
"""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 __lowerCAmelCase				(					unittest.TestCase      ):




			"""simple docstring"""







			def      lowerCamelCase__     (				self						:					Tuple	)      -> int:







								"""simple docstring"""
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




			@property
			def      lowerCamelCase__     (				self						:					List[Any]	)      -> List[str]:







								"""simple docstring"""
								A_				 =						1
								A_				 =						3
								A_				 =						(32, 32)

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




			@property
			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> Optional[int]:







								"""simple docstring"""
								torch.manual_seed(0	)
								A_				 =						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      lowerCamelCase__     (				self						:					List[str]	)      -> str:







								"""simple docstring"""
								torch.manual_seed(0	)
								A_				 =						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      lowerCamelCase__     (				self						:					Tuple	)      -> str:







								"""simple docstring"""
								torch.manual_seed(0	)
								A_				 =						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(__lowercase	)




			@property
			def      lowerCamelCase__     (				self						:					Dict	)      -> Union[str, Any]:







								"""simple docstring"""
								def extract(*_snake_case						:					Optional[Any]       ,   **_snake_case						:					Union[str, Any]	):



													class __lowerCAmelCase				:




																"""simple docstring"""







																def __init__(				self						:					Optional[int]	)      -> Union[str, Any]:







																					"""simple docstring"""
																					A_				 =						torch.ones([0]	)






																def      lowerCamelCase__     (				self						:					Any       ,   _snake_case						:					Dict	)      -> int:







																					"""simple docstring"""
																					self.pixel_values.to(__lowercase	)
																					return self





													return Out()

								return extract




			def      lowerCamelCase__     (				self						:					List[Any]	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						"cpu"  # ensure determinism for the device-dependent torch.Generator
								A_				 =						self.dummy_cond_unet
								A_				 =						DDIMScheduler(
								    beta_start=0.0_0_0_8_5       ,   beta_end=0.0_1_2       ,   beta_schedule="scaled_linear"       ,   clip_sample=__lowercase       ,   set_alpha_to_one=__lowercase       ,   )

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

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

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

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

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

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

								assert image.shape == (1, 64, 64, 3)
								A_				 =						np.array([0.5_7_5_6, 0.6_1_1_8, 0.5_0_0_5, 0.5_0_4_1, 0.5_4_7_1, 0.4_7_2_6, 0.4_9_7_6, 0.4_8_6_5, 0.4_8_6_4]	)

								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						:					int	)      -> str:







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

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

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

								A_				 =						output.images

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

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

								assert image.shape == (1, 64, 64, 3)
								A_				 =						np.array([0.5_1_2_5, 0.5_7_1_6, 0.4_8_2_8, 0.5_0_6_0, 0.5_6_5_0, 0.4_7_6_8, 0.5_1_8_5, 0.4_8_9_5, 0.4_9_9_3]	)

								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						:					Union[str, Any]	)      -> str:







								"""simple docstring"""
								A_				 =						StableDiffusionPipeline.from_pretrained(
								    "hf-internal-testing/tiny-stable-diffusion-lms-pipe"       ,   safety_checker=__lowercase	)
								assert isinstance(__lowercase       ,   __lowercase	)
								assert isinstance(pipe.scheduler       ,   __lowercase	)
								assert pipe.safety_checker is None

								A_				 =						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(__lowercase	)
													A_				 =						StableDiffusionPipeline.from_pretrained(__lowercase	)

								# sanity check that the pipeline still works
								assert pipe.safety_checker is None
								A_				 =						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      lowerCamelCase__     (				self						:					Optional[Any]	)      -> int:







								"""simple docstring"""
								A_				 =						self.dummy_cond_unet
								A_				 =						PNDMScheduler(skip_prk_steps=__lowercase	)
								A_				 =						self.dummy_vae
								A_				 =						self.dummy_text_encoder
								A_				 =						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

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

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

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

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





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




			"""simple docstring"""







			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> Optional[Any]:







								"""simple docstring"""
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Tuple:







								"""simple docstring"""
								A_				 =						StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"       ,   safety_checker=__lowercase	)
								A_				 =						LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config	)
								A_				 =						sd_pipe.to(__lowercase	)
								sd_pipe.set_progress_bar_config(disable=__lowercase	)

								A_				 =						(
								    "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 "
								)
								A_				 =						4_003_660_346
								A_				 =						7

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

								A_				 =						output.images
								A_				 =						image[0, -3:, -3:, -1]
								A_				 =						[0.2_2_7_8, 0.2_2_3_1, 0.2_2_4_9, 0.2_3_3_3, 0.2_3_0_3, 0.1_8_8_5, 0.2_2_7_3, 0.2_1_4_4, 0.2_1_7_6]

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

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

								# without safety guidance (strong configuration)
								A_				 =						torch.manual_seed(__lowercase	)
								A_				 =						sd_pipe(
								    [prompt]       ,   generator=__lowercase       ,   guidance_scale=__lowercase       ,   num_inference_steps=50       ,   output_type="np"       ,   width=512       ,   height=512       ,   sld_guidance_scale=2_000       ,   sld_warmup_steps=7       ,   sld_threshold=0.0_2_5       ,   sld_momentum_scale=0.5       ,   sld_mom_beta=0.7       ,   )

								A_				 =						output.images
								A_				 =						image[0, -3:, -3:, -1]
								A_				 =						[0.2_3_8_3, 0.2_2_7_6, 0.2_3_6, 0.2_1_9_2, 0.2_1_8_6, 0.2_0_5_3, 0.1_9_7_1, 0.1_9_0_1, 0.1_7_1_9]

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

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




			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> Dict:







								"""simple docstring"""
								A_				 =						StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"       ,   safety_checker=__lowercase	)
								A_				 =						LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config	)
								A_				 =						sd_pipe.to(__lowercase	)
								sd_pipe.set_progress_bar_config(disable=__lowercase	)

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

								A_				 =						torch.manual_seed(__lowercase	)
								A_				 =						sd_pipe(
								    [prompt]       ,   generator=__lowercase       ,   guidance_scale=__lowercase       ,   num_inference_steps=50       ,   output_type="np"       ,   width=512       ,   height=512       ,   sld_guidance_scale=0       ,   )

								A_				 =						output.images
								A_				 =						image[0, -3:, -3:, -1]
								A_				 =						[0.3_5_0_2, 0.3_6_2_2, 0.3_3_9_6, 0.3_6_4_2, 0.3_4_7_8, 0.3_3_1_8, 0.3_5, 0.3_3_4_8, 0.3_2_9_7]

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

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

								A_				 =						torch.manual_seed(__lowercase	)
								A_				 =						sd_pipe(
								    [prompt]       ,   generator=__lowercase       ,   guidance_scale=__lowercase       ,   num_inference_steps=50       ,   output_type="np"       ,   width=512       ,   height=512       ,   sld_guidance_scale=2_000       ,   sld_warmup_steps=7       ,   sld_threshold=0.0_2_5       ,   sld_momentum_scale=0.5       ,   sld_mom_beta=0.7       ,   )

								A_				 =						output.images
								A_				 =						image[0, -3:, -3:, -1]
								A_				 =						[0.5_5_3_1, 0.5_2_0_6, 0.4_8_9_5, 0.5_1_5_6, 0.5_1_8_2, 0.4_7_5_1, 0.4_8_0_2, 0.4_8_0_3, 0.4_4_4_3]

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

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






			def      lowerCamelCase__     (				self						:					List[str]	)      -> Tuple:







								"""simple docstring"""
								A_				 =						StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"	)
								A_				 =						sd_pipe.to(__lowercase	)
								sd_pipe.set_progress_bar_config(disable=__lowercase	)

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

								A_				 =						torch.manual_seed(__lowercase	)
								A_				 =						sd_pipe(
								    [prompt]       ,   generator=__lowercase       ,   guidance_scale=__lowercase       ,   num_inference_steps=50       ,   output_type="np"       ,   width=512       ,   height=512       ,   sld_guidance_scale=0       ,   )

								A_				 =						output.images
								A_				 =						image[0, -3:, -3:, -1]
								A_				 =						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

								A_				 =						torch.manual_seed(__lowercase	)
								A_				 =						sd_pipe(
								    [prompt]       ,   generator=__lowercase       ,   guidance_scale=__lowercase       ,   num_inference_steps=50       ,   output_type="np"       ,   width=512       ,   height=512       ,   sld_guidance_scale=2_000       ,   sld_warmup_steps=7       ,   sld_threshold=0.0_2_5       ,   sld_momentum_scale=0.5       ,   sld_mom_beta=0.7       ,   )

								A_				 =						output.images
								A_				 =						image[0, -3:, -3:, -1]
								A_				 =						np.array([0.5_8_1_8, 0.6_2_8_5, 0.6_8_3_5, 0.6_0_1_9, 0.6_2_5, 0.6_7_5_4, 0.6_0_9_6, 0.6_3_3_4, 0.6_5_6_1]	)
								assert image.shape == (1, 512, 512, 3)

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






"""simple docstring"""





import torch
def 							A_     ():

					'''simple docstring'''





					if torch.cuda.is_available():
										A_				 =						torch.cuda.device_count()
					else:
										A_				 =						0
					print(f'Successfully ran on {num_gpus} GPUs'				)


if __name__ == "__main__":
		main()




