'''simple docstring'''







import gc
import random
import unittest

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

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE						,						unittest.TestCase					):
						A_			:							Optional[int]     		= UnCLIPImageVariationPipeline
						A_			:							str     		= IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
						A_			:							str     		= IMAGE_VARIATION_BATCH_PARAMS

						A_			:							str     		= [
						    'generator',
						    'return_dict',
						    'decoder_num_inference_steps',
						    'super_res_num_inference_steps',
						]
						A_			:							str     		= False




						@property
						def      __lowerCamelCase       (    self :       Tuple      )     ->				int:
													return 32




						@property
						def      __lowerCamelCase       (    self :       Dict      )     ->				str:
													return 32




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




						@property
						def      __lowerCamelCase       (    self :       Dict      )     ->				Union[str, Any]:
													return self.time_input_dim * 4




						@property
						def      __lowerCamelCase       (    self :       Dict      )     ->				List[Any]:
													return 1_00




						@property
						def      __lowerCamelCase       (    self :       str      )     ->				Dict:
													SCREAMING_SNAKE_CASE__     :Dict						    = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )
													return tokenizer




						@property
						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				List[Any]:
													torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :Any						    = CLIPTextConfig(
													    bos_token_id=0							,							eos_token_id=2							,							hidden_size=self.text_embedder_hidden_size							,							projection_dim=self.text_embedder_hidden_size							,							intermediate_size=37							,							layer_norm_eps=1e-05							,							num_attention_heads=4							,							num_hidden_layers=5							,							pad_token_id=1							,							vocab_size=10_00							,							)
													return CLIPTextModelWithProjection(UpperCamelCase_      )




						@property
						def      __lowerCamelCase       (    self :       Dict      )     ->				List[Any]:
													torch.manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :List[str]						    = CLIPVisionConfig(
													    hidden_size=self.text_embedder_hidden_size							,							projection_dim=self.text_embedder_hidden_size							,							num_hidden_layers=5							,							num_attention_heads=4							,							image_size=32							,							intermediate_size=37							,							patch_size=1							,							)
													return CLIPVisionModelWithProjection(UpperCamelCase_      )




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

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = {
													    'clip_embeddings_dim': self.text_embedder_hidden_size,
													    'time_embed_dim': self.time_embed_dim,
													    'cross_attention_dim': self.cross_attention_dim,
													}

													SCREAMING_SNAKE_CASE__     :List[str]						    = UnCLIPTextProjModel(**UpperCamelCase_      )
													return model




						@property
						def      __lowerCamelCase       (    self :       List[str]      )     ->				Any:
													torch.manual_seed(0      )

													SCREAMING_SNAKE_CASE__     :int						    = {
													    'sample_size': 32,
													    # RGB in channels
													    'in_channels': 3,
													    # Out channels is double in channels because predicts mean and variance
													    'out_channels': 6,
													    '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,
													    'cross_attention_dim': self.cross_attention_dim,
													    'attention_head_dim': 4,
													    'resnet_time_scale_shift': 'scale_shift',
													    'class_embed_type': 'identity',
													}

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = UNetaDConditionModel(**UpperCamelCase_      )
													return model




						@property
						def      __lowerCamelCase       (    self :       str      )     ->				Tuple:
													return {
													    "sample_size": 64,
													    "layers_per_block": 1,
													    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
													    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
													    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
													    "in_channels": 6,
													    "out_channels": 3,
													}




						@property
						def      __lowerCamelCase       (    self :       List[Any]      )     ->				List[Any]:
													torch.manual_seed(0      )

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = UNetaDModel(**self.dummy_super_res_kwargs      )
													return model




						@property
						def      __lowerCamelCase       (    self :       Optional[int]      )     ->				Tuple:
													# seeded differently to get different unet than `self.dummy_super_res_first`
													torch.manual_seed(1      )

													SCREAMING_SNAKE_CASE__     :Any						    = UNetaDModel(**self.dummy_super_res_kwargs      )
													return model




						def      __lowerCamelCase       (    self :       Dict      )     ->				Tuple:
													SCREAMING_SNAKE_CASE__     :Dict						    = self.dummy_decoder
													SCREAMING_SNAKE_CASE__     :List[Any]						    = self.dummy_text_proj
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.dummy_text_encoder
													SCREAMING_SNAKE_CASE__     :int						    = self.dummy_tokenizer
													SCREAMING_SNAKE_CASE__     :Tuple						    = self.dummy_super_res_first
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.dummy_super_res_last

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = UnCLIPScheduler(
													    variance_type='learned_range'							,							prediction_type='epsilon'							,							num_train_timesteps=10_00							,							)

													SCREAMING_SNAKE_CASE__     :str						    = UnCLIPScheduler(
													    variance_type='fixed_small_log'							,							prediction_type='epsilon'							,							num_train_timesteps=10_00							,							)

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = CLIPImageProcessor(crop_size=32							,							size=32      )

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.dummy_image_encoder

													return {
													    "decoder": decoder,
													    "text_encoder": text_encoder,
													    "tokenizer": tokenizer,
													    "text_proj": text_proj,
													    "feature_extractor": feature_extractor,
													    "image_encoder": image_encoder,
													    "super_res_first": super_res_first,
													    "super_res_last": super_res_last,
													    "decoder_scheduler": decoder_scheduler,
													    "super_res_scheduler": super_res_scheduler,
													}




						def      __lowerCamelCase       (    self :       str							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       Any=0							,							UpperCamelCase_ :       str=True      )     ->				List[str]:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = floats_tensor((1, 3, 32, 32)							,							rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
													if str(UpperCamelCase_      ).startswith('mps'      ):
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = torch.manual_seed(UpperCamelCase_      )
													else:
																				SCREAMING_SNAKE_CASE__     :int						    = torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )

													if pil_image:
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = input_image * 0.5 + 0.5
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = input_image.clamp(0							,							1      )
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = input_image.cpu().permute(0							,							2							,							3							,							1      ).float().numpy()
																				SCREAMING_SNAKE_CASE__     :List[str]						    = DiffusionPipeline.numpy_to_pil(UpperCamelCase_      )[0]

													return {
													    "image": input_image,
													    "generator": generator,
													    "decoder_num_inference_steps": 2,
													    "super_res_num_inference_steps": 2,
													    "output_type": "np",
													}




						def      __lowerCamelCase       (    self :       Dict      )     ->				int:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = 'cpu'

													SCREAMING_SNAKE_CASE__     :int						    = self.get_dummy_components()

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.pipeline_class(**UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = pipe.to(UpperCamelCase_      )

													pipe.set_progress_bar_config(disable=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Tuple						    = self.get_dummy_inputs(UpperCamelCase_							,							pil_image=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :List[Any]						    = pipe(**UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Any						    = output.images

													SCREAMING_SNAKE_CASE__     :List[Any]						    = self.get_dummy_inputs(UpperCamelCase_							,							pil_image=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Any						    = pipe(
													    **UpperCamelCase_							,							return_dict=UpperCamelCase_							,							)[0]

													SCREAMING_SNAKE_CASE__     :Tuple						    = image[0, -3:, -3:, -1]
													SCREAMING_SNAKE_CASE__     :List[str]						    = image_from_tuple[0, -3:, -3:, -1]

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

													SCREAMING_SNAKE_CASE__     :Tuple						    = np.array(
													    [
													        0.9997,
													        0.0002,
													        0.9997,
													        0.9997,
													        0.9969,
													        0.0023,
													        0.9997,
													        0.9969,
													        0.9970,
													    ]      )

													assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
													assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2




						def      __lowerCamelCase       (    self :       Any      )     ->				List[str]:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 'cpu'

													SCREAMING_SNAKE_CASE__     :List[Any]						    = self.get_dummy_components()

													SCREAMING_SNAKE_CASE__     :Any						    = self.pipeline_class(**UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Dict						    = pipe.to(UpperCamelCase_      )

													pipe.set_progress_bar_config(disable=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Dict						    = self.get_dummy_inputs(UpperCamelCase_							,							pil_image=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = pipe(**UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = output.images

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.get_dummy_inputs(UpperCamelCase_							,							pil_image=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Any						    = pipe(
													    **UpperCamelCase_							,							return_dict=UpperCamelCase_							,							)[0]

													SCREAMING_SNAKE_CASE__     :str						    = image[0, -3:, -3:, -1]
													SCREAMING_SNAKE_CASE__     :List[Any]						    = image_from_tuple[0, -3:, -3:, -1]

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

													SCREAMING_SNAKE_CASE__     :List[str]						    = np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]      )

													assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
													assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Tuple:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 'cpu'

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.get_dummy_components()

													SCREAMING_SNAKE_CASE__     :Tuple						    = self.pipeline_class(**UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :List[str]						    = pipe.to(UpperCamelCase_      )

													pipe.set_progress_bar_config(disable=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :int						    = self.get_dummy_inputs(UpperCamelCase_							,							pil_image=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = [
													    pipeline_inputs['image'],
													    pipeline_inputs['image'],
													]

													SCREAMING_SNAKE_CASE__     :str						    = pipe(**UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Tuple						    = output.images

													SCREAMING_SNAKE_CASE__     :List[Any]						    = self.get_dummy_inputs(UpperCamelCase_							,							pil_image=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :int						    = [
													    tuple_pipeline_inputs['image'],
													    tuple_pipeline_inputs['image'],
													]

													SCREAMING_SNAKE_CASE__     :Any						    = pipe(
													    **UpperCamelCase_							,							return_dict=UpperCamelCase_							,							)[0]

													SCREAMING_SNAKE_CASE__     :Tuple						    = image[0, -3:, -3:, -1]
													SCREAMING_SNAKE_CASE__     :Tuple						    = image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (2, 64, 64, 3)

													SCREAMING_SNAKE_CASE__     :int						    = np.array(
													    [
													        0.9997,
													        0.9989,
													        0.0008,
													        0.0021,
													        0.9960,
													        0.0018,
													        0.0014,
													        0.0002,
													        0.9933,
													    ]      )

													assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
													assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2




						def      __lowerCamelCase       (    self :       Tuple      )     ->				List[str]:
													SCREAMING_SNAKE_CASE__     :Dict						    = torch.device('cpu'      )




													class _SCREAMING_SNAKE_CASE:
																			A_			:							List[str]     		= 1
													SCREAMING_SNAKE_CASE__     :int						    = self.get_dummy_components()

													SCREAMING_SNAKE_CASE__     :Tuple						    = self.pipeline_class(**UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Any						    = pipe.to(UpperCamelCase_      )

													pipe.set_progress_bar_config(disable=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Dict						    = torch.Generator(device=UpperCamelCase_      ).manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = pipe.decoder.dtype
													SCREAMING_SNAKE_CASE__     :Any						    = 1

													SCREAMING_SNAKE_CASE__     :int						    = (
													    batch_size,
													    pipe.decoder.config.in_channels,
													    pipe.decoder.config.sample_size,
													    pipe.decoder.config.sample_size,
													)
													SCREAMING_SNAKE_CASE__     :Any						    = pipe.prepare_latents(
													    UpperCamelCase_							,							dtype=UpperCamelCase_							,							device=UpperCamelCase_							,							generator=UpperCamelCase_							,							latents=UpperCamelCase_							,							scheduler=DummyScheduler()      )

													SCREAMING_SNAKE_CASE__     :str						    = (
													    batch_size,
													    pipe.super_res_first.config.in_channels // 2,
													    pipe.super_res_first.config.sample_size,
													    pipe.super_res_first.config.sample_size,
													)
													SCREAMING_SNAKE_CASE__     :List[str]						    = pipe.prepare_latents(
													    UpperCamelCase_							,							dtype=UpperCamelCase_							,							device=UpperCamelCase_							,							generator=UpperCamelCase_							,							latents=UpperCamelCase_							,							scheduler=DummyScheduler()      )

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = self.get_dummy_inputs(UpperCamelCase_							,							pil_image=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Any						    = pipe(
													    **UpperCamelCase_							,							decoder_latents=UpperCamelCase_							,							super_res_latents=UpperCamelCase_      ).images

													SCREAMING_SNAKE_CASE__     :Dict						    = self.get_dummy_inputs(UpperCamelCase_							,							pil_image=UpperCamelCase_      )
													# Don't pass image, instead pass embedding
													SCREAMING_SNAKE_CASE__     :Any						    = pipeline_inputs.pop('image'      )
													SCREAMING_SNAKE_CASE__     :List[str]						    = pipe.image_encoder(UpperCamelCase_      ).image_embeds

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = pipe(
													    **UpperCamelCase_							,							decoder_latents=UpperCamelCase_							,							super_res_latents=UpperCamelCase_							,							image_embeddings=UpperCamelCase_							,							).images

													# make sure passing text embeddings manually is identical
													assert np.abs(img_out_a - img_out_a      ).max() < 1e-4




						@skip_mps
						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				Tuple:
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = torch_device == 'cpu'

													# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 1e-2

													self._test_attention_slicing_forward_pass(
													    test_max_difference=UpperCamelCase_							,							expected_max_diff=UpperCamelCase_      )




						@skip_mps
						def      __lowerCamelCase       (    self :       int      )     ->				List[str]:
													SCREAMING_SNAKE_CASE__     :Dict						    = torch_device == 'cpu'
													SCREAMING_SNAKE_CASE__     :str						    = True
													SCREAMING_SNAKE_CASE__     :List[str]						    = [
													    'decoder_num_inference_steps',
													    'super_res_num_inference_steps',
													]

													self._test_inference_batch_single_identical(
													    test_max_difference=UpperCamelCase_							,							relax_max_difference=UpperCamelCase_							,							additional_params_copy_to_batched_inputs=UpperCamelCase_							,							)




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				str:
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = [
													    'decoder_num_inference_steps',
													    'super_res_num_inference_steps',
													]

													if torch_device == "mps":
																				# TODO: MPS errors with larger batch sizes
																				SCREAMING_SNAKE_CASE__     :Any						    = [2, 3]
																				self._test_inference_batch_consistent(
																				    batch_sizes=UpperCamelCase_							,							additional_params_copy_to_batched_inputs=UpperCamelCase_							,							)
													else:
																				self._test_inference_batch_consistent(
																				    additional_params_copy_to_batched_inputs=UpperCamelCase_      )




						@skip_mps
						def      __lowerCamelCase       (    self :       Dict      )     ->				List[Any]:
													return super().test_dict_tuple_outputs_equivalent()




						@skip_mps
						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				Optional[int]:
													return super().test_save_load_local()




						@skip_mps
						def      __lowerCamelCase       (    self :       Tuple      )     ->				Union[str, Any]:
													return super().test_save_load_optional_components()




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




						def      __lowerCamelCase       (    self :       Any      )     ->				Dict:
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				List[Any]:
													SCREAMING_SNAKE_CASE__     :Any						    = load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/unclip/karlo_v1_alpha_cat_variation_fp16.npy'      )

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = UnCLIPImageVariationPipeline.from_pretrained(
													    'kakaobrain/karlo-v1-alpha-image-variations'							,							torch_dtype=torch.floataa      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = pipeline.to(UpperCamelCase_      )
													pipeline.set_progress_bar_config(disable=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Dict						    = torch.Generator(device='cpu'      ).manual_seed(0      )
													SCREAMING_SNAKE_CASE__     :List[str]						    = pipeline(
													    UpperCamelCase_							,							generator=UpperCamelCase_							,							output_type='np'							,							)

													SCREAMING_SNAKE_CASE__     :Any						    = output.images[0]

													assert image.shape == (2_56, 2_56, 3)

													assert_mean_pixel_difference(UpperCamelCase_							,							UpperCamelCase_							,							15      )







'''simple docstring'''







from __future__ import annotations

import math
import random
from typing import Any




class _SCREAMING_SNAKE_CASE:




						def __init__(    self :       str      )     ->				None:
													SCREAMING_SNAKE_CASE__     :list[Any]						    = []
													SCREAMING_SNAKE_CASE__     :int						    = 0
													SCREAMING_SNAKE_CASE__     :int						    = 0




						def      __lowerCamelCase       (    self :       Any      )     ->				bool:
													return self.head == self.tail




						def      __lowerCamelCase       (    self :       Any							,							UpperCamelCase_ :       Any      )     ->				None:
													self.data.append(UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :int						    = self.tail + 1




						def      __lowerCamelCase       (    self :       Tuple      )     ->				Any:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.data[self.head]
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.head + 1
													return ret




						def      __lowerCamelCase       (    self :       str      )     ->				int:
													return self.tail - self.head




						def      __lowerCamelCase       (    self :       Optional[int]      )     ->				None:
													print(self.data      )
													print('**************'      )
													print(self.data[self.head : self.tail]      )




class _SCREAMING_SNAKE_CASE:




						def __init__(    self :       List[str]							,							UpperCamelCase_ :       Any      )     ->				None:
													SCREAMING_SNAKE_CASE__     :Tuple						    = data
													SCREAMING_SNAKE_CASE__     :MyNode | None						    = None
													SCREAMING_SNAKE_CASE__     :MyNode | None						    = None
													SCREAMING_SNAKE_CASE__     :int						    = 1




						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				Any:
													return self.data




						def      __lowerCamelCase       (    self :       Optional[int]      )     ->				MyNode | None:
													return self.left




						def      __lowerCamelCase       (    self :       List[str]      )     ->				MyNode | None:
													return self.right




						def      __lowerCamelCase       (    self :       List[Any]      )     ->				int:
													return self.height




						def      __lowerCamelCase       (    self :       Optional[Any]							,							UpperCamelCase_ :       Any      )     ->				None:
													SCREAMING_SNAKE_CASE__     :List[str]						    = data




						def      __lowerCamelCase       (    self :       Dict							,							UpperCamelCase_ :       MyNode | None      )     ->				None:
													SCREAMING_SNAKE_CASE__     :Dict						    = node




						def      __lowerCamelCase       (    self :       Optional[int]							,							UpperCamelCase_ :       MyNode | None      )     ->				None:
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = node




						def      __lowerCamelCase       (    self :       Union[str, Any]							,							UpperCamelCase_ :       int      )     ->				None:
													SCREAMING_SNAKE_CASE__     :Dict						    = height


def    lowerCamelCase       (			UpperCAmelCase__				:  MyNode | None		)			->	int:


							'''simple docstring'''






							if node is None:
														return 0
							return node.get_height()


def    lowerCamelCase       (			UpperCAmelCase__				:  int				,						UpperCAmelCase__				:  int		)			->	int:


							'''simple docstring'''






							if a > b:
														return a
							return b


def    lowerCamelCase       (			UpperCAmelCase__				:  MyNode		)			->	MyNode:


							'''simple docstring'''






							print('left rotation node:'				,						node.get_data()		)
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = node.get_left()
							assert ret is not None
							node.set_left(ret.get_right()		)
							ret.set_right(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Any						    = my_max(get_height(node.get_right()		)				,						get_height(node.get_left()		)		) + 1
							node.set_height(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Dict						    = my_max(get_height(ret.get_right()		)				,						get_height(ret.get_left()		)		) + 1
							ret.set_height(UpperCAmelCase__		)
							return ret


def    lowerCamelCase       (			UpperCAmelCase__				:  MyNode		)			->	MyNode:


							'''simple docstring'''






							print('right rotation node:'				,						node.get_data()		)
							SCREAMING_SNAKE_CASE__     :Tuple						    = node.get_right()
							assert ret is not None
							node.set_right(ret.get_left()		)
							ret.set_left(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = my_max(get_height(node.get_right()		)				,						get_height(node.get_left()		)		) + 1
							node.set_height(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :int						    = my_max(get_height(ret.get_right()		)				,						get_height(ret.get_left()		)		) + 1
							ret.set_height(UpperCAmelCase__		)
							return ret


def    lowerCamelCase       (			UpperCAmelCase__				:  MyNode		)			->	MyNode:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :str						    = node.get_left()
							assert left_child is not None
							node.set_left(left_rotation(UpperCAmelCase__		)		)
							return right_rotation(UpperCAmelCase__		)


def    lowerCamelCase       (			UpperCAmelCase__				:  MyNode		)			->	MyNode:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = node.get_right()
							assert right_child is not None
							node.set_right(right_rotation(UpperCAmelCase__		)		)
							return left_rotation(UpperCAmelCase__		)


def    lowerCamelCase       (			UpperCAmelCase__				:  MyNode | None				,						UpperCAmelCase__				:  Any		)			->	MyNode | None:


							'''simple docstring'''






							if node is None:
														return MyNode(UpperCAmelCase__		)
							if data < node.get_data():
														node.set_left(insert_node(node.get_left()				,						UpperCAmelCase__		)		)
														if (
														    get_height(node.get_left()		) - get_height(node.get_right()		) == 2
														):  # an unbalance detected
																					SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = node.get_left()
																					assert left_child is not None
																					if (
																					    data < left_child.get_data()
																					):  # new node is the left child of the left child
																												SCREAMING_SNAKE_CASE__     :Dict						    = right_rotation(UpperCAmelCase__		)
																					else:
																												SCREAMING_SNAKE_CASE__     :str						    = lr_rotation(UpperCAmelCase__		)
							else:
														node.set_right(insert_node(node.get_right()				,						UpperCAmelCase__		)		)
														if get_height(node.get_right()		) - get_height(node.get_left()		) == 2:
																					SCREAMING_SNAKE_CASE__     :Optional[int]						    = node.get_right()
																					assert right_child is not None
																					if data < right_child.get_data():
																												SCREAMING_SNAKE_CASE__     :Optional[Any]						    = rl_rotation(UpperCAmelCase__		)
																					else:
																												SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = left_rotation(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Tuple						    = my_max(get_height(node.get_right()		)				,						get_height(node.get_left()		)		) + 1
							node.set_height(UpperCAmelCase__		)
							return node


def    lowerCamelCase       (			UpperCAmelCase__				:  MyNode		)			->	Any:


							'''simple docstring'''






							while True:
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = root.get_right()
														if right_child is None:
																					break
														SCREAMING_SNAKE_CASE__     :str						    = right_child
							return root.get_data()


def    lowerCamelCase       (			UpperCAmelCase__				:  MyNode		)			->	Any:


							'''simple docstring'''






							while True:
														SCREAMING_SNAKE_CASE__     :Optional[int]						    = root.get_left()
														if left_child is None:
																					break
														SCREAMING_SNAKE_CASE__     :List[Any]						    = left_child
							return root.get_data()


def    lowerCamelCase       (			UpperCAmelCase__				:  MyNode				,						UpperCAmelCase__				:  Any		)			->	MyNode | None:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :List[Any]						    = root.get_left()
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = root.get_right()
							if root.get_data() == data:
														if left_child is not None and right_child is not None:
																					SCREAMING_SNAKE_CASE__     :int						    = get_left_most(UpperCAmelCase__		)
																					root.set_data(UpperCAmelCase__		)
																					root.set_right(del_node(UpperCAmelCase__				,						UpperCAmelCase__		)		)
														elif left_child is not None:
																					SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = left_child
														elif right_child is not None:
																					SCREAMING_SNAKE_CASE__     :Optional[int]						    = right_child
														else:
																					return None
							elif root.get_data() > data:
														if left_child is None:
																					print('No such data'		)
																					return root
														else:
																					root.set_left(del_node(UpperCAmelCase__				,						UpperCAmelCase__		)		)
							else:  # root.get_data() < data
														if right_child is None:
																					return root
														else:
																					root.set_right(del_node(UpperCAmelCase__				,						UpperCAmelCase__		)		)

							if get_height(UpperCAmelCase__		) - get_height(UpperCAmelCase__		) == 2:
														assert right_child is not None
														if get_height(right_child.get_right()		) > get_height(right_child.get_left()		):
																					SCREAMING_SNAKE_CASE__     :Any						    = left_rotation(UpperCAmelCase__		)
														else:
																					SCREAMING_SNAKE_CASE__     :int						    = rl_rotation(UpperCAmelCase__		)
							elif get_height(UpperCAmelCase__		) - get_height(UpperCAmelCase__		) == -2:
														assert left_child is not None
														if get_height(left_child.get_left()		) > get_height(left_child.get_right()		):
																					SCREAMING_SNAKE_CASE__     :Any						    = right_rotation(UpperCAmelCase__		)
														else:
																					SCREAMING_SNAKE_CASE__     :str						    = lr_rotation(UpperCAmelCase__		)
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = my_max(get_height(root.get_right()		)				,						get_height(root.get_left()		)		) + 1
							root.set_height(UpperCAmelCase__		)
							return root




class _SCREAMING_SNAKE_CASE:




						def __init__(    self :       List[Any]      )     ->				None:
													SCREAMING_SNAKE_CASE__     :MyNode | None						    = None




						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				int:
													return get_height(self.root      )




						def      __lowerCamelCase       (    self :       int							,							UpperCamelCase_ :       Any      )     ->				None:
													print('insert:' + str(UpperCamelCase_      )      )
													SCREAMING_SNAKE_CASE__     :Dict						    = insert_node(self.root							,							UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       str							,							UpperCamelCase_ :       Any      )     ->				None:
													print('delete:' + str(UpperCamelCase_      )      )
													if self.root is None:
																				print('Tree is empty!'      )
																				return
													SCREAMING_SNAKE_CASE__     :List[Any]						    = del_node(self.root							,							UpperCamelCase_      )




						def __str__(    self :       List[Any]							,							)     ->				str:  # a level traversale, gives a more intuitive look on the tree
													SCREAMING_SNAKE_CASE__     :List[str]						    = ''
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = MyQueue()
													q.push(self.root      )
													SCREAMING_SNAKE_CASE__     :int						    = self.get_height()
													if layer == 0:
																				return output
													SCREAMING_SNAKE_CASE__     :str						    = 0
													while not q.is_empty():
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = q.pop()
																				SCREAMING_SNAKE_CASE__     :List[str]						    = ' ' * int(math.pow(2							,							layer - 1      )      )
																				output += space
																				if node is None:
																											output += "*"
																											q.push(UpperCamelCase_      )
																											q.push(UpperCamelCase_      )
																				else:
																											output += str(node.get_data()      )
																											q.push(node.get_left()      )
																											q.push(node.get_right()      )
																				output += space
																				SCREAMING_SNAKE_CASE__     :Tuple						    = cnt + 1
																				for i in range(1_00      ):
																											if cnt == math.pow(2							,							UpperCamelCase_      ) - 1:
																																		SCREAMING_SNAKE_CASE__     :Optional[int]						    = layer - 1
																																		if layer == 0:
																																									output += "\n*************************************"
																																									return output
																																		output += "\n"
																																		break
													output += "\n*************************************"
													return output


def    lowerCamelCase       (			)			->	None:


							'''simple docstring'''






							import doctest

							doctest.testmod()


if __name__ == "__main__":
		_test()
		UpperCamelCase_      	=	AVLtree()
		UpperCamelCase_      	=	list(range(10))
		random.shuffle(lst)
		for i in lst:
				t.insert(i)
				print(str(t))
		random.shuffle(lst)
		for i in lst:
				t.del_node(i)
				print(str(t))






