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






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

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






	@property
	def 				UpperCamelCase_  (       self					: Dict ):
					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 				UpperCamelCase_  (       self					: Any ):
					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 				UpperCamelCase_  (       self					: List[str] ):
					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=10_00							,)
					return CLIPTextModel(A )






	@property
	def 				UpperCamelCase_  (       self					: Tuple ):
					def extract(*A					: Any							,**A					: Optional[int] ):





									class 							UpperCAmelCase      :




										'''simple docstring'''





										def __init__(       self					: List[Any] ):
														__A   			= torch.ones([0] )







										def 				UpperCamelCase_  (       self					: List[Any]							,A					: Optional[int] ):
														self.pixel_values.to(A )
														return self





									return Out()

					return extract






	def 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= "cpu"  # ensure determinism for the device-dependent torch.Generator
					__A   			= self.dummy_cond_unet
					__A   			= DDIMScheduler(
					    beta_start=0.0_00_85							,beta_end=0.0_12							,beta_schedule="scaled_linear"							,clip_sample=A							,set_alpha_to_one=A							,)

					__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=A							,scheduler=A							,vae=A							,text_encoder=A							,tokenizer=A							,safety_checker=A							,feature_extractor=self.dummy_extractor							,)
					__A   			= sd_pipe.to(A )
					sd_pipe.set_progress_bar_config(disable=A )

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

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

					__A   			= torch.Generator(device=A ).manual_seed(0 )
					__A   			= sd_pipe(
					    [prompt]							,generator=A							,guidance_scale=6.0							,num_inference_steps=2							,output_type="np"							,return_dict=A							,)[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([0.57_56, 0.61_18, 0.50_05, 0.50_41, 0.54_71, 0.47_26, 0.49_76, 0.48_65, 0.48_64] )

					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					: Any ):
					__A   			= "cpu"  # ensure determinism for the device-dependent torch.Generator
					__A   			= self.dummy_cond_unet
					__A   			= PNDMScheduler(skip_prk_steps=A )
					__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=A							,scheduler=A							,vae=A							,text_encoder=A							,tokenizer=A							,safety_checker=A							,feature_extractor=self.dummy_extractor							,)
					__A   			= sd_pipe.to(A )
					sd_pipe.set_progress_bar_config(disable=A )

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

					__A   			= output.images

					__A   			= torch.Generator(device=A ).manual_seed(0 )
					__A   			= sd_pipe(
					    [prompt]							,generator=A							,guidance_scale=6.0							,num_inference_steps=2							,output_type="np"							,return_dict=A							,)[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([0.51_25, 0.57_16, 0.48_28, 0.50_60, 0.56_50, 0.47_68, 0.51_85, 0.48_95, 0.49_93] )

					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					: Optional[int] ):
					__A   			= 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

					__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(A )
									__A   			= StableDiffusionPipeline.from_pretrained(A )

					# 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 				UpperCamelCase_  (       self					: Dict ):
					__A   			= self.dummy_cond_unet
					__A   			= PNDMScheduler(skip_prk_steps=A )
					__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=A							,scheduler=A							,vae=A							,text_encoder=A							,tokenizer=A							,safety_checker=A							,feature_extractor=self.dummy_extractor							,)
					__A   			= sd_pipe.to(A )
					sd_pipe.set_progress_bar_config(disable=A )

					__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 							UpperCAmelCase      (  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					: Dict ):
					__A   			= StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"							,safety_checker=A )
					__A   			= LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
					__A   			= sd_pipe.to(A )
					sd_pipe.set_progress_bar_config(disable=A )

					__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   			= 40_03_66_03_46
					__A   			= 7

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

					__A   			= output.images
					__A   			= image[0, -3:, -3:, -1]
					__A   			= [0.22_78, 0.22_31, 0.22_49, 0.23_33, 0.23_03, 0.18_85, 0.22_73, 0.21_44, 0.21_76]

					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)
					__A   			= torch.manual_seed(A )
					__A   			= sd_pipe(
					    [prompt]							,generator=A							,guidance_scale=A							,num_inference_steps=50							,output_type="np"							,width=5_12							,height=5_12							,sld_guidance_scale=20_00							,sld_warmup_steps=7							,sld_threshold=0.0_25							,sld_momentum_scale=0.5							,sld_mom_beta=0.7							,)

					__A   			= output.images
					__A   			= image[0, -3:, -3:, -1]
					__A   			= [0.23_83, 0.22_76, 0.2_36, 0.21_92, 0.21_86, 0.20_53, 0.19_71, 0.19_01, 0.17_19]

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

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






	def 				UpperCamelCase_  (       self					: List[str] ):
					__A   			= StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"							,safety_checker=A )
					__A   			= LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
					__A   			= sd_pipe.to(A )
					sd_pipe.set_progress_bar_config(disable=A )

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

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

					__A   			= output.images
					__A   			= image[0, -3:, -3:, -1]
					__A   			= [0.35_02, 0.36_22, 0.33_96, 0.36_42, 0.34_78, 0.33_18, 0.35, 0.33_48, 0.32_97]

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

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

					__A   			= torch.manual_seed(A )
					__A   			= sd_pipe(
					    [prompt]							,generator=A							,guidance_scale=A							,num_inference_steps=50							,output_type="np"							,width=5_12							,height=5_12							,sld_guidance_scale=20_00							,sld_warmup_steps=7							,sld_threshold=0.0_25							,sld_momentum_scale=0.5							,sld_mom_beta=0.7							,)

					__A   			= output.images
					__A   			= image[0, -3:, -3:, -1]
					__A   			= [0.55_31, 0.52_06, 0.48_95, 0.51_56, 0.51_82, 0.47_51, 0.48_02, 0.48_03, 0.44_43]

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

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







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

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

					__A   			= torch.manual_seed(A )
					__A   			= sd_pipe(
					    [prompt]							,generator=A							,guidance_scale=A							,num_inference_steps=50							,output_type="np"							,width=5_12							,height=5_12							,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, 5_12, 5_12, 3)

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

					__A   			= torch.manual_seed(A )
					__A   			= sd_pipe(
					    [prompt]							,generator=A							,guidance_scale=A							,num_inference_steps=50							,output_type="np"							,width=5_12							,height=5_12							,sld_guidance_scale=20_00							,sld_warmup_steps=7							,sld_threshold=0.0_25							,sld_momentum_scale=0.5							,sld_mom_beta=0.7							,)

					__A   			= output.images
					__A   			= image[0, -3:, -3:, -1]
					__A   			= np.array([0.58_18, 0.62_85, 0.68_35, 0.60_19, 0.6_25, 0.67_54, 0.60_96, 0.63_34, 0.65_61] )
					assert image.shape == (1, 5_12, 5_12, 3)

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


def       A       (					lowercase    ,       lowercase    ,       lowercase    ,       lowercase    ,       lowercase )   ->  int:
		'''simple docstring'''

		if index == number_of_items:
				return 0
		UpperCamelCase		=     0
		UpperCamelCase		=     0
		UpperCamelCase		=     knapsack(lowercase    ,       lowercase    ,       lowercase    ,       lowercase    ,       index + 1 )
		if weights[index] <= max_weight:
				UpperCamelCase		=     values[index] + knapsack(
				    lowercase    ,       lowercase    ,       lowercase    ,       max_weight - weights[index]    ,       index + 1 )
		return max(lowercase    ,       lowercase )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
