





"""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 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




				def        __lowercase	(  self	:int      ):
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()




				@property
				def        __lowercase	(  self	:Any      ):
											__lowerCamelCase   :   Union[str, Any]       =1
											__lowerCamelCase   :   int       =3
											__lowerCamelCase   :   Dict       =(32, 32)

											__lowerCamelCase   :   Dict       =floats_tensor((batch_size, num_channels) + sizes    ,     rng=random.Random(0      )      ).to(__A      )
											return image




				@property
				def        __lowercase	(  self	:str      ):
											torch.manual_seed(0      )
											__lowerCamelCase   :   Optional[Any]       =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        __lowercase	(  self	:List[Any]      ):
											torch.manual_seed(0      )
											__lowerCamelCase   :   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    ,     )
											return model




				@property
				def        __lowercase	(  self	:Union[str, Any]      ):
											torch.manual_seed(0      )
											__lowerCamelCase   :   Tuple       =CLIPTextConfig(
											    bos_token_id=0    ,     eos_token_id=2    ,     hidden_size=32    ,     intermediate_size=37    ,     layer_norm_eps=1e-0_5    ,     num_attention_heads=4    ,     num_hidden_layers=5    ,     pad_token_id=1    ,     vocab_size=1000    ,     )
											return CLIPTextModel(__A      )




				@property
				def        __lowercase	(  self	:Any      ):



											def extract(*__lowercase	:Tuple    ,     **__lowercase	:str      ):
																		class 		SCREAMING_SNAKE_CASE_				:
																						"""simple docstring"""




																						def __init__(  self	:Union[str, Any]      ):
																													__lowerCamelCase   :   int       =torch.ones([0]      )




																						def        __lowercase	(  self	:Dict    ,     __lowercase	:Tuple      ):
																													self.pixel_values.to(__A      )
																													return self

																		return Out()

											return extract




				def        __lowercase	(  self	:Optional[int]      ):
											__lowerCamelCase   :   str       ='''cpu'''  # ensure determinism for the device-dependent torch.Generator
											__lowerCamelCase   :   Any       =self.dummy_cond_unet
											__lowerCamelCase   :   List[Any]       =DDIMScheduler(
											    beta_start=0.00085    ,     beta_end=0.012    ,     beta_schedule='''scaled_linear'''    ,     clip_sample=__A    ,     set_alpha_to_one=__A    ,     )

											__lowerCamelCase   :   Dict       =self.dummy_vae
											__lowerCamelCase   :   Any       =self.dummy_text_encoder
											__lowerCamelCase   :   Union[str, Any]       =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

											# make sure here that pndm scheduler skips prk
											__lowerCamelCase   :   Dict       =StableDiffusionPipeline(
											    unet=__A    ,     scheduler=__A    ,     vae=__A    ,     text_encoder=__A    ,     tokenizer=__A    ,     safety_checker=__A    ,     feature_extractor=self.dummy_extractor    ,     )
											__lowerCamelCase   :   Optional[int]       =sd_pipe.to(__A      )
											sd_pipe.set_progress_bar_config(disable=__A      )

											__lowerCamelCase   :   List[str]       ='''A painting of a squirrel eating a burger'''

											__lowerCamelCase   :   int       =torch.Generator(device=__A      ).manual_seed(0      )
											__lowerCamelCase   :   str       =sd_pipe([prompt]    ,     generator=__A    ,     guidance_scale=6.0    ,     num_inference_steps=2    ,     output_type='''np'''      )
											__lowerCamelCase   :   str       =output.images

											__lowerCamelCase   :   Any       =torch.Generator(device=__A      ).manual_seed(0      )
											__lowerCamelCase   :   str       =sd_pipe(
											    [prompt]    ,     generator=__A    ,     guidance_scale=6.0    ,     num_inference_steps=2    ,     output_type='''np'''    ,     return_dict=__A    ,     )[0]

											__lowerCamelCase   :   List[Any]       =image[0, -3:, -3:, -1]
											__lowerCamelCase   :   Union[str, Any]       =image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)
											__lowerCamelCase   :   List[str]       =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        __lowercase	(  self	:int      ):
											__lowerCamelCase   :   Dict       ='''cpu'''  # ensure determinism for the device-dependent torch.Generator
											__lowerCamelCase   :   Optional[int]       =self.dummy_cond_unet
											__lowerCamelCase   :   List[Any]       =PNDMScheduler(skip_prk_steps=__A      )
											__lowerCamelCase   :   Union[str, Any]       =self.dummy_vae
											__lowerCamelCase   :   Any       =self.dummy_text_encoder
											__lowerCamelCase   :   Any       =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

											# make sure here that pndm scheduler skips prk
											__lowerCamelCase   :   int       =StableDiffusionPipeline(
											    unet=__A    ,     scheduler=__A    ,     vae=__A    ,     text_encoder=__A    ,     tokenizer=__A    ,     safety_checker=__A    ,     feature_extractor=self.dummy_extractor    ,     )
											__lowerCamelCase   :   List[str]       =sd_pipe.to(__A      )
											sd_pipe.set_progress_bar_config(disable=__A      )

											__lowerCamelCase   :   List[Any]       ='''A painting of a squirrel eating a burger'''
											__lowerCamelCase   :   Tuple       =torch.Generator(device=__A      ).manual_seed(0      )
											__lowerCamelCase   :   List[str]       =sd_pipe([prompt]    ,     generator=__A    ,     guidance_scale=6.0    ,     num_inference_steps=2    ,     output_type='''np'''      )

											__lowerCamelCase   :   int       =output.images

											__lowerCamelCase   :   Optional[int]       =torch.Generator(device=__A      ).manual_seed(0      )
											__lowerCamelCase   :   Any       =sd_pipe(
											    [prompt]    ,     generator=__A    ,     guidance_scale=6.0    ,     num_inference_steps=2    ,     output_type='''np'''    ,     return_dict=__A    ,     )[0]

											__lowerCamelCase   :   List[Any]       =image[0, -3:, -3:, -1]
											__lowerCamelCase   :   Optional[Any]       =image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)
											__lowerCamelCase   :   str       =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        __lowercase	(  self	:Tuple      ):
											__lowerCamelCase   :   Tuple       =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

											__lowerCamelCase   :   List[Any]       =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      )
																		__lowerCamelCase   :   Dict       =StableDiffusionPipeline.from_pretrained(__A      )

											# sanity check that the pipeline still works
											assert pipe.safety_checker is None
											__lowerCamelCase   :   Any       =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        __lowercase	(  self	:Any      ):
											__lowerCamelCase   :   Optional[int]       =self.dummy_cond_unet
											__lowerCamelCase   :   Union[str, Any]       =PNDMScheduler(skip_prk_steps=__A      )
											__lowerCamelCase   :   Union[str, Any]       =self.dummy_vae
											__lowerCamelCase   :   Optional[int]       =self.dummy_text_encoder
											__lowerCamelCase   :   Any       =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

											# put models in fp16
											__lowerCamelCase   :   List[Any]       =unet.half()
											__lowerCamelCase   :   int       =vae.half()
											__lowerCamelCase   :   Tuple       =bert.half()

											# make sure here that pndm scheduler skips prk
											__lowerCamelCase   :   Tuple       =StableDiffusionPipeline(
											    unet=__A    ,     scheduler=__A    ,     vae=__A    ,     text_encoder=__A    ,     tokenizer=__A    ,     safety_checker=__A    ,     feature_extractor=self.dummy_extractor    ,     )
											__lowerCamelCase   :   Optional[int]       =sd_pipe.to(__A      )
											sd_pipe.set_progress_bar_config(disable=__A      )

											__lowerCamelCase   :   Optional[Any]       ='''A painting of a squirrel eating a burger'''
											__lowerCamelCase   :   Union[str, Any]       =sd_pipe([prompt]    ,     num_inference_steps=2    ,     output_type='''np'''      ).images

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

@nightly
@require_torch_gpu
class 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




				def        __lowercase	(  self	:int      ):
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()




				def        __lowercase	(  self	:Dict      ):
											__lowerCamelCase   :   str       =StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''    ,     safety_checker=__A      )
											__lowerCamelCase   :   Optional[int]       =LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config      )
											__lowerCamelCase   :   List[str]       =sd_pipe.to(__A      )
											sd_pipe.set_progress_bar_config(disable=__A      )

											__lowerCamelCase   :   Optional[Any]       =(
											    '''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   :   Optional[int]       =40_0366_0346
											__lowerCamelCase   :   List[Any]       =7

											# without safety guidance (sld_guidance_scale = 0)
											__lowerCamelCase   :   List[Any]       =torch.manual_seed(__A      )
											__lowerCamelCase   :   List[Any]       =sd_pipe(
											    [prompt]    ,     generator=__A    ,     guidance_scale=__A    ,     num_inference_steps=50    ,     output_type='''np'''    ,     width=512    ,     height=512    ,     sld_guidance_scale=0    ,     )

											__lowerCamelCase   :   int       =output.images
											__lowerCamelCase   :   List[Any]       =image[0, -3:, -3:, -1]
											__lowerCamelCase   :   Tuple       =[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)
											__lowerCamelCase   :   Optional[int]       =torch.manual_seed(__A      )
											__lowerCamelCase   :   Optional[Any]       =sd_pipe(
											    [prompt]    ,     generator=__A    ,     guidance_scale=__A    ,     num_inference_steps=50    ,     output_type='''np'''    ,     width=512    ,     height=512    ,     sld_guidance_scale=2000    ,     sld_warmup_steps=7    ,     sld_threshold=0.025    ,     sld_momentum_scale=0.5    ,     sld_mom_beta=0.7    ,     )

											__lowerCamelCase   :   str       =output.images
											__lowerCamelCase   :   Dict       =image[0, -3:, -3:, -1]
											__lowerCamelCase   :   List[str]       =[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        __lowercase	(  self	:Any      ):
											__lowerCamelCase   :   List[str]       =StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''    ,     safety_checker=__A      )
											__lowerCamelCase   :   Optional[int]       =LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config      )
											__lowerCamelCase   :   Dict       =sd_pipe.to(__A      )
											sd_pipe.set_progress_bar_config(disable=__A      )

											__lowerCamelCase   :   List[Any]       ='''padme amidala taking a bath artwork, safe for work, no nudity'''
											__lowerCamelCase   :   Optional[Any]       =27_3497_1755
											__lowerCamelCase   :   Optional[int]       =7

											__lowerCamelCase   :   List[str]       =torch.manual_seed(__A      )
											__lowerCamelCase   :   Union[str, Any]       =sd_pipe(
											    [prompt]    ,     generator=__A    ,     guidance_scale=__A    ,     num_inference_steps=50    ,     output_type='''np'''    ,     width=512    ,     height=512    ,     sld_guidance_scale=0    ,     )

											__lowerCamelCase   :   str       =output.images
											__lowerCamelCase   :   Optional[Any]       =image[0, -3:, -3:, -1]
											__lowerCamelCase   :   Dict       =[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

											__lowerCamelCase   :   Dict       =torch.manual_seed(__A      )
											__lowerCamelCase   :   Optional[Any]       =sd_pipe(
											    [prompt]    ,     generator=__A    ,     guidance_scale=__A    ,     num_inference_steps=50    ,     output_type='''np'''    ,     width=512    ,     height=512    ,     sld_guidance_scale=2000    ,     sld_warmup_steps=7    ,     sld_threshold=0.025    ,     sld_momentum_scale=0.5    ,     sld_mom_beta=0.7    ,     )

											__lowerCamelCase   :   Optional[int]       =output.images
											__lowerCamelCase   :   Union[str, Any]       =image[0, -3:, -3:, -1]
											__lowerCamelCase   :   Any       =[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        __lowercase	(  self	:Dict      ):
											__lowerCamelCase   :   Optional[Any]       =StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''      )
											__lowerCamelCase   :   int       =sd_pipe.to(__A      )
											sd_pipe.set_progress_bar_config(disable=__A      )

											__lowerCamelCase   :   int       =(
											    '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
											    ''' leyendecker'''
											)
											__lowerCamelCase   :   Dict       =10_4435_5234
											__lowerCamelCase   :   Any       =12

											__lowerCamelCase   :   Tuple       =torch.manual_seed(__A      )
											__lowerCamelCase   :   Optional[int]       =sd_pipe(
											    [prompt]    ,     generator=__A    ,     guidance_scale=__A    ,     num_inference_steps=50    ,     output_type='''np'''    ,     width=512    ,     height=512    ,     sld_guidance_scale=0    ,     )

											__lowerCamelCase   :   Optional[int]       =output.images
											__lowerCamelCase   :   Optional[int]       =image[0, -3:, -3:, -1]
											__lowerCamelCase   :   Dict       =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

											__lowerCamelCase   :   List[str]       =torch.manual_seed(__A      )
											__lowerCamelCase   :   Any       =sd_pipe(
											    [prompt]    ,     generator=__A    ,     guidance_scale=__A    ,     num_inference_steps=50    ,     output_type='''np'''    ,     width=512    ,     height=512    ,     sld_guidance_scale=2000    ,     sld_warmup_steps=7    ,     sld_threshold=0.025    ,     sld_momentum_scale=0.5    ,     sld_mom_beta=0.7    ,     )

											__lowerCamelCase   :   int       =output.images
											__lowerCamelCase   :   Optional[int]       =image[0, -3:, -3:, -1]
											__lowerCamelCase   :   Tuple       =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











def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				float       , UpperCamelCase__:				float     ):
		if mass < 0:
				raise ValueError("""The mass of a body cannot be negative"""     )
		return 0.5 * mass * abs(UpperCamelCase__     ) * abs(UpperCamelCase__     )


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)