




"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

class 				snake_case_			(					unittest.TestCase			):







   """simple docstring"""




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







   @property
   def   UpperCAmelCase__			(		self)				->   str:
       UpperCamelCase              =	1
       UpperCamelCase              =	3
       UpperCamelCase              =	(3_2, 3_2)

       UpperCamelCase              =	floats_tensor((batch_size, num_channels) + sizes       ,					rng=random.Random(0)).to(lowerCamelCase_)
       return image







   @property
   def   UpperCAmelCase__			(		self)				->   Optional[int]:
       torch.manual_seed(0)
       UpperCamelCase              =	UNetaDConditionModel(
           block_out_channels=(3_2, 6_4)       ,					layers_per_block=2       ,					sample_size=3_2       ,					in_channels=4       ,					out_channels=4       ,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,					up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,					cross_attention_dim=3_2       ,					)
       return model







   @property
   def   UpperCAmelCase__			(		self)				->   Tuple:
       torch.manual_seed(0)
       UpperCamelCase              =	AutoencoderKL(
           block_out_channels=[3_2, 6_4]       ,					in_channels=3       ,					out_channels=3       ,					down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,					up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,					latent_channels=4       ,					)
       return model







   @property
   def   UpperCAmelCase__			(		self)				->   Dict:
       torch.manual_seed(0)
       UpperCamelCase              =	RobertaSeriesConfig(
           hidden_size=3_2       ,					project_dim=3_2       ,					intermediate_size=3_7       ,					layer_norm_eps=1e-05       ,					num_attention_heads=4       ,					num_hidden_layers=5       ,					pad_token_id=1       ,					vocab_size=5_0_0_6       ,					)
       return RobertaSeriesModelWithTransformation(lowerCamelCase_)







   @property
   def   UpperCAmelCase__			(		self)				->   List[str]:
       def extract(*lowerCamelCase_       ,					**lowerCamelCase_):

           class 				snake_case_			:







              """simple docstring"""




              def __init__(		self)				->   Tuple:
                  UpperCamelCase              =	torch.ones([0])







              def   UpperCAmelCase__			(		self       ,					lowerCamelCase_)				->   Optional[int]:
                  self.pixel_values.to(lowerCamelCase_)
                  return self
           return Out()

       return extract







   def   UpperCAmelCase__			(		self)				->   Optional[Any]:
       UpperCamelCase              =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       UpperCamelCase              =	self.dummy_cond_unet
       UpperCamelCase              =	PNDMScheduler(skip_prk_steps=lowerCamelCase_)
       UpperCamelCase              =	self.dummy_vae
       UpperCamelCase              =	self.dummy_text_encoder
       UpperCamelCase              =	XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''')
       UpperCamelCase              =	7_7

       UpperCamelCase              =	self.dummy_image.to(lowerCamelCase_)
       UpperCamelCase              =	init_image / 2 + 0.5

       # make sure here that pndm scheduler skips prk
       UpperCamelCase              =	AltDiffusionImgaImgPipeline(
           unet=lowerCamelCase_       ,					scheduler=lowerCamelCase_       ,					vae=lowerCamelCase_       ,					text_encoder=lowerCamelCase_       ,					tokenizer=lowerCamelCase_       ,					safety_checker=lowerCamelCase_       ,					feature_extractor=self.dummy_extractor       ,					)
       UpperCamelCase              =	VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor       ,					do_normalize=lowerCamelCase_)
       UpperCamelCase              =	alt_pipe.to(lowerCamelCase_)
       alt_pipe.set_progress_bar_config(disable=lowerCamelCase_)

       UpperCamelCase              =	'''A painting of a squirrel eating a burger'''
       UpperCamelCase              =	torch.Generator(device=lowerCamelCase_).manual_seed(0)
       UpperCamelCase              =	alt_pipe(
           [prompt]       ,					generator=lowerCamelCase_       ,					guidance_scale=6.0       ,					num_inference_steps=2       ,					output_type='''np'''       ,					image=lowerCamelCase_       ,					)

       UpperCamelCase              =	output.images

       UpperCamelCase              =	torch.Generator(device=lowerCamelCase_).manual_seed(0)
       UpperCamelCase              =	alt_pipe(
           [prompt]       ,					generator=lowerCamelCase_       ,					guidance_scale=6.0       ,					num_inference_steps=2       ,					output_type='''np'''       ,					image=lowerCamelCase_       ,					return_dict=lowerCamelCase_       ,					)[0]

       UpperCamelCase              =	image[0, -3:, -3:, -1]
       UpperCamelCase              =	image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 3_2, 3_2, 3)
       UpperCamelCase              =	np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499])

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







   @unittest.skipIf(torch_device != '''cuda'''       ,					'''This test requires a GPU''')
   def   UpperCAmelCase__			(		self)				->   Tuple:
       UpperCamelCase              =	self.dummy_cond_unet
       UpperCamelCase              =	PNDMScheduler(skip_prk_steps=lowerCamelCase_)
       UpperCamelCase              =	self.dummy_vae
       UpperCamelCase              =	self.dummy_text_encoder
       UpperCamelCase              =	XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta''')
       UpperCamelCase              =	7_7

       UpperCamelCase              =	self.dummy_image.to(lowerCamelCase_)

       # put models in fp16
       UpperCamelCase              =	unet.half()
       UpperCamelCase              =	vae.half()
       UpperCamelCase              =	bert.half()

       # make sure here that pndm scheduler skips prk
       UpperCamelCase              =	AltDiffusionImgaImgPipeline(
           unet=lowerCamelCase_       ,					scheduler=lowerCamelCase_       ,					vae=lowerCamelCase_       ,					text_encoder=lowerCamelCase_       ,					tokenizer=lowerCamelCase_       ,					safety_checker=lowerCamelCase_       ,					feature_extractor=self.dummy_extractor       ,					)
       UpperCamelCase              =	VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor       ,					do_normalize=lowerCamelCase_)
       UpperCamelCase              =	alt_pipe.to(lowerCamelCase_)
       alt_pipe.set_progress_bar_config(disable=lowerCamelCase_)

       UpperCamelCase              =	'''A painting of a squirrel eating a burger'''
       UpperCamelCase              =	torch.manual_seed(0)
       UpperCamelCase              =	alt_pipe(
           [prompt]       ,					generator=lowerCamelCase_       ,					num_inference_steps=2       ,					output_type='''np'''       ,					image=lowerCamelCase_       ,					).images

       assert image.shape == (1, 3_2, 3_2, 3)







   @unittest.skipIf(torch_device != '''cuda'''       ,					'''This test requires a GPU''')
   def   UpperCAmelCase__			(		self)				->   Tuple:
       UpperCamelCase              =	load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/img2img/sketch-mountains-input.jpg''')
       # resize to resolution that is divisible by 8 but not 16 or 32
       UpperCamelCase              =	init_image.resize((7_6_0, 5_0_4))

       UpperCamelCase              =	'''BAAI/AltDiffusion'''
       UpperCamelCase              =	AltDiffusionImgaImgPipeline.from_pretrained(
           lowerCamelCase_       ,					safety_checker=lowerCamelCase_       ,					)
       pipe.to(lowerCamelCase_)
       pipe.set_progress_bar_config(disable=lowerCamelCase_)
       pipe.enable_attention_slicing()

       UpperCamelCase              =	'''A fantasy landscape, trending on artstation'''

       UpperCamelCase              =	torch.manual_seed(0)
       UpperCamelCase              =	pipe(
           prompt=lowerCamelCase_       ,					image=lowerCamelCase_       ,					strength=0.75       ,					guidance_scale=7.5       ,					generator=lowerCamelCase_       ,					output_type='''np'''       ,					)
       UpperCamelCase              =	output.images[0]

       UpperCamelCase              =	image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

       assert image.shape == (5_0_4, 7_6_0, 3)
       UpperCamelCase              =	np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000])

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

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







   """simple docstring"""




   def   UpperCAmelCase__			(		self)				->   List[str]:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







   def   UpperCAmelCase__			(		self)				->   List[str]:
       UpperCamelCase              =	load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/img2img/sketch-mountains-input.jpg''')
       UpperCamelCase              =	init_image.resize((7_6_8, 5_1_2))
       UpperCamelCase              =	load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy''')

       UpperCamelCase              =	'''BAAI/AltDiffusion'''
       UpperCamelCase              =	AltDiffusionImgaImgPipeline.from_pretrained(
           lowerCamelCase_       ,					safety_checker=lowerCamelCase_       ,					)
       pipe.to(lowerCamelCase_)
       pipe.set_progress_bar_config(disable=lowerCamelCase_)
       pipe.enable_attention_slicing()

       UpperCamelCase              =	'''A fantasy landscape, trending on artstation'''

       UpperCamelCase              =	torch.manual_seed(0)
       UpperCamelCase              =	pipe(
           prompt=lowerCamelCase_       ,					image=lowerCamelCase_       ,					strength=0.75       ,					guidance_scale=7.5       ,					generator=lowerCamelCase_       ,					output_type='''np'''       ,					)
       UpperCamelCase              =	output.images[0]

       assert image.shape == (5_1_2, 7_6_8, 3)
       # img2img is flaky across GPUs even in fp32, so using MAE here
       assert np.abs(expected_image - image).max() < 1e-2


"""simple docstring"""
from __future__ import annotations

import math

class       lowerCamelCase	:


  '''simple docstring'''







  def __init__(self							,       _lowerCamelCase	):


        """simple docstring"""

        UpperCAmelCase__			:							List[str]							=    size
        # approximate the overall size of segment tree with given value
        UpperCAmelCase__			:							Tuple							=    [0 for i in range(0							,       4 * size	)]
        # create array to store lazy update
        UpperCAmelCase__			:							int							=    [0 for i in range(0							,       4 * size	)]
        UpperCAmelCase__			:							Tuple							=    [0 for i in range(0							,       4 * size	)]  # flag for lazy update



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        return idx * 2



  def  _a      (self							,       _lowerCamelCase	):


        """simple docstring"""

        return idx * 2 + 1



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        if left_element == right_element:
              UpperCAmelCase__			:							Union[str, Any]							=    a[left_element - 1]
        else:
              UpperCAmelCase__			:							List[str]							=    (left_element + right_element) // 2
              self.build(self.left(_lowerCamelCase	)							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
              self.build(self.right(_lowerCamelCase	)							,       mid + 1							,       _lowerCamelCase							,       _lowerCamelCase	)
              UpperCAmelCase__			:							int							=    max(
                  self.segment_tree[self.left(_lowerCamelCase	)]							,       self.segment_tree[self.right(_lowerCamelCase	)]	)



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        if self.flag[idx] is True:
              UpperCAmelCase__			:							Union[str, Any]							=    self.lazy[idx]
              UpperCAmelCase__			:							Dict							=    False
              if left_element != right_element:
                    UpperCAmelCase__			:							Optional[Any]							=    self.lazy[idx]
                    UpperCAmelCase__			:							Optional[int]							=    self.lazy[idx]
                    UpperCAmelCase__			:							Tuple							=    True
                    UpperCAmelCase__			:							str							=    True

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



  def  _a      (self							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	):


        """simple docstring"""

        if self.flag[idx] is True:
              UpperCAmelCase__			:							List[Any]							=    self.lazy[idx]
              UpperCAmelCase__			:							Optional[Any]							=    False
              if left_element != right_element:
                    UpperCAmelCase__			:							Tuple							=    self.lazy[idx]
                    UpperCAmelCase__			:							Union[str, Any]							=    self.lazy[idx]
                    UpperCAmelCase__			:							List[str]							=    True
                    UpperCAmelCase__			:							str							=    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__			:							Union[str, Any]							=    (left_element + right_element) // 2
        UpperCAmelCase__			:							Tuple							=    self.query(self.left(_lowerCamelCase	)							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        UpperCAmelCase__			:							List[Any]							=    self.query(self.right(_lowerCamelCase	)							,       mid + 1							,       _lowerCamelCase							,       _lowerCamelCase							,       _lowerCamelCase	)
        return max(_lowerCamelCase							,       _lowerCamelCase	)




  def __str__(self	):


        """simple docstring"""

        return str([self.query(1							,       1							,       self.size							,       _lowerCamelCase							,       _lowerCamelCase	) for i in range(1							,       self.size + 1	)]	)


if __name__ == "__main__":
    _A							=  [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
    _A							=  15
    _A							=  SegmentTree(size)
    segt.build(1, 1, size, A)
    print(segt.query(1, 1, size, 4, 6))
    print(segt.query(1, 1, size, 7, 11))
    print(segt.query(1, 1, size, 7, 12))
    segt.update(1, 1, size, 1, 3, 1_11)
    print(segt.query(1, 1, size, 1, 15))
    segt.update(1, 1, size, 7, 8, 2_35)
    print(segt)

