






'''simple docstring'''




from __future__ import annotations

from typing import Any







class       __lowercase					:

	def __init__(      self   :						Any  ,					UpperCAmelCase_   :						int  ,					UpperCAmelCase_   :						int  ,					UpperCAmelCase_   :						float = 0):
			UpperCamelCase__,	UpperCamelCase__				:					Tuple					=					row, column
			UpperCamelCase__				:					Any					=					[[default_value for c in range(UpperCAmelCase_)] for r in range(UpperCAmelCase_)]

	def __str__(      self   :						Union[str, Any]):
			UpperCamelCase__				:					Any					=					F'Matrix consist of {self.row} rows and {self.column} columns\n'

			# Make string identifier
			UpperCamelCase__				:					str					=					0
			for row_vector in self.array:
					for obj in row_vector:
							UpperCamelCase__				:					Any					=					max(UpperCAmelCase_  ,					len(str(UpperCAmelCase_)))
			UpperCamelCase__				:					List[str]					=					F'%{max_element_length}s'

			# Make string and return
			def single_line(UpperCAmelCase_   :						list[float]) -> str:
					nonlocal string_format_identifier
					UpperCamelCase__				:					List[str]					=					'['
					line += ", ".join(string_format_identifier % (obj,) for obj in row_vector)
					line += "]"
					return line

			s += "\n".join(single_line(UpperCAmelCase_) for row_vector in self.array)
			return s

	def __repr__(      self   :						Any):
			return str(self)

	def      __UpperCamelCase							(      self   :						int  ,					UpperCAmelCase_   :						tuple[int, int]):
			if not (isinstance(UpperCAmelCase_  ,					(list, tuple)) and len(UpperCAmelCase_) == 2):
					return False
			elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
					return False
			else:
					return True

	def __getitem__(      self   :						Dict  ,					UpperCAmelCase_   :						tuple[int, int]):
			assert self.validate_indicies(UpperCAmelCase_)
			return self.array[loc[0]][loc[1]]

	def __setitem__(      self   :						Tuple  ,					UpperCAmelCase_   :						tuple[int, int]  ,					UpperCAmelCase_   :						float):
			assert self.validate_indicies(UpperCAmelCase_)
			UpperCamelCase__				:					str					=					value

	def __add__(      self   :						Optional[Any]  ,					UpperCAmelCase_   :						Matrix):
			assert isinstance(UpperCAmelCase_  ,					UpperCAmelCase_)
			assert self.row == another.row and self.column == another.column

			# Add
			UpperCamelCase__				:					List[Any]					=					Matrix(self.row  ,					self.column)
			for r in range(self.row):
					for c in range(self.column):
							UpperCamelCase__				:					Optional[Any]					=					self[r, c] + another[r, c]
			return result

	def __neg__(      self   :						str):
			UpperCamelCase__				:					List[Any]					=					Matrix(self.row  ,					self.column)
			for r in range(self.row):
					for c in range(self.column):
							UpperCamelCase__				:					Optional[int]					=					-self[r, c]
			return result

	def __sub__(      self   :						Union[str, Any]  ,					UpperCAmelCase_   :						Matrix):
			return self + (-another)

	def __mul__(      self   :						Optional[int]  ,					UpperCAmelCase_   :						int | float | Matrix):

			if isinstance(UpperCAmelCase_  ,					(int, float)):  # Scalar multiplication
					UpperCamelCase__				:					str					=					Matrix(self.row  ,					self.column)
					for r in range(self.row):
							for c in range(self.column):
									UpperCamelCase__				:					int					=					self[r, c] * another
					return result
			elif isinstance(UpperCAmelCase_  ,					UpperCAmelCase_):  # Matrix multiplication
					assert self.column == another.row
					UpperCamelCase__				:					Tuple					=					Matrix(self.row  ,					another.column)
					for r in range(self.row):
							for c in range(another.column):
									for i in range(self.column):
											result[r, c] += self[r, i] * another[i, c]
					return result
			else:
					UpperCamelCase__				:					Optional[Any]					=					F'Unsupported type given for another ({type(UpperCAmelCase_)})'
					raise TypeError(UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Any):
			UpperCamelCase__				:					List[Any]					=					Matrix(self.column  ,					self.row)
			for r in range(self.row):
					for c in range(self.column):
							UpperCamelCase__				:					List[str]					=					self[r, c]
			return result





	def      __UpperCamelCase							(      self   :						List[Any]  ,					UpperCAmelCase_   :						Matrix  ,					UpperCAmelCase_   :						Matrix):
			assert isinstance(UpperCAmelCase_  ,					UpperCAmelCase_) and isinstance(UpperCAmelCase_  ,					UpperCAmelCase_)
			assert self.row == self.column == u.row == v.row  # u, v should be column vector
			assert u.column == v.column == 1  # u, v should be column vector

			# Calculate
			UpperCamelCase__				:					Dict					=					v.transpose()
			UpperCamelCase__				:					Optional[int]					=					(v_t * self * u)[0, 0] + 1
			if numerator_factor == 0:
					return None  # It's not invertable
			return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":







		def       __UpperCAmelCase						(  )							->     None:
				# a^(-1)
				UpperCamelCase__				:					Optional[Any]					=					Matrix(3     ,      3     ,      0)
				for i in range(3):
						UpperCamelCase__				:					int					=					1
				print(f'a^(-1) is {ainv}')
				# u, v
				UpperCamelCase__				:					Dict					=					Matrix(3     ,      1     ,      0)
				UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					Union[str, Any]					=					1, 2, -3
				UpperCamelCase__				:					Optional[int]					=					Matrix(3     ,      1     ,      0)
				UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					Tuple					=					4, -2, 5
				print(f'u is {u}')
				print(f'v is {v}')
				print(f'uv^T is {u * v.transpose()}')
				# Sherman Morrison
				print(f'(a + uv^T)^(-1) is {ainv.sherman_morrison(lowerCamelCase_     ,      lowerCamelCase_)}')







		def       __UpperCAmelCase						(  )							->     None:
				import doctest

				doctest.testmod()


		testa()












'''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       __lowercase					(unittest.TestCase    ):

	def      __UpperCamelCase							(      self   :						List[str]):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()

	@property
	def      __UpperCamelCase							(      self   :						List[Any]):
			UpperCamelCase__				:					Union[str, Any]					=					1
			UpperCamelCase__				:					Union[str, Any]					=					3
			UpperCamelCase__				:					Dict					=					(32, 32)

			UpperCamelCase__				:					int					=					floats_tensor((batch_size, num_channels) + sizes  ,					rng=random.Random(0)).to(UpperCAmelCase_)
			return image

	@property
	def      __UpperCamelCase							(      self   :						Any):
			torch.manual_seed(0)
			UpperCamelCase__				:					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      __UpperCamelCase							(      self   :						Any):
			torch.manual_seed(0)
			UpperCamelCase__				:					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      __UpperCamelCase							(      self   :						str):
			torch.manual_seed(0)
			UpperCamelCase__				:					Tuple					=					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(UpperCAmelCase_)

	@property
	def      __UpperCamelCase							(      self   :						Optional[Any]):
			def extract(*UpperCAmelCase_   :						List[Any]  ,					**UpperCAmelCase_   :						Dict):







					class       __lowercase					:

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





						def      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						int):
								self.pixel_values.to(UpperCAmelCase_)
								return self


					return Out()

			return extract

	def      __UpperCamelCase							(      self   :						str):
			UpperCamelCase__				:					Optional[Any]					=					'cpu'  # ensure determinism for the device-dependent torch.Generator
			UpperCamelCase__				:					Any					=					self.dummy_cond_unet
			UpperCamelCase__				:					Any					=					DDIMScheduler(
			    beta_start=0.0_00_85  ,					beta_end=0.0_12  ,					beta_schedule='scaled_linear'  ,					clip_sample=UpperCAmelCase_  ,					set_alpha_to_one=UpperCAmelCase_  ,					)

			UpperCamelCase__				:					List[str]					=					self.dummy_vae
			UpperCamelCase__				:					str					=					self.dummy_text_encoder
			UpperCamelCase__				:					Tuple					=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

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

			UpperCamelCase__				:					Optional[Any]					=					'A painting of a squirrel eating a burger'

			UpperCamelCase__				:					Dict					=					torch.Generator(device=UpperCAmelCase_).manual_seed(0)
			UpperCamelCase__				:					List[Any]					=					sd_pipe([prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=6.0  ,					num_inference_steps=2  ,					output_type='np')
			UpperCamelCase__				:					Tuple					=					output.images

			UpperCamelCase__				:					List[Any]					=					torch.Generator(device=UpperCAmelCase_).manual_seed(0)
			UpperCamelCase__				:					Tuple					=					sd_pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=6.0  ,					num_inference_steps=2  ,					output_type='np'  ,					return_dict=UpperCAmelCase_  ,					)[0]

			UpperCamelCase__				:					List[str]					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					Optional[int]					=					image_from_tuple[0, -3:, -3:, -1]

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

			# make sure here that pndm scheduler skips prk
			UpperCamelCase__				:					Dict					=					StableDiffusionPipeline(
			    unet=UpperCAmelCase_  ,					scheduler=UpperCAmelCase_  ,					vae=UpperCAmelCase_  ,					text_encoder=UpperCAmelCase_  ,					tokenizer=UpperCAmelCase_  ,					safety_checker=UpperCAmelCase_  ,					feature_extractor=self.dummy_extractor  ,					)
			UpperCamelCase__				:					Tuple					=					sd_pipe.to(UpperCAmelCase_)
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_)

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

			UpperCamelCase__				:					List[str]					=					output.images

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

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

			assert image.shape == (1, 64, 64, 3)
			UpperCamelCase__				:					List[Any]					=					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   :						Dict):
			UpperCamelCase__				:					Dict					=					StableDiffusionPipeline.from_pretrained(
			    'hf-internal-testing/tiny-stable-diffusion-lms-pipe'  ,					safety_checker=UpperCAmelCase_)
			assert isinstance(UpperCAmelCase_  ,					UpperCAmelCase_)
			assert isinstance(pipe.scheduler  ,					UpperCAmelCase_)
			assert pipe.safety_checker is None

			UpperCamelCase__				:					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(UpperCAmelCase_)
					UpperCamelCase__				:					List[str]					=					StableDiffusionPipeline.from_pretrained(UpperCAmelCase_)

			# sanity check that the pipeline still works
			assert pipe.safety_checker is None
			UpperCamelCase__				:					Optional[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      __UpperCamelCase							(      self   :						List[Any]):
			UpperCamelCase__				:					Dict					=					self.dummy_cond_unet
			UpperCamelCase__				:					str					=					PNDMScheduler(skip_prk_steps=UpperCAmelCase_)
			UpperCamelCase__				:					Any					=					self.dummy_vae
			UpperCamelCase__				:					Optional[Any]					=					self.dummy_text_encoder
			UpperCamelCase__				:					str					=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

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

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

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

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







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

	def      __UpperCamelCase							(      self   :						Optional[int]):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()

	def      __UpperCamelCase							(      self   :						List[Any]):
			UpperCamelCase__				:					Optional[int]					=					StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'  ,					safety_checker=UpperCAmelCase_)
			UpperCamelCase__				:					Union[str, Any]					=					LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
			UpperCamelCase__				:					Optional[Any]					=					sd_pipe.to(UpperCAmelCase_)
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					List[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 '
			)
			UpperCamelCase__				:					Any					=					4_003_660_346
			UpperCamelCase__				:					Any					=					7

			# without safety guidance (sld_guidance_scale = 0)
			UpperCamelCase__				:					int					=					torch.manual_seed(UpperCAmelCase_)
			UpperCamelCase__				:					Optional[int]					=					sd_pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=UpperCAmelCase_  ,					num_inference_steps=50  ,					output_type='np'  ,					width=512  ,					height=512  ,					sld_guidance_scale=0  ,					)

			UpperCamelCase__				:					str					=					output.images
			UpperCamelCase__				:					Union[str, Any]					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					Tuple					=					[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, 512, 512, 3)

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

			# without safety guidance (strong configuration)
			UpperCamelCase__				:					Tuple					=					torch.manual_seed(UpperCAmelCase_)
			UpperCamelCase__				:					str					=					sd_pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=UpperCAmelCase_  ,					num_inference_steps=50  ,					output_type='np'  ,					width=512  ,					height=512  ,					sld_guidance_scale=2_000  ,					sld_warmup_steps=7  ,					sld_threshold=0.0_25  ,					sld_momentum_scale=0.5  ,					sld_mom_beta=0.7  ,					)

			UpperCamelCase__				:					Dict					=					output.images
			UpperCamelCase__				:					str					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					Tuple					=					[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, 512, 512, 3)

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

	def      __UpperCamelCase							(      self   :						Optional[Any]):
			UpperCamelCase__				:					Dict					=					StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'  ,					safety_checker=UpperCAmelCase_)
			UpperCamelCase__				:					str					=					LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
			UpperCamelCase__				:					Dict					=					sd_pipe.to(UpperCAmelCase_)
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					str					=					'padme amidala taking a bath artwork, safe for work, no nudity'
			UpperCamelCase__				:					Tuple					=					2_734_971_755
			UpperCamelCase__				:					Tuple					=					7

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

			UpperCamelCase__				:					int					=					output.images
			UpperCamelCase__				:					Union[str, Any]					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					Any					=					[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, 512, 512, 3)

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

			UpperCamelCase__				:					List[str]					=					torch.manual_seed(UpperCAmelCase_)
			UpperCamelCase__				:					Union[str, Any]					=					sd_pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=UpperCAmelCase_  ,					num_inference_steps=50  ,					output_type='np'  ,					width=512  ,					height=512  ,					sld_guidance_scale=2_000  ,					sld_warmup_steps=7  ,					sld_threshold=0.0_25  ,					sld_momentum_scale=0.5  ,					sld_mom_beta=0.7  ,					)

			UpperCamelCase__				:					Tuple					=					output.images
			UpperCamelCase__				:					List[str]					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					Union[str, Any]					=					[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, 512, 512, 3)

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





	def      __UpperCamelCase							(      self   :						Any):
			UpperCamelCase__				:					Optional[Any]					=					StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5')
			UpperCamelCase__				:					Optional[Any]					=					sd_pipe.to(UpperCAmelCase_)
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					int					=					(
			    'the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'
			    ' leyendecker'
			)
			UpperCamelCase__				:					Any					=					1_044_355_234
			UpperCamelCase__				:					Optional[int]					=					12

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

			UpperCamelCase__				:					List[str]					=					output.images
			UpperCamelCase__				:					Any					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					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

			UpperCamelCase__				:					int					=					torch.manual_seed(UpperCAmelCase_)
			UpperCamelCase__				:					List[str]					=					sd_pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=UpperCAmelCase_  ,					num_inference_steps=50  ,					output_type='np'  ,					width=512  ,					height=512  ,					sld_guidance_scale=2_000  ,					sld_warmup_steps=7  ,					sld_threshold=0.0_25  ,					sld_momentum_scale=0.5  ,					sld_mom_beta=0.7  ,					)

			UpperCamelCase__				:					Optional[Any]					=					output.images
			UpperCamelCase__				:					List[Any]					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					str					=					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, 512, 512, 3)

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




