






import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()
class    _UpperCAmelCase      ( A__	,A__	,A__	,unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= StableUnCLIPImgaImgPipeline
				lowercase__       							= TEXT_GUIDED_IMAGE_VARIATION_PARAMS
				lowercase__       							= TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				lowercase__       							= frozenset(
				    []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				lowercase__       							= frozenset([]			)




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    32
								lowercase__     				=    embedder_hidden_size

								# image encoding components

								lowercase__     				=    CLIPImageProcessor(crop_size=32,						size=32						)

								torch.manual_seed(0						)
								lowercase__     				=    CLIPVisionModelWithProjection(
								    CLIPVisionConfig(
								        hidden_size=lowerCamelCase,						projection_dim=lowerCamelCase,						num_hidden_layers=5,						num_attention_heads=4,						image_size=32,						intermediate_size=37,						patch_size=1,						)						)

								# regular denoising components

								torch.manual_seed(0						)
								lowercase__     				=    StableUnCLIPImageNormalizer(embedding_dim=lowerCamelCase						)
								lowercase__     				=    DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''						)

								torch.manual_seed(0						)
								lowercase__     				=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

								torch.manual_seed(0						)
								lowercase__     				=    CLIPTextModel(
								    CLIPTextConfig(
								        bos_token_id=0,						eos_token_id=2,						hidden_size=lowerCamelCase,						projection_dim=32,						intermediate_size=37,						layer_norm_eps=1E-05,						num_attention_heads=4,						num_hidden_layers=5,						pad_token_id=1,						vocab_size=1_000,						)						)

								torch.manual_seed(0						)
								lowercase__     				=    UNetaDConditionModel(
								    sample_size=32,						in_channels=4,						out_channels=4,						down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D'''),						up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D'''),						block_out_channels=(32, 64),						attention_head_dim=(2, 4),						class_embed_type='''projection''',						projection_class_embeddings_input_dim=embedder_projection_dim * 2,						cross_attention_dim=lowerCamelCase,						layers_per_block=1,						upcast_attention=lowerCamelCase,						use_linear_projection=lowerCamelCase,						)

								torch.manual_seed(0						)
								lowercase__     				=    DDIMScheduler(
								    beta_schedule='''scaled_linear''',						beta_start=0.00085,						beta_end=0.012,						prediction_type='''v_prediction''',						set_alpha_to_one=lowerCamelCase,						steps_offset=1,						)

								torch.manual_seed(0						)
								lowercase__     				=    AutoencoderKL()

								lowercase__     				=    {
								    # image encoding components
								    '''feature_extractor''': feature_extractor,
								    '''image_encoder''': image_encoder.eval(),
								    # image noising components
								    '''image_normalizer''': image_normalizer.eval(),
								    '''image_noising_scheduler''': image_noising_scheduler,
								    # regular denoising components
								    '''tokenizer''': tokenizer,
								    '''text_encoder''': text_encoder.eval(),
								    '''unet''': unet.eval(),
								    '''scheduler''': scheduler,
								    '''vae''': vae.eval(),
								}

								return components




				def        lowercase__     (  self		:					str,						lowerCamelCase		:					List[str],						lowerCamelCase		:					Any=0,						lowerCamelCase		:					Optional[int]=True						):

								'''simple docstring'''
								if str(lowerCamelCase						).startswith('''mps'''						):
												lowercase__     				=    torch.manual_seed(lowerCamelCase						)
								else:
												lowercase__     				=    torch.Generator(device=lowerCamelCase						).manual_seed(lowerCamelCase						)

								lowercase__     				=    floats_tensor((1, 3, 32, 32),						rng=random.Random(lowerCamelCase						)						).to(lowerCamelCase						)

								if pil_image:
												lowercase__     				=    input_image * 0.5 + 0.5
												lowercase__     				=    input_image.clamp(0,						1						)
												lowercase__     				=    input_image.cpu().permute(0,						2,						3,						1						).float().numpy()
												lowercase__     				=    DiffusionPipeline.numpy_to_pil(lowerCamelCase						)[0]

								return {
								    "prompt": "An anime racoon running a marathon",
								    "image": input_image,
								    "generator": generator,
								    "num_inference_steps": 2,
								    "output_type": "np",
								}




				@skip_mps
				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
								lowercase__     				=    self.get_dummy_components()
								lowercase__     				=    StableUnCLIPImgaImgPipeline(**lowerCamelCase						)
								lowercase__     				=    sd_pipe.to(lowerCamelCase						)
								sd_pipe.set_progress_bar_config(disable=lowerCamelCase						)

								lowercase__     				=    self.get_dummy_inputs(lowerCamelCase						)
								inputs.update({'''image_embeds''': None}						)
								lowercase__     				=    sd_pipe(**lowerCamelCase						).images
								lowercase__     				=    image[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								lowercase__     				=    np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078]						)

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




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    torch_device in ['''cpu''', '''mps''']

								self._test_attention_slicing_forward_pass(test_max_difference=lowerCamelCase						)




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__     				=    torch_device in ['''cpu''', '''mps''']

								self._test_inference_batch_single_identical(test_max_difference=lowerCamelCase						)




				@unittest.skipIf(
				    torch_device != '''cuda''' or not is_xformers_available(),						reason='''XFormers attention is only available with CUDA and `xformers` installed''',						)
				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								self._test_xformers_attention_forwardGenerator_pass(test_max_difference=lowerCamelCase						)



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

				"""simple docstring"""




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'''						)

								lowercase__     				=    load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy'''						)

								lowercase__     				=    StableUnCLIPImgaImgPipeline.from_pretrained(
								    '''fusing/stable-unclip-2-1-l-img2img''',						torch_dtype=torch.floataa						)
								pipe.to(lowerCamelCase						)
								pipe.set_progress_bar_config(disable=lowerCamelCase						)
								# stable unclip will oom when integration tests are run on a V100,
								# so turn on memory savings
								pipe.enable_attention_slicing()
								pipe.enable_sequential_cpu_offload()

								lowercase__     				=    torch.Generator(device='''cpu'''						).manual_seed(0						)
								lowercase__     				=    pipe(lowerCamelCase,						'''anime turle''',						generator=lowerCamelCase,						output_type='''np'''						)

								lowercase__     				=    output.images[0]

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

								assert_mean_pixel_difference(lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'''						)

								lowercase__     				=    load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy'''						)

								lowercase__     				=    StableUnCLIPImgaImgPipeline.from_pretrained(
								    '''fusing/stable-unclip-2-1-h-img2img''',						torch_dtype=torch.floataa						)
								pipe.to(lowerCamelCase						)
								pipe.set_progress_bar_config(disable=lowerCamelCase						)
								# stable unclip will oom when integration tests are run on a V100,
								# so turn on memory savings
								pipe.enable_attention_slicing()
								pipe.enable_sequential_cpu_offload()

								lowercase__     				=    torch.Generator(device='''cpu'''						).manual_seed(0						)
								lowercase__     				=    pipe(lowerCamelCase,						'''anime turle''',						generator=lowerCamelCase,						output_type='''np'''						)

								lowercase__     				=    output.images[0]

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

								assert_mean_pixel_difference(lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__     				=    load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'''						)

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

								lowercase__     				=    StableUnCLIPImgaImgPipeline.from_pretrained(
								    '''fusing/stable-unclip-2-1-h-img2img''',						torch_dtype=torch.floataa						)
								lowercase__     				=    pipe.to(lowerCamelCase						)
								pipe.set_progress_bar_config(disable=lowerCamelCase						)
								pipe.enable_attention_slicing()
								pipe.enable_sequential_cpu_offload()

								lowercase__     				=    pipe(
								    lowerCamelCase,						'''anime turtle''',						num_inference_steps=2,						output_type='''np''',						)

								lowercase__     				=    torch.cuda.max_memory_allocated()
								# make sure that less than 7 GB is allocated
								assert mem_bytes < 7 * 10**9












from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class    _UpperCAmelCase      :

				"""simple docstring"""




				lowercase__       							= 42
				lowercase__       							= None
				lowercase__       							= None






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    Node(1       )
				lowercase__     				=    Node(2       )
				lowercase__     				=    Node(3       )
				lowercase__     				=    Node(4       )
				lowercase__     				=    Node(5       )
				return tree






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				return [root.data, *preorder(root.left       ), *preorder(root.right       )] if root else []






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				return postorder(root.left       ) + postorder(root.right       ) + [root.data] if root else []






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				return [*inorder(root.left       ), root.data, *inorder(root.right       )] if root else []






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				return (max(height(root.left       )	,     height(root.right       )       ) + 1) if root else 0






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    []

				if root is None:
								return output

				lowercase__     				=    deque([root]       )

				while process_queue:
								lowercase__     				=    process_queue.popleft()
								output.append(node.data       )

								if node.left:
												process_queue.append(node.left       )
								if node.right:
												process_queue.append(node.right       )
				return output






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    []

				def populate_output(lowerCamelCase_	,     lowerCamelCase_       ) -> None:
								if not root:
												return
								if level == 1:
												output.append(root.data       )
								elif level > 1:
												populate_output(root.left	,     level - 1       )
												populate_output(root.right	,     level - 1       )

				populate_output(lowerCamelCase_	,     lowerCamelCase_       )
				return output






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    []

				def populate_output(lowerCamelCase_	,     lowerCamelCase_       ) -> None:
								if root is None:
												return
								if level == 1:
												output.append(root.data       )
								elif level > 1:
												populate_output(root.right	,     level - 1       )
												populate_output(root.left	,     level - 1       )

				populate_output(lowerCamelCase_	,     lowerCamelCase_       )
				return output






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				if root is None:
								return []

				lowercase__     				=    []

				lowercase__     				=    0
				lowercase__     				=    height(lowerCamelCase_       )

				for h in range(1	,     height_tree + 1       ):
								if not flag:
												output.append(get_nodes_from_left_to_right(lowerCamelCase_	,     lowerCamelCase_       )       )
												lowercase__     				=    1
								else:
												output.append(get_nodes_from_right_to_left(lowerCamelCase_	,     lowerCamelCase_       )       )
												lowercase__     				=    0

				return output






def 						a    (		):  # Main function for testing.






				'''simple docstring'''
				lowercase__     				=    make_tree()
				print(F"""In-order Traversal: {inorder(lowerCamelCase_       )}"""       )
				print(F"""Pre-order Traversal: {preorder(lowerCamelCase_       )}"""       )
				print(F"""Post-order Traversal: {postorder(lowerCamelCase_       )}"""	,     '''\n'''       )

				print(F"""Height of Tree: {height(lowerCamelCase_       )}"""	,     '''\n'''       )

				print('''Complete Level Order Traversal: '''       )
				print(level_order(lowerCamelCase_       )	,     '''\n'''       )

				print('''Level-wise order Traversal: '''       )

				for level in range(1	,     height(lowerCamelCase_       ) + 1       ):
								print(F"""Level {level}:"""	,     get_nodes_from_left_to_right(lowerCamelCase_	,     level=lowerCamelCase_       )       )

				print('''\nZigZag order Traversal: '''       )
				print(zigzag(lowerCamelCase_       )       )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()




