     =					self.heap[0]
				self._bubble_down(_A      )
			return elem







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						T  ,					_A		:						int      )  ->			None:
			# Update the weight of the given key
			__magic_name__	:   Optional[Any]					     =					self.position_map[elem]
			__magic_name__	:   Any					     =					(elem, weight)
			if position > 0:
				__magic_name__	:   Optional[int]					     =					get_parent_position(_A      )
				__magic_name__     ,    __magic_name__	:   Any					     =					self.heap[parent_position]
				if parent_weight > weight:
					self._bubble_up(_A      )
				else:
					self._bubble_down(_A      )
			else:
				self._bubble_down(_A      )







		def        __lowerCAmelCase		( self		:						Optional[int]  ,					_A		:						T      )  ->			None:
			# Place a node at the proper position (upward movement) [to be used internally
			# only]
			__magic_name__	:   Union[str, Any]					     =					self.position_map[elem]
			if curr_pos == 0:
				return None
			__magic_name__	:   Optional[Any]					     =					get_parent_position(_A      )
			__magic_name__     ,    __magic_name__	:   Any					     =					self.heap[curr_pos]
			__magic_name__     ,    __magic_name__	:   int					     =					self.heap[parent_position]
			if parent_weight > weight:
				self._swap_nodes(_A  ,					_A      )
				return self._bubble_up(_A      )
			return None







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						T      )  ->			None:
			# Place a node at the proper position (downward movement) [to be used
			# internally only]
			__magic_name__	:   Dict					     =					self.position_map[elem]
			__magic_name__     ,    __magic_name__	:   List[str]					     =					self.heap[curr_pos]
			__magic_name__	:   str					     =					get_child_left_position(_A      )
			__magic_name__	:   List[str]					     =					get_child_right_position(_A      )
			if child_left_position < self.elements and child_right_position < self.elements:
				__magic_name__     ,    __magic_name__	:   int					     =					self.heap[child_left_position]
				__magic_name__     ,    __magic_name__	:   int					     =					self.heap[child_right_position]
				if child_right_weight < child_left_weight and child_right_weight < weight:
					self._swap_nodes(_A  ,					_A      )
					return self._bubble_down(_A      )
			if child_left_position < self.elements:
				__magic_name__     ,    __magic_name__	:   Any					     =					self.heap[child_left_position]
				if child_left_weight < weight:
					self._swap_nodes(_A  ,					_A      )
					return self._bubble_down(_A      )
			else:
				return None
			if child_right_position < self.elements:
				__magic_name__     ,    __magic_name__	:   str					     =					self.heap[child_right_position]
				if child_right_weight < weight:
					self._swap_nodes(_A  ,					_A      )
					return self._bubble_down(_A      )
			return None







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						int  ,					_A		:						int      )  ->			None:
			# Swap the nodes at the given positions
			__magic_name__	:   List[Any]					     =					self.heap[nodea_pos][0]
			__magic_name__	:   Union[str, Any]					     =					self.heap[nodea_pos][0]
			__magic_name__     ,    __magic_name__	:   Optional[Any]					     =					(
			    self.heap[nodea_pos],
			    self.heap[nodea_pos],
			)
			__magic_name__	:   Optional[Any]					     =					nodea_pos
			__magic_name__	:   Optional[Any]					     =					nodea_pos







class _lowerCamelCase	(						Generic[T]							):



		'''simple docstring'''







		def __init__( self		:						Union[str, Any]      )  ->			None:
			__magic_name__	:   dict[T, dict[T, int]]					     =					{}
			__magic_name__	:   int					     =					0







		def __repr__( self		:						str      )  ->			str:
			return str(self.connections      )







		def __len__( self		:						Union[str, Any]      )  ->			int:
			return self.nodes







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						T      )  ->			None:
			# Add a node in the graph if it is not in the graph
			if node not in self.connections:
				__magic_name__	:   str					     =					{}
				self.nodes += 1







		def        __lowerCAmelCase		( self		:						List[str]  ,					_A		:						T  ,					_A		:						T  ,					_A		:						int      )  ->			None:
			# Add an edge between 2 nodes in the graph
			self.add_node(_A      )
			self.add_node(_A      )
			__magic_name__	:   Any					     =					weight
			__magic_name__	:   Dict					     =					weight







def lowerCamelCase      (      lowerCAmelCase    :     GraphUndirectedWeighted[T]		,		):





	"""simple docstring"""







	__magic_name__	:   dict[T, int]					     =					{node: maxsize for node in graph.connections}
	__magic_name__	:   dict[T, T | None]					     =					{node: None for node in graph.connections}

	__magic_name__	:   MinPriorityQueue[T]					     =					MinPriorityQueue()
	for node, weight in dist.items():
		priority_queue.push(lowerCAmelCase		,		lowerCAmelCase		)

	if priority_queue.is_empty():
		return dist, parent

	# initialization
	__magic_name__	:   Any					     =					priority_queue.extract_min()
	__magic_name__	:   List[str]					     =					0
	for neighbour in graph.connections[node]:
		if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
			__magic_name__	:   Any					     =					dist[node] + graph.connections[node][neighbour]
			priority_queue.update_key(lowerCAmelCase		,		dist[neighbour]		)
			__magic_name__	:   Tuple					     =					node

    # running prim's algorithm
	while not priority_queue.is_empty():
		__magic_name__	:   Tuple					     =					priority_queue.extract_min()
		for neighbour in graph.connections[node]:
			if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
				__magic_name__	:   int					     =					dist[node] + graph.connections[node][neighbour]
				priority_queue.update_key(lowerCAmelCase		,		dist[neighbour]		)
				__magic_name__	:   Union[str, Any]					     =					node
	return dist, parent