
"""simple docstring"""


class    A_     :



							"""simple docstring"""






							def __init__(  self  :List[str]					,							lowerCamelCase_  :int	):





										"""simple docstring"""

										lowerCamelCase__     :					Optional[Any]		=data
										lowerCamelCase__     :					BinaryTreeNode | None		=None
										lowerCamelCase__     :					BinaryTreeNode | None		=None






def        lowerCAmelCase_       (		snake_case_		:      BinaryTreeNode | None							,   snake_case_		:      int      )       ->BinaryTreeNode | None:
			if node is None:
						lowerCamelCase__     :					List[Any]		=BinaryTreeNode(snake_case_      )
						return node

			# binary search tree is not empty,
			# so we will insert it into the tree
			# if new_value is less than value of data in node,
			#  add it to left subtree and proceed recursively
			if new_value < node.data:
						lowerCamelCase__     :					Dict		=insert(node.left_child							,   snake_case_      )
			else:
						# if new_value is greater than value of data in node,
						#  add it to right subtree and proceed recursively
						lowerCamelCase__     :					List[str]		=insert(node.right_child							,   snake_case_      )
			return node






def        lowerCAmelCase_       (		snake_case_		:      None | BinaryTreeNode      )       ->list[int]:  # if node is None,return
			if node:
						lowerCamelCase__     :					Optional[int]		=inorder(node.left_child      )
						lowerCamelCase__     :					List[str]		=[*inorder_array, node.data]
						lowerCamelCase__     :					str		=inorder_array + inorder(node.right_child      )
			else:
						lowerCamelCase__     :					Optional[Any]		=[]
			return inorder_array






def        lowerCAmelCase_       (		)       ->BinaryTreeNode | None:
			lowerCamelCase__     :					List[Any]		=insert(snake_case_							,   1_5      )
			insert(snake_case_							,   1_0      )
			insert(snake_case_							,   2_5      )
			insert(snake_case_							,   6      )
			insert(snake_case_							,   1_4      )
			insert(snake_case_							,   2_0      )
			insert(snake_case_							,   6_0      )
			return root






def        lowerCAmelCase_       (		)       ->None:
			# main function
			lowerCamelCase__     :					Dict		=make_tree()
			print('Printing values of binary search tree in Inorder Traversal.'      )
			inorder(snake_case_      )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()

"""simple docstring"""


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    A_     (					A__				, A__				, A__				, unittest.TestCase       ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_     				=       StableDiffusionImageVariationPipeline
							SCREAMING_SNAKE_CASE_     				=       IMAGE_VARIATION_PARAMS
							SCREAMING_SNAKE_CASE_     				=       IMAGE_VARIATION_BATCH_PARAMS
							SCREAMING_SNAKE_CASE_     				=       frozenset([]       )
							# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							SCREAMING_SNAKE_CASE_     				=       frozenset([]       )





							def 				UpperCAmelCase__							(  self  :str	):





										"""simple docstring"""

										torch.manual_seed(0	)
										lowerCamelCase__     :					int		=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					,							)
										lowerCamelCase__     :					int		=PNDMScheduler(skip_prk_steps=lowerCamelCase_	)
										torch.manual_seed(0	)
										lowerCamelCase__     :					Optional[int]		=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	)
										lowerCamelCase__     :					Optional[int]		=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					,							)
										lowerCamelCase__     :					Optional[Any]		=CLIPVisionModelWithProjection(lowerCamelCase_	)
										lowerCamelCase__     :					List[str]		=CLIPImageProcessor(crop_size=32					,							size=32	)

										lowerCamelCase__     :					Optional[int]		={
										    'unet': unet,
										    'scheduler': scheduler,
										    'vae': vae,
										    'image_encoder': image_encoder,
										    'feature_extractor': feature_extractor,
										    'safety_checker': None,
										}
										return components





							def 				UpperCAmelCase__							(  self  :List[Any]					,							lowerCamelCase_  :List[Any]					,							lowerCamelCase_  :Optional[int]=0	):





										"""simple docstring"""

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





							def 				UpperCAmelCase__							(  self  :Optional[int]	):





										"""simple docstring"""

										lowerCamelCase__     :					str		='cpu'  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase__     :					List[str]		=self.get_dummy_components()
										lowerCamelCase__     :					Union[str, Any]		=StableDiffusionImageVariationPipeline(**lowerCamelCase_	)
										lowerCamelCase__     :					int		=sd_pipe.to(lowerCamelCase_	)
										sd_pipe.set_progress_bar_config(disable=lowerCamelCase_	)

										lowerCamelCase__     :					str		=self.get_dummy_inputs(lowerCamelCase_	)
										lowerCamelCase__     :					List[str]		=sd_pipe(**lowerCamelCase_	).images
										lowerCamelCase__     :					Tuple		=image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)
										lowerCamelCase__     :					str		=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  :Tuple	):





										"""simple docstring"""

										lowerCamelCase__     :					Any		='cpu'  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase__     :					Any		=self.get_dummy_components()
										lowerCamelCase__     :					Any		=StableDiffusionImageVariationPipeline(**lowerCamelCase_	)
										lowerCamelCase__     :					Dict		=sd_pipe.to(lowerCamelCase_	)
										sd_pipe.set_progress_bar_config(disable=lowerCamelCase_	)

										lowerCamelCase__     :					Any		=self.get_dummy_inputs(lowerCamelCase_	)
										lowerCamelCase__     :					Union[str, Any]		=2 * [inputs['image']]
										lowerCamelCase__     :					Optional[int]		=sd_pipe(**lowerCamelCase_	)

										lowerCamelCase__     :					str		=output.images

										lowerCamelCase__     :					str		=image[-1, -3:, -3:, -1]

										assert image.shape == (2, 64, 64, 3)
										lowerCamelCase__     :					int		=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  :List[str]	):





										"""simple docstring"""

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






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



							"""simple docstring"""






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





										"""simple docstring"""

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





							def 				UpperCAmelCase__							(  self  :Union[str, Any]					,							lowerCamelCase_  :Optional[Any]					,							lowerCamelCase_  :Optional[Any]="cpu"					,							lowerCamelCase_  :Any=torch.floataa					,							lowerCamelCase_  :str=0	):





										"""simple docstring"""

										lowerCamelCase__     :					Union[str, Any]		=torch.Generator(device=lowerCamelCase_	).manual_seed(lowerCamelCase_	)
										lowerCamelCase__     :					Tuple		=load_image(
										    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
										    '/stable_diffusion_imgvar/input_image_vermeer.png'	)
										lowerCamelCase__     :					str		=np.random.RandomState(lowerCamelCase_	).standard_normal((1, 4, 64, 64)	)
										lowerCamelCase__     :					List[str]		=torch.from_numpy(lowerCamelCase_	).to(device=lowerCamelCase_					,							dtype=lowerCamelCase_	)
										lowerCamelCase__     :					str		={
										    '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"""

										lowerCamelCase__     :					Tuple		=StableDiffusionImageVariationPipeline.from_pretrained(
										    'lambdalabs/sd-image-variations-diffusers'					,							safety_checker=lowerCamelCase_	)
										lowerCamelCase__     :					List[Any]		=sd_pipe.to(lowerCamelCase_	)
										sd_pipe.set_progress_bar_config(disable=lowerCamelCase_	)

										lowerCamelCase__     :					Union[str, Any]		=self.get_inputs(lowerCamelCase_	)
										lowerCamelCase__     :					Dict		=sd_pipe(**lowerCamelCase_	).images
										lowerCamelCase__     :					Optional[int]		=image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 512, 512, 3)
										lowerCamelCase__     :					List[str]		=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  :Optional[Any]	):





										"""simple docstring"""

										lowerCamelCase__     :					Optional[Any]		=0

										def callback_fn(lowerCamelCase_  :int					,							lowerCamelCase_  :int					,							lowerCamelCase_  :torch.FloatTensor	) -> None:
													lowerCamelCase__     :					Union[str, Any]		=True
													nonlocal number_of_steps
													number_of_steps += 1
													if step == 1:
																lowerCamelCase__     :					Dict		=latents.detach().cpu().numpy()
																assert latents.shape == (1, 4, 64, 64)
																lowerCamelCase__     :					str		=latents[0, -3:, -3:, -1]
																lowerCamelCase__     :					str		=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:
																lowerCamelCase__     :					Union[str, Any]		=latents.detach().cpu().numpy()
																assert latents.shape == (1, 4, 64, 64)
																lowerCamelCase__     :					Dict		=latents[0, -3:, -3:, -1]
																lowerCamelCase__     :					Optional[Any]		=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

										lowerCamelCase__     :					str		=False

										lowerCamelCase__     :					Optional[Any]		=StableDiffusionImageVariationPipeline.from_pretrained(
										    'fusing/sd-image-variations-diffusers'					,							safety_checker=lowerCamelCase_					,							torch_dtype=torch.floataa					,							)
										pipe.to(lowerCamelCase_	)
										pipe.set_progress_bar_config(disable=lowerCamelCase_	)
										pipe.enable_attention_slicing()

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





							def 				UpperCAmelCase__							(  self  :List[Any]	):





										"""simple docstring"""

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

										lowerCamelCase__     :					str		='fusing/sd-image-variations-diffusers'
										lowerCamelCase__     :					Union[str, Any]		=StableDiffusionImageVariationPipeline.from_pretrained(
										    lowerCamelCase_					,							safety_checker=lowerCamelCase_					,							torch_dtype=torch.floataa	)
										lowerCamelCase__     :					Any		=pipe.to(lowerCamelCase_	)
										pipe.set_progress_bar_config(disable=lowerCamelCase_	)
										pipe.enable_attention_slicing(1	)
										pipe.enable_sequential_cpu_offload()

										lowerCamelCase__     :					Dict		=self.get_inputs(lowerCamelCase_					,							dtype=torch.floataa	)
										lowerCamelCase__     :					Tuple		=pipe(**lowerCamelCase_	)

										lowerCamelCase__     :					Any		=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    A_     (					unittest.TestCase       ):



							"""simple docstring"""






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





										"""simple docstring"""

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





							def 				UpperCAmelCase__							(  self  :List[str]					,							lowerCamelCase_  :Optional[int]					,							lowerCamelCase_  :Union[str, Any]="cpu"					,							lowerCamelCase_  :Union[str, Any]=torch.floataa					,							lowerCamelCase_  :Union[str, Any]=0	):





										"""simple docstring"""

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





							def 				UpperCAmelCase__							(  self  :Dict	):





										"""simple docstring"""

										lowerCamelCase__     :					List[str]		=StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'	)
										sd_pipe.to(lowerCamelCase_	)
										sd_pipe.set_progress_bar_config(disable=lowerCamelCase_	)

										lowerCamelCase__     :					Optional[Any]		=self.get_inputs(lowerCamelCase_	)
										lowerCamelCase__     :					Dict		=sd_pipe(**lowerCamelCase_	).images[0]

										lowerCamelCase__     :					Optional[int]		=load_numpy(
										    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
										    '/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy'	)
										lowerCamelCase__     :					Dict		=np.abs(expected_image - image	).max()
										assert max_diff < 1e-3





							def 				UpperCAmelCase__							(  self  :Tuple	):





										"""simple docstring"""

										lowerCamelCase__     :					List[Any]		=StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'	)
										lowerCamelCase__     :					Optional[Any]		=DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config	)
										sd_pipe.to(lowerCamelCase_	)
										sd_pipe.set_progress_bar_config(disable=lowerCamelCase_	)

										lowerCamelCase__     :					Tuple		=self.get_inputs(lowerCamelCase_	)
										lowerCamelCase__     :					int		=25
										lowerCamelCase__     :					Optional[int]		=sd_pipe(**lowerCamelCase_	).images[0]

										lowerCamelCase__     :					str		=load_numpy(
										    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
										    '/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy'	)
										lowerCamelCase__     :					Dict		=np.abs(expected_image - image	).max()
										assert max_diff < 1e-3