






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class  __UpperCAmelCase						(_UpperCAmelCase			,_UpperCAmelCase			,_UpperCAmelCase			,unittest.TestCase       ):
			__snake_case							: List[Any]							=				StableDiffusionImageVariationPipeline
			__snake_case							: Dict							=				IMAGE_VARIATION_PARAMS
			__snake_case							: List[str]							=				IMAGE_VARIATION_BATCH_PARAMS
			__snake_case							: str							=				frozenset([]       )
			# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			__snake_case							: List[str]							=				frozenset([]       )







			def 				UpperCamelCase (      self:    str	):







									'''simple docstring'''




									torch.manual_seed(0	)
									_SCREAMING_SNAKE_CASE       =    UNetaDConditionModel(
									    block_out_channels=(32, 64) ,      layers_per_block=2 ,      sample_size=32 ,      in_channels=4 ,      out_channels=4 ,      down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,      up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") ,      cross_attention_dim=32 ,      )
									_SCREAMING_SNAKE_CASE       =    PNDMScheduler(skip_prk_steps=UpperCAmelCase_	)
									torch.manual_seed(0	)
									_SCREAMING_SNAKE_CASE       =    AutoencoderKL(
									    block_out_channels=[32, 64] ,      in_channels=3 ,      out_channels=3 ,      down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,      up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,      latent_channels=4 ,      )
									torch.manual_seed(0	)
									_SCREAMING_SNAKE_CASE       =    CLIPVisionConfig(
									    hidden_size=32 ,      projection_dim=32 ,      intermediate_size=37 ,      layer_norm_eps=1E-05 ,      num_attention_heads=4 ,      num_hidden_layers=5 ,      image_size=32 ,      patch_size=4 ,      )
									_SCREAMING_SNAKE_CASE       =    CLIPVisionModelWithProjection(UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    CLIPImageProcessor(crop_size=32 ,      size=32	)

									_SCREAMING_SNAKE_CASE       =    {
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """vae""": vae,
									    """image_encoder""": image_encoder,
									    """feature_extractor""": feature_extractor,
									    """safety_checker""": None,
									}
									return components







			def 				UpperCamelCase (      self:    Any ,      UpperCAmelCase_:    Optional[Any] ,      UpperCAmelCase_:    str=0	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    floats_tensor((1, 3, 32, 32) ,      rng=random.Random(UpperCAmelCase_	)	)
									_SCREAMING_SNAKE_CASE       =    image.cpu().permute(0 ,      2 ,      3 ,      1	)[0]
									_SCREAMING_SNAKE_CASE       =    Image.fromarray(np.uinta(UpperCAmelCase_	)	).convert("""RGB"""	).resize((32, 32)	)
									if str(UpperCAmelCase_	).startswith("""mps"""	):
															_SCREAMING_SNAKE_CASE       =    torch.manual_seed(UpperCAmelCase_	)
									else:
															_SCREAMING_SNAKE_CASE       =    torch.Generator(device=UpperCAmelCase_	).manual_seed(UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    {
									    """image""": image,
									    """generator""": generator,
									    """num_inference_steps""": 2,
									    """guidance_scale""": 6.0,
									    """output_type""": """numpy""",
									}
									return inputs







			def 				UpperCamelCase (      self:    Any	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    """cpu"""  # ensure determinism for the device-dependent torch.Generator
									_SCREAMING_SNAKE_CASE       =    self.get_dummy_components()
									_SCREAMING_SNAKE_CASE       =    StableDiffusionImageVariationPipeline(**UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    sd_pipe.to(UpperCAmelCase_	)
									sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_	)

									_SCREAMING_SNAKE_CASE       =    self.get_dummy_inputs(UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    sd_pipe(**UpperCAmelCase_	).images
									_SCREAMING_SNAKE_CASE       =    image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)
									_SCREAMING_SNAKE_CASE       =    np.array([0.52_39, 0.57_23, 0.47_96, 0.50_49, 0.55_50, 0.46_85, 0.53_29, 0.48_91, 0.49_21]	)

									assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3







			def 				UpperCamelCase (      self:    Any	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    """cpu"""  # ensure determinism for the device-dependent torch.Generator
									_SCREAMING_SNAKE_CASE       =    self.get_dummy_components()
									_SCREAMING_SNAKE_CASE       =    StableDiffusionImageVariationPipeline(**UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    sd_pipe.to(UpperCAmelCase_	)
									sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_	)

									_SCREAMING_SNAKE_CASE       =    self.get_dummy_inputs(UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    2 * [inputs["""image"""]]
									_SCREAMING_SNAKE_CASE       =    sd_pipe(**UpperCAmelCase_	)

									_SCREAMING_SNAKE_CASE       =    output.images

									_SCREAMING_SNAKE_CASE       =    image[-1, -3:, -3:, -1]

									assert image.shape == (2, 64, 64, 3)
									_SCREAMING_SNAKE_CASE       =    np.array([0.68_92, 0.56_37, 0.58_36, 0.57_71, 0.62_54, 0.64_09, 0.55_80, 0.55_69, 0.52_89]	)

									assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-3







			def 				UpperCamelCase (      self:    int	):







									'''simple docstring'''




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



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







			def 				UpperCamelCase (      self:    Tuple	):







									'''simple docstring'''




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







			def 				UpperCamelCase (      self:    Any ,      UpperCAmelCase_:    Optional[int] ,      UpperCAmelCase_:    List[Any]="cpu" ,      UpperCAmelCase_:    Tuple=torch.floataa ,      UpperCAmelCase_:    Any=0	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    torch.Generator(device=UpperCAmelCase_	).manual_seed(UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    load_image(
									    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
									    """/stable_diffusion_imgvar/input_image_vermeer.png"""	)
									_SCREAMING_SNAKE_CASE       =    np.random.RandomState(UpperCAmelCase_	).standard_normal((1, 4, 64, 64)	)
									_SCREAMING_SNAKE_CASE       =    torch.from_numpy(UpperCAmelCase_	).to(device=UpperCAmelCase_ ,      dtype=UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    {
									    """image""": init_image,
									    """latents""": latents,
									    """generator""": generator,
									    """num_inference_steps""": 3,
									    """guidance_scale""": 7.5,
									    """output_type""": """numpy""",
									}
									return inputs







			def 				UpperCamelCase (      self:    str	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    StableDiffusionImageVariationPipeline.from_pretrained(
									    """lambdalabs/sd-image-variations-diffusers""" ,      safety_checker=UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    sd_pipe.to(UpperCAmelCase_	)
									sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_	)

									_SCREAMING_SNAKE_CASE       =    self.get_inputs(UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    sd_pipe(**UpperCAmelCase_	).images
									_SCREAMING_SNAKE_CASE       =    image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 512, 3)
									_SCREAMING_SNAKE_CASE       =    np.array([0.8_44_91, 0.9_07_89, 0.7_57_08, 0.7_87_34, 0.8_34_85, 0.7_00_99, 0.6_69_38, 0.6_87_27, 0.6_13_79]	)
									assert np.abs(image_slice - expected_slice	).max() < 6E-3







			def 				UpperCamelCase (      self:    List[str]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    0

									def callback_fn(UpperCAmelCase_:    int ,      UpperCAmelCase_:    int ,      UpperCAmelCase_:    torch.FloatTensor	) -> None:
															_SCREAMING_SNAKE_CASE       =    True
															nonlocal number_of_steps
															number_of_steps += 1
															if step == 1:
																					_SCREAMING_SNAKE_CASE       =    latents.detach().cpu().numpy()
																					assert latents.shape == (1, 4, 64, 64)
																					_SCREAMING_SNAKE_CASE       =    latents[0, -3:, -3:, -1]
																					_SCREAMING_SNAKE_CASE       =    np.array(
																					    [-0.16_21, 0.28_37, -0.79_79, -0.12_21, -1.30_57, 0.76_81, -2.11_91, 0.04_64, 1.63_09]	)

																					assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5E-2
															elif step == 2:
																					_SCREAMING_SNAKE_CASE       =    latents.detach().cpu().numpy()
																					assert latents.shape == (1, 4, 64, 64)
																					_SCREAMING_SNAKE_CASE       =    latents[0, -3:, -3:, -1]
																					_SCREAMING_SNAKE_CASE       =    np.array([0.62_99, 1.75_00, 1.19_92, -2.15_82, -1.89_94, 0.73_34, -0.70_90, 1.01_37, 1.52_73]	)

																					assert np.abs(latents_slice.flatten() - expected_slice	).max() < 5E-2

									_SCREAMING_SNAKE_CASE       =    False

									_SCREAMING_SNAKE_CASE       =    StableDiffusionImageVariationPipeline.from_pretrained(
									    """fusing/sd-image-variations-diffusers""" ,      safety_checker=UpperCAmelCase_ ,      torch_dtype=torch.floataa ,      )
									pipe.to(UpperCAmelCase_	)
									pipe.set_progress_bar_config(disable=UpperCAmelCase_	)
									pipe.enable_attention_slicing()

									_SCREAMING_SNAKE_CASE       =    self.get_inputs(UpperCAmelCase_ ,      dtype=torch.floataa	)
									pipe(**UpperCAmelCase_ ,      callback=UpperCAmelCase_ ,      callback_steps=1	)
									assert callback_fn.has_been_called
									assert number_of_steps == inputs["num_inference_steps"]







			def 				UpperCamelCase (      self:    Union[str, Any]	):







									'''simple docstring'''




									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									_SCREAMING_SNAKE_CASE       =    """fusing/sd-image-variations-diffusers"""
									_SCREAMING_SNAKE_CASE       =    StableDiffusionImageVariationPipeline.from_pretrained(
									    UpperCAmelCase_ ,      safety_checker=UpperCAmelCase_ ,      torch_dtype=torch.floataa	)
									_SCREAMING_SNAKE_CASE       =    pipe.to(UpperCAmelCase_	)
									pipe.set_progress_bar_config(disable=UpperCAmelCase_	)
									pipe.enable_attention_slicing(1	)
									pipe.enable_sequential_cpu_offload()

									_SCREAMING_SNAKE_CASE       =    self.get_inputs(UpperCAmelCase_ ,      dtype=torch.floataa	)
									_SCREAMING_SNAKE_CASE       =    pipe(**UpperCAmelCase_	)

									_SCREAMING_SNAKE_CASE       =    torch.cuda.max_memory_allocated()
									# make sure that less than 2.6 GB is allocated
									assert mem_bytes < 2.6 * 10**9



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







			def 				UpperCamelCase (      self:    str	):







									'''simple docstring'''




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







			def 				UpperCamelCase (      self:    Tuple ,      UpperCAmelCase_:    str ,      UpperCAmelCase_:    Any="cpu" ,      UpperCAmelCase_:    Union[str, Any]=torch.floataa ,      UpperCAmelCase_:    Optional[Any]=0	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    torch.Generator(device=UpperCAmelCase_	).manual_seed(UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    load_image(
									    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
									    """/stable_diffusion_imgvar/input_image_vermeer.png"""	)
									_SCREAMING_SNAKE_CASE       =    np.random.RandomState(UpperCAmelCase_	).standard_normal((1, 4, 64, 64)	)
									_SCREAMING_SNAKE_CASE       =    torch.from_numpy(UpperCAmelCase_	).to(device=UpperCAmelCase_ ,      dtype=UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    {
									    """image""": init_image,
									    """latents""": latents,
									    """generator""": generator,
									    """num_inference_steps""": 50,
									    """guidance_scale""": 7.5,
									    """output_type""": """numpy""",
									}
									return inputs







			def 				UpperCamelCase (      self:    List[Any]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    StableDiffusionImageVariationPipeline.from_pretrained("""fusing/sd-image-variations-diffusers"""	)
									sd_pipe.to(UpperCAmelCase_	)
									sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_	)

									_SCREAMING_SNAKE_CASE       =    self.get_inputs(UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    sd_pipe(**UpperCAmelCase_	).images[0]

									_SCREAMING_SNAKE_CASE       =    load_numpy(
									    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
									    """/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy"""	)
									_SCREAMING_SNAKE_CASE       =    np.abs(expected_image - image	).max()
									assert max_diff < 1E-3







			def 				UpperCamelCase (      self:    List[str]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    StableDiffusionImageVariationPipeline.from_pretrained("""fusing/sd-image-variations-diffusers"""	)
									_SCREAMING_SNAKE_CASE       =    DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config	)
									sd_pipe.to(UpperCAmelCase_	)
									sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_	)

									_SCREAMING_SNAKE_CASE       =    self.get_inputs(UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    25
									_SCREAMING_SNAKE_CASE       =    sd_pipe(**UpperCAmelCase_	).images[0]

									_SCREAMING_SNAKE_CASE       =    load_numpy(
									    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
									    """/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy"""	)
									_SCREAMING_SNAKE_CASE       =    np.abs(expected_image - image	).max()
									assert max_diff < 1E-3









def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Dict:






						"""simple docstring"""





						if not root or root not in binary_tree_mirror_dictionary:
												return
						_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    binary_tree_mirror_dictionary[root][:2]
						_SCREAMING_SNAKE_CASE       =    [right_child, left_child]
						binary_tree_mirror_dict(snake_case__			,snake_case__						)
						binary_tree_mirror_dict(snake_case__			,snake_case__						)

def 			__lowerCamelCase     (						snake_case__			,snake_case__ = 1						)				->    dict:






						"""simple docstring"""





						if not binary_tree:
												raise ValueError("""binary tree cannot be empty"""						)
						if root not in binary_tree:
												_SCREAMING_SNAKE_CASE       =    F'root {root} is not present in the binary_tree'
												raise ValueError(snake_case__						)
						_SCREAMING_SNAKE_CASE       =    dict(snake_case__						)
						binary_tree_mirror_dict(snake_case__			,snake_case__						)
						return binary_tree_mirror_dictionary


if __name__ == "__main__":
		UpperCamelCase												=      {1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
		print(f"Binary tree: {binary_tree}")
		UpperCamelCase												=      binary_tree_mirror(binary_tree, 5)
		print(f"Binary tree mirror: {binary_tree_mirror_dictionary}")

