




'''simple docstring'''







import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


class     _a						(			unittest.TestCase				):
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
       @property
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	1
           UpperCAmelCase     =	3
           UpperCAmelCase     =	(32, 32)

           UpperCAmelCase     =	floats_tensor((batch_size, num_channels) + sizes     ,       rng=random.Random(0       )       ).to(A__       )
           return image
       @property
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           torch.manual_seed(0       )
           UpperCAmelCase     =	UNetaDConditionModel(
               block_out_channels=(32, 64)     ,       layers_per_block=2     ,       sample_size=32     ,       in_channels=4     ,       out_channels=4     ,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')     ,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')     ,       cross_attention_dim=32     ,       )
           return model
       @property
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           torch.manual_seed(0       )
           UpperCAmelCase     =	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     ,       )
           return model
       @property
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           torch.manual_seed(0       )
           UpperCAmelCase     =	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=1_000     ,       )
           return CLIPTextModel(A__       )
       @property
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           def extract(*lowercase		:			Optional[int]     ,       **lowercase		:			Dict       ):


               class     _a						:
                      def __init__(			self		:			List[str]       ):



                          '''simple docstring'''

                          UpperCAmelCase     =	torch.ones([0]       )







                      def 					A   (			self		:			Dict     ,       lowercase		:			Any       ):



                          '''simple docstring'''

                          self.pixel_values.to(A__       )
                          return self






               return Out()

           return extract
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
           UpperCAmelCase     =	self.dummy_cond_unet
           UpperCAmelCase     =	DDIMScheduler(
               beta_start=0.0_0085     ,       beta_end=0.012     ,       beta_schedule='''scaled_linear'''     ,       clip_sample=A__     ,       set_alpha_to_one=A__     ,       )

           UpperCAmelCase     =	self.dummy_vae
           UpperCAmelCase     =	self.dummy_text_encoder
           UpperCAmelCase     =	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

           # make sure here that pndm scheduler skips prk
           UpperCAmelCase     =	StableDiffusionPipeline(
               unet=A__     ,       scheduler=A__     ,       vae=A__     ,       text_encoder=A__     ,       tokenizer=A__     ,       safety_checker=A__     ,       feature_extractor=self.dummy_extractor     ,       )
           UpperCAmelCase     =	sd_pipe.to(A__       )
           sd_pipe.set_progress_bar_config(disable=A__       )

           UpperCAmelCase     =	"""A painting of a squirrel eating a burger"""

           UpperCAmelCase     =	torch.Generator(device=A__       ).manual_seed(0       )
           UpperCAmelCase     =	sd_pipe([prompt]     ,       generator=A__     ,       guidance_scale=6.0     ,       num_inference_steps=2     ,       output_type='''np'''       )
           UpperCAmelCase     =	output.images

           UpperCAmelCase     =	torch.Generator(device=A__       ).manual_seed(0       )
           UpperCAmelCase     =	sd_pipe(
               [prompt]     ,       generator=A__     ,       guidance_scale=6.0     ,       num_inference_steps=2     ,       output_type='''np'''     ,       return_dict=A__     ,       )[0]

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

           assert image.shape == (1, 64, 64, 3)
           UpperCAmelCase     =	np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864]       )

           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 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase     =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
           UpperCAmelCase     =	self.dummy_cond_unet
           UpperCAmelCase     =	PNDMScheduler(skip_prk_steps=A__       )
           UpperCAmelCase     =	self.dummy_vae
           UpperCAmelCase     =	self.dummy_text_encoder
           UpperCAmelCase     =	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

           # make sure here that pndm scheduler skips prk
           UpperCAmelCase     =	StableDiffusionPipeline(
               unet=A__     ,       scheduler=A__     ,       vae=A__     ,       text_encoder=A__     ,       tokenizer=A__     ,       safety_checker=A__     ,       feature_extractor=self.dummy_extractor     ,       )
           UpperCAmelCase     =	sd_pipe.to(A__       )
           sd_pipe.set_progress_bar_config(disable=A__       )

           UpperCAmelCase     =	"""A painting of a squirrel eating a burger"""
           UpperCAmelCase     =	torch.Generator(device=A__       ).manual_seed(0       )
           UpperCAmelCase     =	sd_pipe([prompt]     ,       generator=A__     ,       guidance_scale=6.0     ,       num_inference_steps=2     ,       output_type='''np'''       )

           UpperCAmelCase     =	output.images

           UpperCAmelCase     =	torch.Generator(device=A__       ).manual_seed(0       )
           UpperCAmelCase     =	sd_pipe(
               [prompt]     ,       generator=A__     ,       guidance_scale=6.0     ,       num_inference_steps=2     ,       output_type='''np'''     ,       return_dict=A__     ,       )[0]

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

           assert image.shape == (1, 64, 64, 3)
           UpperCAmelCase     =	np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993]       )

           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 					A   (			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	StableDiffusionPipeline.from_pretrained(
               '''hf-internal-testing/tiny-stable-diffusion-lms-pipe'''     ,       safety_checker=A__       )
           assert isinstance(A__     ,       A__       )
           assert isinstance(pipe.scheduler     ,       A__       )
           assert pipe.safety_checker is None

           UpperCAmelCase     =	pipe('''example prompt'''     ,       num_inference_steps=2       ).images[0]
           assert image is not None

           # check that there's no error when saving a pipeline with one of the models being None
           with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(A__       )
               UpperCAmelCase     =	StableDiffusionPipeline.from_pretrained(A__       )

           # sanity check that the pipeline still works
           assert pipe.safety_checker is None
           UpperCAmelCase     =	pipe('''example prompt'''     ,       num_inference_steps=2       ).images[0]
           assert image is not None







       @unittest.skipIf(torch_device != '''cuda'''     ,       '''This test requires a GPU'''       )
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.dummy_cond_unet
           UpperCAmelCase     =	PNDMScheduler(skip_prk_steps=A__       )
           UpperCAmelCase     =	self.dummy_vae
           UpperCAmelCase     =	self.dummy_text_encoder
           UpperCAmelCase     =	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

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

           # make sure here that pndm scheduler skips prk
           UpperCAmelCase     =	StableDiffusionPipeline(
               unet=A__     ,       scheduler=A__     ,       vae=A__     ,       text_encoder=A__     ,       tokenizer=A__     ,       safety_checker=A__     ,       feature_extractor=self.dummy_extractor     ,       )
           UpperCAmelCase     =	sd_pipe.to(A__       )
           sd_pipe.set_progress_bar_config(disable=A__       )

           UpperCAmelCase     =	"""A painting of a squirrel eating a burger"""
           UpperCAmelCase     =	sd_pipe([prompt]     ,       num_inference_steps=2     ,       output_type='''np'''       ).images

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






@nightly
@require_torch_gpu
class     _a						(			unittest.TestCase				):
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''     ,       safety_checker=A__       )
           UpperCAmelCase     =	LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config       )
           UpperCAmelCase     =	sd_pipe.to(A__       )
           sd_pipe.set_progress_bar_config(disable=A__       )

           UpperCAmelCase     =	(
               """portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"""
               """ coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"""
               """ anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"""
               """ children from bahnhof zoo, detailed """
           )
           UpperCAmelCase     =	4_003_660_346
           UpperCAmelCase     =	7

           # without safety guidance (sld_guidance_scale = 0)
           UpperCAmelCase     =	torch.manual_seed(A__       )
           UpperCAmelCase     =	sd_pipe(
               [prompt]     ,       generator=A__     ,       guidance_scale=A__     ,       num_inference_steps=50     ,       output_type='''np'''     ,       width=512     ,       height=512     ,       sld_guidance_scale=0     ,       )

           UpperCAmelCase     =	output.images
           UpperCAmelCase     =	image[0, -3:, -3:, -1]
           UpperCAmelCase     =	[0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

           assert image.shape == (1, 512, 512, 3)

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

           # without safety guidance (strong configuration)
           UpperCAmelCase     =	torch.manual_seed(A__       )
           UpperCAmelCase     =	sd_pipe(
               [prompt]     ,       generator=A__     ,       guidance_scale=A__     ,       num_inference_steps=50     ,       output_type='''np'''     ,       width=512     ,       height=512     ,       sld_guidance_scale=2_000     ,       sld_warmup_steps=7     ,       sld_threshold=0.025     ,       sld_momentum_scale=0.5     ,       sld_mom_beta=0.7     ,       )

           UpperCAmelCase     =	output.images
           UpperCAmelCase     =	image[0, -3:, -3:, -1]
           UpperCAmelCase     =	[0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

           assert image.shape == (1, 512, 512, 3)

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           UpperCAmelCase     =	StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''     ,       safety_checker=A__       )
           UpperCAmelCase     =	LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config       )
           UpperCAmelCase     =	sd_pipe.to(A__       )
           sd_pipe.set_progress_bar_config(disable=A__       )

           UpperCAmelCase     =	"""padme amidala taking a bath artwork, safe for work, no nudity"""
           UpperCAmelCase     =	2_734_971_755
           UpperCAmelCase     =	7

           UpperCAmelCase     =	torch.manual_seed(A__       )
           UpperCAmelCase     =	sd_pipe(
               [prompt]     ,       generator=A__     ,       guidance_scale=A__     ,       num_inference_steps=50     ,       output_type='''np'''     ,       width=512     ,       height=512     ,       sld_guidance_scale=0     ,       )

           UpperCAmelCase     =	output.images
           UpperCAmelCase     =	image[0, -3:, -3:, -1]
           UpperCAmelCase     =	[0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

           assert image.shape == (1, 512, 512, 3)

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

           UpperCAmelCase     =	torch.manual_seed(A__       )
           UpperCAmelCase     =	sd_pipe(
               [prompt]     ,       generator=A__     ,       guidance_scale=A__     ,       num_inference_steps=50     ,       output_type='''np'''     ,       width=512     ,       height=512     ,       sld_guidance_scale=2_000     ,       sld_warmup_steps=7     ,       sld_threshold=0.025     ,       sld_momentum_scale=0.5     ,       sld_mom_beta=0.7     ,       )

           UpperCAmelCase     =	output.images
           UpperCAmelCase     =	image[0, -3:, -3:, -1]
           UpperCAmelCase     =	[0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

           assert image.shape == (1, 512, 512, 3)

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







       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''       )
           UpperCAmelCase     =	sd_pipe.to(A__       )
           sd_pipe.set_progress_bar_config(disable=A__       )

           UpperCAmelCase     =	(
               """the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
               """ leyendecker"""
           )
           UpperCAmelCase     =	1_044_355_234
           UpperCAmelCase     =	12

           UpperCAmelCase     =	torch.manual_seed(A__       )
           UpperCAmelCase     =	sd_pipe(
               [prompt]     ,       generator=A__     ,       guidance_scale=A__     ,       num_inference_steps=50     ,       output_type='''np'''     ,       width=512     ,       height=512     ,       sld_guidance_scale=0     ,       )

           UpperCAmelCase     =	output.images
           UpperCAmelCase     =	image[0, -3:, -3:, -1]
           UpperCAmelCase     =	np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]       )

           assert image.shape == (1, 512, 512, 3)

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-7

           UpperCAmelCase     =	torch.manual_seed(A__       )
           UpperCAmelCase     =	sd_pipe(
               [prompt]     ,       generator=A__     ,       guidance_scale=A__     ,       num_inference_steps=50     ,       output_type='''np'''     ,       width=512     ,       height=512     ,       sld_guidance_scale=2_000     ,       sld_warmup_steps=7     ,       sld_threshold=0.025     ,       sld_momentum_scale=0.5     ,       sld_mom_beta=0.7     ,       )

           UpperCAmelCase     =	output.images
           UpperCAmelCase     =	image[0, -3:, -3:, -1]
           UpperCAmelCase     =	np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561]       )
           assert image.shape == (1, 512, 512, 3)

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









'''simple docstring'''







from __future__ import annotations

import math


class     _a						:
       def __init__(			self		:			Dict     ,       lowercase		:			int       ):



           '''simple docstring'''

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



           '''simple docstring'''

           return idx * 2
       def 					A   (			self		:			Optional[Any]     ,       lowercase		:			int       ):



           '''simple docstring'''

           return idx * 2 + 1
       def 					A   (			self		:			Dict     ,       lowercase		:			int     ,       lowercase		:			int     ,       lowercase		:			int     ,       lowercase		:			list[int]       ):



           '''simple docstring'''

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



           '''simple docstring'''

           if self.flag[idx] is True:
               UpperCAmelCase     =	self.lazy[idx]
               UpperCAmelCase     =	False
               if left_element != right_element:
                   UpperCAmelCase     =	self.lazy[idx]
                   UpperCAmelCase     =	self.lazy[idx]
                   UpperCAmelCase     =	True
                   UpperCAmelCase     =	True

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



           '''simple docstring'''

           if self.flag[idx] is True:
               UpperCAmelCase     =	self.lazy[idx]
               UpperCAmelCase     =	False
               if left_element != right_element:
                   UpperCAmelCase     =	self.lazy[idx]
                   UpperCAmelCase     =	self.lazy[idx]
                   UpperCAmelCase     =	True
                   UpperCAmelCase     =	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     =	(left_element + right_element) // 2
           UpperCAmelCase     =	self.query(self.left(lowercase       )     ,       lowercase     ,       lowercase     ,       lowercase     ,       lowercase       )
           UpperCAmelCase     =	self.query(self.right(lowercase       )     ,       mid + 1     ,       lowercase     ,       lowercase     ,       lowercase       )
           return max(lowercase     ,       lowercase       )







       def __str__(			self		:			Any       ):



           '''simple docstring'''

           return str([self.query(1     ,       1     ,       self.size     ,       lowercase     ,       lowercase       ) 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)




