






'''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 		UpperCamelCase__     (unittest.TestCase      ):




		'''simple docstring'''





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






		@property
		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	1
								lowerCamelCase__								=	3
								lowerCamelCase__								=	(32, 32)

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






		@property
		def       UpperCamelCase_       (   self							):
								torch.manual_seed(0							)
								lowerCamelCase__								=	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       UpperCamelCase_       (   self							):
								torch.manual_seed(0							)
								lowerCamelCase__								=	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       UpperCamelCase_       (   self							):
								torch.manual_seed(0							)
								lowerCamelCase__								=	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=10_00 ,)
								return CLIPTextModel(_lowerCAmelCase							)






		@property
		def       UpperCamelCase_       (   self							):
								def extract(*_lowerCAmelCase ,**_lowerCAmelCase							):




														class 		UpperCamelCase__     :




																'''simple docstring'''





																def __init__(   self							):
																						lowerCamelCase__								=	torch.ones([0]							)






																def       UpperCamelCase_       (   self ,_lowerCAmelCase							):
																						self.pixel_values.to(_lowerCAmelCase							)
																						return self



														return Out()

								return extract






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
								lowerCamelCase__								=	self.dummy_cond_unet
								lowerCamelCase__								=	DDIMScheduler(
								    beta_start=0.0_0085 ,beta_end=0.012 ,beta_schedule="""scaled_linear""" ,clip_sample=_lowerCAmelCase ,set_alpha_to_one=_lowerCAmelCase ,)

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

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

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

								lowerCamelCase__								=	torch.Generator(device=_lowerCAmelCase							).manual_seed(0							)
								lowerCamelCase__								=	sd_pipe([prompt] ,generator=_lowerCAmelCase ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np"""							)
								lowerCamelCase__								=	output.images

								lowerCamelCase__								=	torch.Generator(device=_lowerCAmelCase							).manual_seed(0							)
								lowerCamelCase__								=	sd_pipe(
								    [prompt] ,generator=_lowerCAmelCase ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" ,return_dict=_lowerCAmelCase ,)[0]

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

								assert image.shape == (1, 64, 64, 3)
								lowerCamelCase__								=	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       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
								lowerCamelCase__								=	self.dummy_cond_unet
								lowerCamelCase__								=	PNDMScheduler(skip_prk_steps=_lowerCAmelCase							)
								lowerCamelCase__								=	self.dummy_vae
								lowerCamelCase__								=	self.dummy_text_encoder
								lowerCamelCase__								=	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

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

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

								lowerCamelCase__								=	output.images

								lowerCamelCase__								=	torch.Generator(device=_lowerCAmelCase							).manual_seed(0							)
								lowerCamelCase__								=	sd_pipe(
								    [prompt] ,generator=_lowerCAmelCase ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="""np""" ,return_dict=_lowerCAmelCase ,)[0]

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

								assert image.shape == (1, 64, 64, 3)
								lowerCamelCase__								=	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       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	StableDiffusionPipeline.from_pretrained(
								    """hf-internal-testing/tiny-stable-diffusion-lms-pipe""" ,safety_checker=_lowerCAmelCase							)
								assert isinstance(_lowerCAmelCase ,_lowerCAmelCase							)
								assert isinstance(pipe.scheduler ,_lowerCAmelCase							)
								assert pipe.safety_checker is None

								lowerCamelCase__								=	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(_lowerCAmelCase							)
														lowerCamelCase__								=	StableDiffusionPipeline.from_pretrained(_lowerCAmelCase							)

								# sanity check that the pipeline still works
								assert pipe.safety_checker is None
								lowerCamelCase__								=	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       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.dummy_cond_unet
								lowerCamelCase__								=	PNDMScheduler(skip_prk_steps=_lowerCAmelCase							)
								lowerCamelCase__								=	self.dummy_vae
								lowerCamelCase__								=	self.dummy_text_encoder
								lowerCamelCase__								=	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)

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

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

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

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




@nightly
@require_torch_gpu
class 		UpperCamelCase__     (unittest.TestCase      ):




		'''simple docstring'''





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






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" ,safety_checker=_lowerCAmelCase							)
								lowerCamelCase__								=	LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config							)
								lowerCamelCase__								=	sd_pipe.to(_lowerCAmelCase							)
								sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

								lowerCamelCase__								=	(
								    """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 """
								)
								lowerCamelCase__								=	40_03_66_03_46
								lowerCamelCase__								=	7

								# without safety guidance (sld_guidance_scale = 0)
								lowerCamelCase__								=	torch.manual_seed(_lowerCAmelCase							)
								lowerCamelCase__								=	sd_pipe(
								    [prompt] ,generator=_lowerCAmelCase ,guidance_scale=_lowerCAmelCase ,num_inference_steps=50 ,output_type="""np""" ,width=5_12 ,height=5_12 ,sld_guidance_scale=0 ,)

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

								assert image.shape == (1, 5_12, 5_12, 3)

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

								# without safety guidance (strong configuration)
								lowerCamelCase__								=	torch.manual_seed(_lowerCAmelCase							)
								lowerCamelCase__								=	sd_pipe(
								    [prompt] ,generator=_lowerCAmelCase ,guidance_scale=_lowerCAmelCase ,num_inference_steps=50 ,output_type="""np""" ,width=5_12 ,height=5_12 ,sld_guidance_scale=20_00 ,sld_warmup_steps=7 ,sld_threshold=0.025 ,sld_momentum_scale=0.5 ,sld_mom_beta=0.7 ,)

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

								assert image.shape == (1, 5_12, 5_12, 3)

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






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" ,safety_checker=_lowerCAmelCase							)
								lowerCamelCase__								=	LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config							)
								lowerCamelCase__								=	sd_pipe.to(_lowerCAmelCase							)
								sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

								lowerCamelCase__								=	"""padme amidala taking a bath artwork, safe for work, no nudity"""
								lowerCamelCase__								=	27_34_97_17_55
								lowerCamelCase__								=	7

								lowerCamelCase__								=	torch.manual_seed(_lowerCAmelCase							)
								lowerCamelCase__								=	sd_pipe(
								    [prompt] ,generator=_lowerCAmelCase ,guidance_scale=_lowerCAmelCase ,num_inference_steps=50 ,output_type="""np""" ,width=5_12 ,height=5_12 ,sld_guidance_scale=0 ,)

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

								assert image.shape == (1, 5_12, 5_12, 3)

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

								lowerCamelCase__								=	torch.manual_seed(_lowerCAmelCase							)
								lowerCamelCase__								=	sd_pipe(
								    [prompt] ,generator=_lowerCAmelCase ,guidance_scale=_lowerCAmelCase ,num_inference_steps=50 ,output_type="""np""" ,width=5_12 ,height=5_12 ,sld_guidance_scale=20_00 ,sld_warmup_steps=7 ,sld_threshold=0.025 ,sld_momentum_scale=0.5 ,sld_mom_beta=0.7 ,)

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

								assert image.shape == (1, 5_12, 5_12, 3)

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






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""							)
								lowerCamelCase__								=	sd_pipe.to(_lowerCAmelCase							)
								sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

								lowerCamelCase__								=	(
								    """the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
								    """ leyendecker"""
								)
								lowerCamelCase__								=	10_44_35_52_34
								lowerCamelCase__								=	12

								lowerCamelCase__								=	torch.manual_seed(_lowerCAmelCase							)
								lowerCamelCase__								=	sd_pipe(
								    [prompt] ,generator=_lowerCAmelCase ,guidance_scale=_lowerCAmelCase ,num_inference_steps=50 ,output_type="""np""" ,width=5_12 ,height=5_12 ,sld_guidance_scale=0 ,)

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

								assert image.shape == (1, 5_12, 5_12, 3)

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

								lowerCamelCase__								=	torch.manual_seed(_lowerCAmelCase							)
								lowerCamelCase__								=	sd_pipe(
								    [prompt] ,generator=_lowerCAmelCase ,guidance_scale=_lowerCAmelCase ,num_inference_steps=50 ,output_type="""np""" ,width=5_12 ,height=5_12 ,sld_guidance_scale=20_00 ,sld_warmup_steps=7 ,sld_threshold=0.025 ,sld_momentum_scale=0.5 ,sld_mom_beta=0.7 ,)

								lowerCamelCase__								=	output.images
								lowerCamelCase__								=	image[0, -3:, -3:, -1]
								lowerCamelCase__								=	np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561]							)
								assert image.shape == (1, 5_12, 5_12, 3)

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



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__						=				{
    "configuration_upernet": ["UperNetConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase__						=				[
		    "UperNetForSemanticSegmentation",
		    "UperNetPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_upernet import UperNetConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
		import sys

		lowerCamelCase__						=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



