


"""simple docstring"""
from __future__ import annotations







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				set[str]:
							snake_case_       ,						snake_case_           =		set(_SCREAMING_SNAKE_CASE				), [start]

							while stack:
														snake_case_           =		stack.pop()
														explored.add(_SCREAMING_SNAKE_CASE				)
														# Differences from BFS:
														# 1) pop last element instead of first one
														# 2) add adjacent elements to stack without exploring them
														for adj in reversed(graph[v]				):
																					if adj not in explored:
																												stack.append(_SCREAMING_SNAKE_CASE				)
							return explored


__SCREAMING_SNAKE_CASE							:    List[str]		=    {
    'A': ['B', 'C', 'D'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B', 'D'],
    'E': ['B', 'F'],
    'F': ['C', 'E', 'G'],
    'G': ['F'],
}

if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(depth_first_search(G, 'A'))







"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    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     (snake_case__						,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		Dict							      =    KandinskyVaaControlnetPipeline
							__lowercase:		str							      =    ["""image_embeds""", """negative_image_embeds""", """hint"""]
							__lowercase:		List[str]							      =    ["""image_embeds""", """negative_image_embeds""", """hint"""]
							__lowercase:		Union[str, Any]							      =    [
							    """generator""",
							    """height""",
							    """width""",
							    """latents""",
							    """guidance_scale""",
							    """num_inference_steps""",
							    """return_dict""",
							    """guidance_scale""",
							    """num_images_per_prompt""",
							    """output_type""",
							    """return_dict""",
							]
							__lowercase:		Tuple							      =    False



							@property
							def 		lowerCAmelCase       (				self							:      Any       )					->Union[str, Any]:



														"""simple docstring"""



														return 32



							@property
							def 		lowerCAmelCase       (				self							:      Optional[Any]       )					->Union[str, Any]:



														"""simple docstring"""



														return 32



							@property
							def 		lowerCAmelCase       (				self							:      int       )					->List[str]:



														"""simple docstring"""



														return self.time_input_dim



							@property
							def 		lowerCAmelCase       (				self							:      Union[str, Any]       )					->Union[str, Any]:



														"""simple docstring"""



														return self.time_input_dim * 4



							@property
							def 		lowerCAmelCase       (				self							:      Dict       )					->Optional[int]:



														"""simple docstring"""



														return 100



							@property
							def 		lowerCAmelCase       (				self							:      str       )					->List[Any]:



														"""simple docstring"""



														torch.manual_seed(0       )

														snake_case_           =		{
														    """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,
														}

														snake_case_           =		UNetaDConditionModel(**UpperCAmelCase_       )
														return model



							@property
							def 		lowerCAmelCase       (				self							:      Any       )					->Optional[Any]:



														"""simple docstring"""



														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							:      Any       )					->int:



														"""simple docstring"""



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



							def 		lowerCAmelCase       (				self							:      Dict       )					->str:



														"""simple docstring"""



														snake_case_           =		self.dummy_unet
														snake_case_           =		self.dummy_movq

														snake_case_           =		DDIMScheduler(
														    num_train_timesteps=1_000		,       beta_schedule="""linear"""		,       beta_start=0.00_085		,       beta_end=0.012		,       clip_sample=UpperCAmelCase_		,       set_alpha_to_one=UpperCAmelCase_		,       steps_offset=1		,       prediction_type="""epsilon"""		,       thresholding=UpperCAmelCase_		,       )

														snake_case_           =		{
														    """unet""": unet,
														    """scheduler""": scheduler,
														    """movq""": movq,
														}
														return components



							def 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      List[Any]		,       UpperCAmelCase_							:      Optional[Any]=0       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		floats_tensor((1, self.text_embedder_hidden_size)		,       rng=random.Random(UpperCAmelCase_       )       ).to(UpperCAmelCase_       )
														snake_case_           =		floats_tensor((1, self.text_embedder_hidden_size)		,       rng=random.Random(seed + 1       )       ).to(
														    UpperCAmelCase_       )

														# create hint
														snake_case_           =		floats_tensor((1, 3, 64, 64)		,       rng=random.Random(UpperCAmelCase_       )       ).to(UpperCAmelCase_       )

														if str(UpperCAmelCase_       ).startswith("""mps"""       ):
																					snake_case_           =		torch.manual_seed(UpperCAmelCase_       )
														else:
																					snake_case_           =		torch.Generator(device=UpperCAmelCase_       ).manual_seed(UpperCAmelCase_       )
														snake_case_           =		{
														    """image_embeds""": image_embeds,
														    """negative_image_embeds""": negative_image_embeds,
														    """hint""": hint,
														    """generator""": generator,
														    """height""": 64,
														    """width""": 64,
														    """guidance_scale""": 4.0,
														    """num_inference_steps""": 2,
														    """output_type""": """np""",
														}
														return inputs



							def 		lowerCAmelCase       (				self							:      List[str]       )					->List[Any]:



														"""simple docstring"""



														snake_case_           =		"""cpu"""

														snake_case_           =		self.get_dummy_components()

														snake_case_           =		self.pipeline_class(**UpperCAmelCase_       )
														snake_case_           =		pipe.to(UpperCAmelCase_       )

														pipe.set_progress_bar_config(disable=UpperCAmelCase_       )

														snake_case_           =		pipe(**self.get_dummy_inputs(UpperCAmelCase_       )       )
														snake_case_           =		output.images

														snake_case_           =		pipe(
														    **self.get_dummy_inputs(UpperCAmelCase_       )		,       return_dict=UpperCAmelCase_		,       )[0]

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

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

														snake_case_           =		np.array(
														    [0.6_959_826, 0.868_279, 0.7_558_092, 0.68_769_467, 0.85_805_804, 0.65_977_496, 0.44_885_302, 0.5_959_111, 0.4_251_595]       )

														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      __A     (unittest.TestCase):





							'''simple docstring'''

							def 		lowerCAmelCase       (				self							:      List[Any]       )					->Union[str, Any]:



														"""simple docstring"""



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



							def 		lowerCAmelCase       (				self							:      List[str]       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"""       )

														snake_case_           =		load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/kandinskyv22/hint_image_cat.png"""       )
														snake_case_           =		torch.from_numpy(np.array(UpperCAmelCase_       )       ).float() / 255.0
														snake_case_           =		hint.permute(2		,       0		,       1       ).unsqueeze(0       )

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

														snake_case_           =		KandinskyVaaControlnetPipeline.from_pretrained(
														    """kandinsky-community/kandinsky-2-2-controlnet-depth"""		,       torch_dtype=torch.floataa       )
														snake_case_           =		pipeline.to(UpperCAmelCase_       )
														pipeline.set_progress_bar_config(disable=UpperCAmelCase_       )

														snake_case_           =		"""A robot, 4k photo"""

														snake_case_           =		torch.Generator(device="""cuda"""       ).manual_seed(0       )
														snake_case_       ,						snake_case_           =		pipe_prior(
														    UpperCAmelCase_		,       generator=UpperCAmelCase_		,       num_inference_steps=5		,       negative_prompt=""""""		,       ).to_tuple()

														snake_case_           =		torch.Generator(device="""cuda"""       ).manual_seed(0       )
														snake_case_           =		pipeline(
														    image_embeds=UpperCAmelCase_		,       negative_image_embeds=UpperCAmelCase_		,       hint=UpperCAmelCase_		,       generator=UpperCAmelCase_		,       num_inference_steps=100		,       output_type="""np"""		,       )

														snake_case_           =		output.images[0]

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

														assert_mean_pixel_difference(UpperCAmelCase_		,       UpperCAmelCase_       )



