



"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    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 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_		,       unittest.TestCase       ):
					_a             =							KandinskyVaaImgaImgPipeline
					_a             =							['image_embeds', 'negative_image_embeds', 'image']
					_a             =							[
					    'image_embeds',
					    'negative_image_embeds',
					    'image',
					]
					_a             =							[
					    'generator',
					    'height',
					    'width',
					    'strength',
					    'guidance_scale',
					    'num_inference_steps',
					    'return_dict',
					    'guidance_scale',
					    'num_images_per_prompt',
					    'output_type',
					    'return_dict',
					]
					_a             =							False



					@property
					def 		snake_case	(	self							:  List[str]					)-> Union[str, Any]:
								return 32



					@property
					def 		snake_case	(	self							:  Optional[Any]					)-> int:
								return 32



					@property
					def 		snake_case	(	self							:  Union[str, Any]					)-> Tuple:
								return self.time_input_dim



					@property
					def 		snake_case	(	self							:  List[str]					)-> str:
								return self.time_input_dim * 4



					@property
					def 		snake_case	(	self							:  Optional[Any]					)-> str:
								return 100



					@property
					def 		snake_case	(	self							:  Any					)-> Dict:
								torch.manual_seed(0					)

								lowerCamelCase__		:		Any		     ={
								    '''in_channels''': 4,
								    # 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__		:		Tuple		     =UNetaDConditionModel(**lowerCamelCase					)
								return model



					@property
					def 		snake_case	(	self							:  str					)-> str:
								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 		snake_case	(	self							:  List[Any]					)-> Dict:
								torch.manual_seed(0					)
								lowerCamelCase__		:		int		     =VQModel(**self.dummy_movq_kwargs					)
								return model



					def 		snake_case	(	self							:  Any					)-> List[Any]:
								lowerCamelCase__		:		List[Any]		     =self.dummy_unet
								lowerCamelCase__		:		str		     =self.dummy_movq

								lowerCamelCase__		:		Union[str, Any]		     ={
								    '''num_train_timesteps''': 1000,
								    '''beta_schedule''': '''linear''',
								    '''beta_start''': 0.00_085,
								    '''beta_end''': 0.012,
								    '''clip_sample''': False,
								    '''set_alpha_to_one''': False,
								    '''steps_offset''': 0,
								    '''prediction_type''': '''epsilon''',
								    '''thresholding''': False,
								}

								lowerCamelCase__		:		List[str]		     =DDIMScheduler(**lowerCamelCase					)

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

								return components



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

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




					def 		snake_case	(	self							:  Any					)-> int:
								lowerCamelCase__		:		Union[str, Any]		     ='''cpu'''

								lowerCamelCase__		:		List[str]		     =self.get_dummy_components()

								lowerCamelCase__		:		str		     =self.pipeline_class(**lowerCamelCase					)
								lowerCamelCase__		:		Optional[int]		     =pipe.to(lowerCamelCase					)

								pipe.set_progress_bar_config(disable=lowerCamelCase					)

								lowerCamelCase__		:		Tuple		     =pipe(**self.get_dummy_inputs(lowerCamelCase					)					)
								lowerCamelCase__		:		Union[str, Any]		     =output.images

								lowerCamelCase__		:		Tuple		     =pipe(
								    **self.get_dummy_inputs(lowerCamelCase					),  return_dict=lowerCamelCase,  )[0]

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

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

								lowerCamelCase__		:		Optional[int]		     =np.array(
								    [0.6_199_778, 0.63_984_406, 0.46_145_785, 0.62_944_984, 0.5_622_215, 0.47_306_132, 0.47_441_456, 0.4_607_606, 0.48_719_263]					)
								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 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):



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




					def 		snake_case	(	self							:  Optional[int]					)-> Tuple:
								lowerCamelCase__		:		List[str]		     =load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/kandinskyv22/kandinskyv22_img2img_frog.npy'''					)

								lowerCamelCase__		:		int		     =load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''					)
								lowerCamelCase__		:		Dict		     ='''A red cartoon frog, 4k'''

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

								lowerCamelCase__		:		List[str]		     =KandinskyVaaImgaImgPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-2-decoder''',  torch_dtype=torch.floataa					)
								lowerCamelCase__		:		str		     =pipeline.to(lowerCamelCase					)

								pipeline.set_progress_bar_config(disable=lowerCamelCase					)

								lowerCamelCase__		:		Tuple		     =torch.Generator(device='''cpu'''					).manual_seed(0					)
								lowerCamelCase__		:		List[str]		     =pipe_prior(
								    lowerCamelCase,  generator=lowerCamelCase,  num_inference_steps=5,  negative_prompt='''''',  ).to_tuple()

								lowerCamelCase__		:		Dict		     =pipeline(
								    image=lowerCamelCase,  image_embeds=lowerCamelCase,  negative_image_embeds=lowerCamelCase,  generator=lowerCamelCase,  num_inference_steps=100,  height=768,  width=768,  strength=0.2,  output_type='''np''',  )

								lowerCamelCase__		:		Optional[Any]		     =output.images[0]

								assert image.shape == (768, 768, 3)

								assert_mean_pixel_difference(lowerCamelCase,  lowerCamelCase					)









"""simple docstring"""






def 					snake_case__       (		__lowerCamelCase    :   int     ):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]		     =[0] * len(__lowerCamelCase     )
			lowerCamelCase__		:		List[Any]		     =[]
			lowerCamelCase__		:		List[Any]		     =[1] * len(__lowerCamelCase     )

			for values in graph.values():
						for i in values:
									indegree[i] += 1

			for i in range(len(__lowerCamelCase     )     ):
						if indegree[i] == 0:
									queue.append(__lowerCamelCase     )

			while queue:
						lowerCamelCase__		:		Tuple		     =queue.pop(0     )
						for x in graph[vertex]:
									indegree[x] -= 1

									if long_dist[vertex] + 1 > long_dist[x]:
												lowerCamelCase__		:		Optional[Any]		     =long_dist[vertex] + 1

									if indegree[x] == 0:
												queue.append(__lowerCamelCase     )

			print(max(__lowerCamelCase     )     )


# Adjacency list of Graph
_lowercase			:			Optional[Any]										=						{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}

longest_distance(graph)




