



'''simple docstring'''




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 			UpperCamelCase_ (							__magic_name__      ,     __magic_name__      ,     __magic_name__      ,     unittest.TestCase			):
	lowercase          = StableDiffusionInstructPixaPixPipeline
	lowercase          = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
	lowercase          = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	lowercase          = IMAGE_TO_IMAGE_IMAGE_PARAMS
	lowercase          = IMAGE_TO_IMAGE_IMAGE_PARAMS





	def  _lowercase(  self    )			->			Any:
								torch.manual_seed(0    )
								UpperCAmelCase     :		Tuple							     =		UNetaDConditionModel(
								    block_out_channels=(32, 64)		,		layers_per_block=2		,		sample_size=32		,		in_channels=8		,		out_channels=4		,		down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")		,		up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")		,		cross_attention_dim=32		,		)
								UpperCAmelCase     :		Tuple							     =		PNDMScheduler(skip_prk_steps=A    )
								torch.manual_seed(0    )
								UpperCAmelCase     :		Optional[Any]							     =		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    )
								UpperCAmelCase     :		Optional[Any]							     =		CLIPTextConfig(
								    bos_token_id=0		,		eos_token_id=2		,		hidden_size=32		,		intermediate_size=37		,		layer_norm_eps=1e-05		,		num_attention_heads=4		,		num_hidden_layers=5		,		pad_token_id=1		,		vocab_size=1000		,		)
								UpperCAmelCase     :		Tuple							     =		CLIPTextModel(A    )
								UpperCAmelCase     :		Union[str, Any]							     =		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

								UpperCAmelCase     :		Optional[Any]							     =		{
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """vae""": vae,
								    """text_encoder""": text_encoder,
								    """tokenizer""": tokenizer,
								    """safety_checker""": None,
								    """feature_extractor""": None,
								}
								return components





	def  _lowercase(  self		,		A		,		A=0    )			->			str:
								UpperCAmelCase     :		Optional[Any]							     =		floats_tensor((1, 3, 32, 32)		,		rng=random.Random(A    )    ).to(A    )
								UpperCAmelCase     :		int							     =		image.cpu().permute(0		,		2		,		3		,		1    )[0]
								UpperCAmelCase     :		Any							     =		Image.fromarray(np.uinta(A    )    ).convert("""RGB"""    )
								if str(A    ).startswith("""mps"""    ):
															UpperCAmelCase     :		str							     =		torch.manual_seed(A    )
								else:
															UpperCAmelCase     :		Optional[Any]							     =		torch.Generator(device=A    ).manual_seed(A    )
								UpperCAmelCase     :		List[Any]							     =		{
								    """prompt""": """A painting of a squirrel eating a burger""",
								    """image""": image,
								    """generator""": generator,
								    """num_inference_steps""": 2,
								    """guidance_scale""": 6.0,
								    """image_guidance_scale""": 1,
								    """output_type""": """numpy""",
								}
								return inputs





	def  _lowercase(  self    )			->			str:
								UpperCAmelCase     :		int							     =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
								UpperCAmelCase     :		str							     =		self.get_dummy_components()
								UpperCAmelCase     :		Any							     =		StableDiffusionInstructPixaPixPipeline(**A    )
								UpperCAmelCase     :		str							     =		sd_pipe.to(A    )
								sd_pipe.set_progress_bar_config(disable=A    )

								UpperCAmelCase     :		List[Any]							     =		self.get_dummy_inputs(A    )
								UpperCAmelCase     :		Tuple							     =		sd_pipe(**A    ).images
								UpperCAmelCase     :		List[str]							     =		image[0, -3:, -3:, -1]
								assert image.shape == (1, 32, 32, 3)
								UpperCAmelCase     :		str							     =		np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5]    )

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





	def  _lowercase(  self    )			->			Tuple:
								UpperCAmelCase     :		List[Any]							     =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
								UpperCAmelCase     :		List[str]							     =		self.get_dummy_components()
								UpperCAmelCase     :		str							     =		StableDiffusionInstructPixaPixPipeline(**A    )
								UpperCAmelCase     :		Any							     =		sd_pipe.to(A    )
								sd_pipe.set_progress_bar_config(disable=A    )

								UpperCAmelCase     :		Optional[Any]							     =		self.get_dummy_inputs(A    )
								UpperCAmelCase     :		Union[str, Any]							     =		"""french fries"""
								UpperCAmelCase     :		Optional[Any]							     =		sd_pipe(**A		,		negative_prompt=A    )
								UpperCAmelCase     :		int							     =		output.images
								UpperCAmelCase     :		Any							     =		image[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								UpperCAmelCase     :		str							     =		np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1]    )

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





	def  _lowercase(  self    )			->			Union[str, Any]:
								UpperCAmelCase     :		str							     =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
								UpperCAmelCase     :		Optional[Any]							     =		self.get_dummy_components()
								UpperCAmelCase     :		str							     =		StableDiffusionInstructPixaPixPipeline(**A    )
								UpperCAmelCase     :		Optional[int]							     =		sd_pipe.to(A    )
								sd_pipe.set_progress_bar_config(disable=A    )

								UpperCAmelCase     :		Optional[Any]							     =		self.get_dummy_inputs(A    )
								UpperCAmelCase     :		Any							     =		[inputs["""prompt"""]] * 2

								UpperCAmelCase     :		str							     =		np.array(inputs["""image"""]    ).astype(np.floataa    ) / 2_5_5.0
								UpperCAmelCase     :		Any							     =		torch.from_numpy(A    ).unsqueeze(0    ).to(A    )
								UpperCAmelCase     :		int							     =		image / 2 + 0.5
								UpperCAmelCase     :		int							     =		image.permute(0		,		3		,		1		,		2    )
								UpperCAmelCase     :		Tuple							     =		image.repeat(2		,		1		,		1		,		1    )

								UpperCAmelCase     :		Optional[int]							     =		sd_pipe(**A    ).images
								UpperCAmelCase     :		Tuple							     =		image[-1, -3:, -3:, -1]

								assert image.shape == (2, 32, 32, 3)
								UpperCAmelCase     :		Optional[int]							     =		np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9]    )

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





	def  _lowercase(  self    )			->			Union[str, Any]:
								UpperCAmelCase     :		Any							     =		"""cpu"""  # ensure determinism for the device-dependent torch.Generator
								UpperCAmelCase     :		Tuple							     =		self.get_dummy_components()
								UpperCAmelCase     :		Tuple							     =		EulerAncestralDiscreteScheduler(
								    beta_start=0.0_0_0_8_5		,		beta_end=0.0_1_2		,		beta_schedule="""scaled_linear"""    )
								UpperCAmelCase     :		Optional[Any]							     =		StableDiffusionInstructPixaPixPipeline(**A    )
								UpperCAmelCase     :		Dict							     =		sd_pipe.to(A    )
								sd_pipe.set_progress_bar_config(disable=A    )

								UpperCAmelCase     :		int							     =		self.get_dummy_inputs(A    )
								UpperCAmelCase     :		str							     =		sd_pipe(**A    ).images
								UpperCAmelCase     :		Any							     =		image[0, -3:, -3:, -1]

								UpperCAmelCase     :		Optional[int]							     =		[round(A		,		4    ) for x in image_slice.flatten().tolist()]
								print(""",""".join([str(A    ) for x in slice]    )    )

								assert image.shape == (1, 32, 32, 3)
								UpperCAmelCase     :		Optional[int]							     =		np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6]    )

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





	def  _lowercase(  self    )			->			Optional[Any]:
								super().test_inference_batch_single_identical(expected_max_diff=3e-3    )






	def  _lowercase(  self    )			->			Tuple:
								UpperCAmelCase     :		Union[str, Any]							     =		self.get_dummy_components()
								UpperCAmelCase     :		List[Any]							     =		StableDiffusionInstructPixaPixPipeline(**A    )
								UpperCAmelCase     :		Tuple							     =		VaeImageProcessor(do_resize=A		,		do_normalize=A    )
								UpperCAmelCase     :		List[Any]							     =		pipe.to(A    )
								pipe.set_progress_bar_config(disable=A    )

								UpperCAmelCase     :		str							     =		pipe(**self.get_dummy_inputs_by_type(A		,		input_image_type="""pt"""    )    )[0]

								UpperCAmelCase     :		Any							     =		components["""vae"""]
								UpperCAmelCase     :		Dict							     =		self.get_dummy_inputs_by_type(A		,		input_image_type="""pt"""    )

								for image_param in self.image_latents_params:
															if image_param in inputs.keys():
																						UpperCAmelCase     :		Tuple							     =		vae.encode(inputs[image_param]    ).latent_dist.mode()

								UpperCAmelCase     :		Dict							     =		pipe(**A    )[0]

								UpperCAmelCase     :		Optional[Any]							     =		np.abs(out - out_latents_inputs    ).max()
								self.assertLess(A		,		1e-4		,		"""passing latents as image input generate different result from passing image"""    )




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





	def  _lowercase(  self    )			->			str:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()





	def  _lowercase(  self		,		A=0    )			->			Union[str, Any]:
								UpperCAmelCase     :		Any							     =		torch.manual_seed(A    )
								UpperCAmelCase     :		Optional[Any]							     =		load_image(
								    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""    )
								UpperCAmelCase     :		str							     =		{
								    """prompt""": """turn him into a cyborg""",
								    """image""": image,
								    """generator""": generator,
								    """num_inference_steps""": 3,
								    """guidance_scale""": 7.5,
								    """image_guidance_scale""": 1.0,
								    """output_type""": """numpy""",
								}
								return inputs





	def  _lowercase(  self    )			->			Tuple:
								UpperCAmelCase     :		List[str]							     =		StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    """timbrooks/instruct-pix2pix"""		,		safety_checker=A    )
								pipe.to(A    )
								pipe.set_progress_bar_config(disable=A    )
								pipe.enable_attention_slicing()

								UpperCAmelCase     :		str							     =		self.get_inputs()
								UpperCAmelCase     :		List[Any]							     =		pipe(**A    ).images
								UpperCAmelCase     :		Optional[int]							     =		image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 512, 512, 3)
								UpperCAmelCase     :		Optional[int]							     =		np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5]    )

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





	def  _lowercase(  self    )			->			List[Any]:
								UpperCAmelCase     :		List[str]							     =		StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    """timbrooks/instruct-pix2pix"""		,		safety_checker=A    )
								UpperCAmelCase     :		Dict							     =		LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
								pipe.to(A    )
								pipe.set_progress_bar_config(disable=A    )
								pipe.enable_attention_slicing()

								UpperCAmelCase     :		Dict							     =		self.get_inputs()
								UpperCAmelCase     :		List[Any]							     =		pipe(**A    ).images
								UpperCAmelCase     :		Optional[Any]							     =		image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 512, 512, 3)
								UpperCAmelCase     :		Optional[int]							     =		np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1]    )

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





	def  _lowercase(  self    )			->			Union[str, Any]:
								UpperCAmelCase     :		str							     =		StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    """timbrooks/instruct-pix2pix"""		,		safety_checker=A    )
								UpperCAmelCase     :		Optional[int]							     =		DDIMScheduler.from_config(pipe.scheduler.config    )
								pipe.to(A    )
								pipe.set_progress_bar_config(disable=A    )
								pipe.enable_attention_slicing()

								UpperCAmelCase     :		Optional[int]							     =		self.get_inputs()
								UpperCAmelCase     :		Dict							     =		pipe(**A    ).images
								UpperCAmelCase     :		int							     =		image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 512, 512, 3)
								UpperCAmelCase     :		Any							     =		np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3]    )

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





	def  _lowercase(  self    )			->			List[str]:
								UpperCAmelCase     :		int							     =		0

								def callback_fn(A		,		A		,		A    ) -> None:
															UpperCAmelCase     :		str							     =		True
															nonlocal number_of_steps
															number_of_steps += 1
															if step == 1:
																						UpperCAmelCase     :		Optional[Any]							     =		latents.detach().cpu().numpy()
																						assert latents.shape == (1, 4, 64, 64)
																						UpperCAmelCase     :		Union[str, Any]							     =		latents[0, -3:, -3:, -1]
																						UpperCAmelCase     :		Tuple							     =		np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3]    )

																						assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 5e-2
															elif step == 2:
																						UpperCAmelCase     :		List[Any]							     =		latents.detach().cpu().numpy()
																						assert latents.shape == (1, 4, 64, 64)
																						UpperCAmelCase     :		str							     =		latents[0, -3:, -3:, -1]
																						UpperCAmelCase     :		List[Any]							     =		np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5]    )

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

								UpperCAmelCase     :		Tuple							     =		False

								UpperCAmelCase     :		Optional[int]							     =		StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    """timbrooks/instruct-pix2pix"""		,		safety_checker=A		,		torch_dtype=torch.floataa    )
								UpperCAmelCase     :		Optional[int]							     =		pipe.to(A    )
								pipe.set_progress_bar_config(disable=A    )
								pipe.enable_attention_slicing()

								UpperCAmelCase     :		Optional[int]							     =		self.get_inputs()
								pipe(**A		,		callback=A		,		callback_steps=1    )
								assert callback_fn.has_been_called
								assert number_of_steps == 3





	def  _lowercase(  self    )			->			List[Any]:
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								UpperCAmelCase     :		Union[str, Any]							     =		StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    """timbrooks/instruct-pix2pix"""		,		safety_checker=A		,		torch_dtype=torch.floataa    )
								UpperCAmelCase     :		Union[str, Any]							     =		pipe.to(A    )
								pipe.set_progress_bar_config(disable=A    )
								pipe.enable_attention_slicing(1    )
								pipe.enable_sequential_cpu_offload()

								UpperCAmelCase     :		Optional[int]							     =		self.get_inputs()
								UpperCAmelCase     :		Optional[int]							     =		pipe(**A    )

								UpperCAmelCase     :		Optional[int]							     =		torch.cuda.max_memory_allocated()
								# make sure that less than 2.2 GB is allocated
								assert mem_bytes < 2.2 * 10**9






	def  _lowercase(  self    )			->			Tuple:
								UpperCAmelCase     :		Tuple							     =		self.get_inputs()
								# resize to resolution that is divisible by 8 but not 16 or 32
								UpperCAmelCase     :		int							     =		inputs["""image"""].resize((504, 504)    )

								UpperCAmelCase     :		Union[str, Any]							     =		"""timbrooks/instruct-pix2pix"""
								UpperCAmelCase     :		List[str]							     =		StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    A		,		safety_checker=A		,		)
								pipe.to(A    )
								pipe.set_progress_bar_config(disable=A    )
								pipe.enable_attention_slicing()

								UpperCAmelCase     :		List[str]							     =		pipe(**A    )
								UpperCAmelCase     :		Optional[int]							     =		output.images[0]

								UpperCAmelCase     :		List[str]							     =		image[255:258, 383:386, -1]

								assert image.shape == (504, 504, 3)
								UpperCAmelCase     :		Optional[int]							     =		np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0]    )

								assert np.abs(image_slice.flatten() - expected_slice    ).max() < 5e-3






'''simple docstring'''




from __future__ import annotations

import math

class 			UpperCamelCase_ :





	def __init__(  self		,		A    )			->			None:
								UpperCAmelCase     :		Optional[int]							     =		size
								# approximate the overall size of segment tree with given value
								UpperCAmelCase     :		Optional[int]							     =		[0 for i in range(0		,		4 * size    )]
								# create array to store lazy update
								UpperCAmelCase     :		Any							     =		[0 for i in range(0		,		4 * size    )]
								UpperCAmelCase     :		Tuple							     =		[0 for i in range(0		,		4 * size    )]  # flag for lazy update





	def  _lowercase(  self		,		A    )			->			int:
								return idx * 2





	def  _lowercase(  self		,		A    )			->			int:
								return idx * 2 + 1





	def  _lowercase(  self		,		A		,		A		,		A		,		A    )			->			None:
								if left_element == right_element:
															UpperCAmelCase     :		str							     =		a[left_element - 1]
								else:
															UpperCAmelCase     :		Tuple							     =		(left_element + right_element) // 2
															self.build(self.left(A    )		,		A		,		A		,		A    )
															self.build(self.right(A    )		,		mid + 1		,		A		,		A    )
															UpperCAmelCase     :		str							     =		max(
															    self.segment_tree[self.left(A    )]		,		self.segment_tree[self.right(A    )]    )





	def  _lowercase(  self		,		A		,		A		,		A		,		A		,		A		,		A    )			->			bool:
								if self.flag[idx] is True:
															UpperCAmelCase     :		Optional[Any]							     =		self.lazy[idx]
															UpperCAmelCase     :		int							     =		False
															if left_element != right_element:
																						UpperCAmelCase     :		List[str]							     =		self.lazy[idx]
																						UpperCAmelCase     :		Optional[Any]							     =		self.lazy[idx]
																						UpperCAmelCase     :		List[str]							     =		True
																						UpperCAmelCase     :		int							     =		True

								if right_element < a or left_element > b:
															return True
								if left_element >= a and right_element <= b:
															UpperCAmelCase     :		Optional[Any]							     =		val
															if left_element != right_element:
																						UpperCAmelCase     :		Tuple							     =		val
																						UpperCAmelCase     :		int							     =		val
																						UpperCAmelCase     :		Any							     =		True
																						UpperCAmelCase     :		str							     =		True
															return True
								UpperCAmelCase     :		str							     =		(left_element + right_element) // 2
								self.update(self.left(A    )		,		A		,		A		,		A		,		A		,		A    )
								self.update(self.right(A    )		,		mid + 1		,		A		,		A		,		A		,		A    )
								UpperCAmelCase     :		List[str]							     =		max(
								    self.segment_tree[self.left(A    )]		,		self.segment_tree[self.right(A    )]    )
								return True





	def  _lowercase(  self		,		A		,		A		,		A		,		A		,		A    )			->			int | float:
								if self.flag[idx] is True:
															UpperCAmelCase     :		Any							     =		self.lazy[idx]
															UpperCAmelCase     :		Any							     =		False
															if left_element != right_element:
																						UpperCAmelCase     :		Optional[Any]							     =		self.lazy[idx]
																						UpperCAmelCase     :		Tuple							     =		self.lazy[idx]
																						UpperCAmelCase     :		List[str]							     =		True
																						UpperCAmelCase     :		Tuple							     =		True
								if right_element < a or left_element > b:
															return -math.inf
								if left_element >= a and right_element <= b:
															return self.segment_tree[idx]
								UpperCAmelCase     :		Dict							     =		(left_element + right_element) // 2
								UpperCAmelCase     :		List[Any]							     =		self.query(self.left(A    )		,		A		,		A		,		A		,		A    )
								UpperCAmelCase     :		str							     =		self.query(self.right(A    )		,		mid + 1		,		A		,		A		,		A    )
								return max(A		,		A    )






	def __str__(  self    )			->			str:
								return str([self.query(1		,		1		,		self.size		,		A		,		A    ) for i in range(1		,		self.size + 1    )]    )


if __name__ == "__main__":
							a   :			Optional[int]					  =    [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
							a   :			Optional[Any]					  =    1_5
							a   :			Union[str, Any]					  =    SegmentTree(size)
							segt.build(1, 1, size, A)
							print(segt.query(1, 1, size, 4, 6))
							print(segt.query(1, 1, size, 7, 1_1))
							print(segt.query(1, 1, size, 7, 1_2))
							segt.update(1, 1, size, 1, 3, 1_1_1)
							print(segt.query(1, 1, size, 1, 1_5))
							segt.update(1, 1, size, 7, 8, 2_3_5)
							print(segt)

