

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       ):


					'''simple docstring'''






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






					@property
					def 						__A    (				self    ):
						A__							:					Optional[Any]       =	1
						A__							:					List[Any]       =	3
						A__							:					Optional[Any]       =	(32, 32)

						A__							:					str       =	floats_tensor((batch_size, num_channels) + sizes					, rng=random.Random(0    )    ).to(A__    )
						return image






					@property
					def 						__A    (				self    ):
						torch.manual_seed(0    )
						A__							:					Tuple       =	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    ):
						torch.manual_seed(0    )
						A__							:					List[str]       =	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    ):
						torch.manual_seed(0    )
						A__							:					Dict       =	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=1000					, )
						return CLIPTextModel(A__    )






					@property
					def 						__A    (				self    ):
						def extract(*A__					, **A__    ):



							class 					_a		:


												'''simple docstring'''






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






												def 						__A    (				self					, A__    ):
													self.pixel_values.to(A__    )
													return self



							return Out()

						return extract






					def 						__A    (				self    ):
						A__							:					Tuple       =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
						A__							:					Union[str, Any]       =	self.dummy_cond_unet
						A__							:					List[Any]       =	DDIMScheduler(
						    beta_start=0.0_0_0_8_5					, beta_end=0.0_1_2					, beta_schedule="""scaled_linear"""					, clip_sample=A__					, set_alpha_to_one=A__					, )

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

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

						A__							:					Optional[int]       =	"""A painting of a squirrel eating a burger"""

						A__							:					Tuple       =	torch.Generator(device=A__    ).manual_seed(0    )
						A__							:					Optional[int]       =	sd_pipe([prompt]					, generator=A__					, guidance_scale=6.0					, num_inference_steps=2					, output_type="""np"""    )
						A__							:					List[Any]       =	output.images

						A__							:					List[str]       =	torch.Generator(device=A__    ).manual_seed(0    )
						A__							:					Optional[Any]       =	sd_pipe(
						    [prompt]					, generator=A__					, guidance_scale=6.0					, num_inference_steps=2					, output_type="""np"""					, return_dict=A__					, )[0]

						A__							:					Optional[Any]       =	image[0, -3:, -3:, -1]
						A__							:					Tuple       =	image_from_tuple[0, -3:, -3:, -1]

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

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

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

						A__							:					Union[str, Any]       =	output.images

						A__							:					int       =	torch.Generator(device=A__    ).manual_seed(0    )
						A__							:					Dict       =	sd_pipe(
						    [prompt]					, generator=A__					, guidance_scale=6.0					, num_inference_steps=2					, output_type="""np"""					, return_dict=A__					, )[0]

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

						assert image.shape == (1, 64, 64, 3)
						A__							:					str       =	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 						__A    (				self    ):
						A__							:					Optional[Any]       =	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__							:					Dict       =	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__							:					Dict       =	StableDiffusionPipeline.from_pretrained(A__    )

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

						# put models in fp16
						A__							:					Optional[Any]       =	unet.half()
						A__							:					int       =	vae.half()
						A__							:					Optional[Any]       =	bert.half()

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

						A__							:					Optional[Any]       =	"""A painting of a squirrel eating a burger"""
						A__							:					str       =	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       ):


					'''simple docstring'''






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






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

						A__							:					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 """
						)
						A__							:					Optional[int]       =	40_0366_0346
						A__							:					Optional[Any]       =	7

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

						A__							:					Tuple       =	output.images
						A__							:					List[Any]       =	image[0, -3:, -3:, -1]
						A__							:					Tuple       =	[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__							:					Union[str, Any]       =	torch.manual_seed(A__    )
						A__							:					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=0.0_2_5					, sld_momentum_scale=0.5					, sld_mom_beta=0.7					, )

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

						A__							:					Dict       =	"""padme amidala taking a bath artwork, safe for work, no nudity"""
						A__							:					List[str]       =	27_3497_1755
						A__							:					Optional[Any]       =	7

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

						A__							:					Union[str, Any]       =	output.images
						A__							:					str       =	image[0, -3:, -3:, -1]
						A__							:					Dict       =	[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__							:					str       =	torch.manual_seed(A__    )
						A__							:					List[str]       =	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=0.0_2_5					, sld_momentum_scale=0.5					, sld_mom_beta=0.7					, )

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

						A__							:					str       =	(
						    """the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
						    """ leyendecker"""
						)
						A__							:					Optional[int]       =	10_4435_5234
						A__							:					Optional[Any]       =	12

						A__							:					Optional[int]       =	torch.manual_seed(A__    )
						A__							:					Dict       =	sd_pipe(
						    [prompt]					, generator=A__					, guidance_scale=A__					, num_inference_steps=50					, output_type="""np"""					, width=512					, height=512					, sld_guidance_scale=0					, )

						A__							:					List[Any]       =	output.images
						A__							:					Tuple       =	image[0, -3:, -3:, -1]
						A__							:					str       =	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__							:					Union[str, Any]       =	torch.manual_seed(A__    )
						A__							:					List[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=0.0_2_5					, sld_momentum_scale=0.5					, sld_mom_beta=0.7					, )

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






def 		UpperCamelCase      (lowercase_:				int      )  ->   int:
	if not isinstance(lowercase_					,   lowercase_      ):
		raise TypeError("""Input value must be an 'int' type"""      )

	A__							:					int       =	0
	while number:
		position += 1
		number >>= 1

	return position


if __name__ == "__main__":
	import doctest

	doctest.testmod()



