




"""simple docstring"""





from __future__ import annotations

from typing import Any

def 		lowerCAmelCase				(			*lowerCamelCase		):
						'''simple docstring'''


						if all(_check_not_integer(lowerCamelCase		) for m in matrix_s		):
												for i in matrix_s[1:]:
																		_verify_matrix_sizes(matrix_s[0]   ,      lowerCamelCase		)
												return [[sum(lowerCamelCase		) for t in zip(*lowerCamelCase		)] for m in zip(*lowerCamelCase		)]
						raise TypeError('''Expected a matrix, got int/list instead'''		)

def 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase		):
						'''simple docstring'''


						if (
						    _check_not_integer(lowerCamelCase		)
						    and _check_not_integer(lowerCamelCase		)
						    and _verify_matrix_sizes(lowerCamelCase   ,      lowerCamelCase		)
						):
												return [[i - j for i, j in zip(*lowerCamelCase		)] for m in zip(lowerCamelCase   ,      lowerCamelCase		)]
						raise TypeError('''Expected a matrix, got int/list instead'''		)

def 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase		):
						'''simple docstring'''


						return [[x * n for x in row] for row in matrix]

def 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase		):
						'''simple docstring'''


						if _check_not_integer(lowerCamelCase		) and _check_not_integer(lowerCamelCase		):
												snake_case__      ,						snake_case__							:       Optional[int]				      =       _verify_matrix_sizes(lowerCamelCase   ,      lowerCamelCase		)

						if cols[0] != rows[1]:
												snake_case__							:       str				      =       (
												    '''Cannot multiply matrix of dimensions '''
												    f'''({rows[0]},{cols[0]}) and ({rows[1]},{cols[1]})'''
												)
												raise ValueError(lowerCamelCase		)
						return [
						    [sum(m * n for m, n in zip(lowerCamelCase   ,      lowerCamelCase		)		) for j in zip(*lowerCamelCase		)] for i in matrix_a
						]

def 		lowerCAmelCase				(			lowerCamelCase		):
						'''simple docstring'''


						snake_case__							:       List[str]				      =       int(lowerCamelCase		)
						return [[int(row == column		) for column in range(lowerCamelCase		)] for row in range(lowerCamelCase		)]

def 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase = True		):
						'''simple docstring'''


						if _check_not_integer(lowerCamelCase		):
												if return_map:
																		return map(lowerCamelCase   ,      zip(*lowerCamelCase		)		)
												else:
																		return list(map(lowerCamelCase   ,      zip(*lowerCamelCase		)		)		)
						raise TypeError('''Expected a matrix, got int/list instead'''		)

def 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase   ,      lowerCamelCase		):
						'''simple docstring'''


						snake_case__							:       Tuple				      =       matrix[:row] + matrix[row + 1 :]
						return [row[:column] + row[column + 1 :] for row in minor]

def 		lowerCAmelCase				(			lowerCamelCase		):
						'''simple docstring'''


						if len(lowerCamelCase		) == 1:
												return matrix[0][0]

						return sum(
						    x * determinant(minor(lowerCamelCase   ,      0   ,      lowerCamelCase		)		) * (-1) ** i
						    for i, x in enumerate(matrix[0]		)		)

def 		lowerCAmelCase				(			lowerCamelCase		):
						'''simple docstring'''


						snake_case__							:       Optional[Any]				      =       determinant(lowerCamelCase		)
						if det == 0:
												return None

						snake_case__							:       Union[str, Any]				      =       [
						    [determinant(minor(lowerCamelCase   ,      lowerCamelCase   ,      lowerCamelCase		)		) for j in range(len(lowerCamelCase		)		)]
						    for i in range(len(lowerCamelCase		)		)
						]

						snake_case__							:       Optional[Any]				      =       [
						    [x * (-1) ** (row + col) for col, x in enumerate(matrix_minor[row]		)]
						    for row in range(len(lowerCamelCase		)		)
						]
						snake_case__							:       Union[str, Any]				      =       list(transpose(lowerCamelCase		)		)
						return scalar_multiply(lowerCamelCase   ,      1 / det		)

def 		lowerCAmelCase				(			lowerCamelCase		):
						'''simple docstring'''


						return not isinstance(lowerCamelCase   ,      lowerCamelCase		) and not isinstance(matrix[0]   ,      lowerCamelCase		)

def 		lowerCAmelCase				(			lowerCamelCase		):
						'''simple docstring'''


						return len(lowerCamelCase		), len(matrix[0]		)

def 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase		):
						'''simple docstring'''


						snake_case__							:       str				      =       _shape(lowerCamelCase		) + _shape(lowerCamelCase		)
						if shape[0] != shape[3] or shape[1] != shape[2]:
												snake_case__							:       str				      =       (
												    '''operands could not be broadcast together with shape '''
												    f'''({shape[0], shape[1]}), ({shape[2], shape[3]})'''
												)
												raise ValueError(lowerCamelCase		)
						return (shape[0], shape[2]), (shape[1], shape[3])

def 		lowerCAmelCase				(			):
						'''simple docstring'''


						snake_case__							:       str				      =       [[12, 10], [3, 9]]
						snake_case__							:       str				      =       [[3, 4], [7, 4]]
						snake_case__							:       List[str]				      =       [[11, 12, 13, 14], [21, 22, 23, 24], [31, 32, 33, 34], [41, 42, 43, 44]]
						snake_case__							:       Dict				      =       [[3, 0, 2], [2, 0, -2], [0, 1, 1]]
						print(f'''Add Operation, {add(lowerCamelCase   ,      lowerCamelCase		) = } \n'''		)
						print(
						    f'''Multiply Operation, {multiply(lowerCamelCase   ,      lowerCamelCase		) = } \n'''   ,      )
						print(f'''Identity: {identity(5		)}\n'''		)
						print(f'''Minor of {matrix_c} = {minor(lowerCamelCase   ,      1   ,      2		)} \n'''		)
						print(f'''Determinant of {matrix_b} = {determinant(lowerCamelCase		)} \n'''		)
						print(f'''Inverse of {matrix_d} = {inverse(lowerCamelCase		)}\n'''		)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()








"""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.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

class    __lowercase							(      unittest.TestCase						):


				'''simple docstring'''


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

				@property
				def    _lowerCamelCase       ( self :  List[Any]   ):
										snake_case__							:       int				      =       1
										snake_case__							:       Any				      =       3
										snake_case__							:       Any				      =       (3_2, 3_2)

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

				@property
				def    _lowerCamelCase       ( self :  Optional[Any]   ):
										torch.manual_seed(0   )
										snake_case__							:       int				      =       UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4)   ,layers_per_block=2   ,sample_size=3_2   ,in_channels=4   ,out_channels=4   ,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')   ,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')   ,cross_attention_dim=3_2   ,)
										return model

				@property
				def    _lowerCamelCase       ( self :  List[str]   ):
										torch.manual_seed(0   )
										snake_case__							:       int				      =       AutoencoderKL(
										    block_out_channels=[3_2, 6_4]   ,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 :  Optional[int]   ):
										torch.manual_seed(0   )
										snake_case__							:       str				      =       CLIPTextConfig(
										    bos_token_id=0   ,eos_token_id=2   ,hidden_size=3_2   ,intermediate_size=3_7   ,layer_norm_eps=1E-05   ,num_attention_heads=4   ,num_hidden_layers=5   ,pad_token_id=1   ,vocab_size=1_0_0_0   ,)
										return CLIPTextModel(SCREAMING_SNAKE_CASE_   )

				@property
				def    _lowerCamelCase       ( self :  Optional[int]   ):
										def extract(*SCREAMING_SNAKE_CASE_ :  Optional[int]   ,**SCREAMING_SNAKE_CASE_ :  Optional[Any]   ):

																class    __lowercase							:


																				'''simple docstring'''


																				def __init__( self :  Optional[int]   ):
																										snake_case__							:       List[str]				      =       torch.ones([0]   )





																				def    _lowerCamelCase       ( self :  Optional[int]   ,SCREAMING_SNAKE_CASE_ :  str   ):
																										self.pixel_values.to(SCREAMING_SNAKE_CASE_   )
																										return self







																return Out()

										return extract

				def    _lowerCamelCase       ( self :  Dict   ):
										snake_case__							:       Tuple				      =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										snake_case__							:       Union[str, Any]				      =       self.dummy_cond_unet
										snake_case__							:       List[Any]				      =       DDIMScheduler(
										    beta_start=0.00085   ,beta_end=0.012   ,beta_schedule='''scaled_linear'''   ,clip_sample=SCREAMING_SNAKE_CASE_   ,set_alpha_to_one=SCREAMING_SNAKE_CASE_   ,)

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

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

										snake_case__							:       Union[str, Any]				      =       '''A painting of a squirrel eating a burger'''

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

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

										snake_case__							:       str				      =       image[0, -3:, -3:, -1]
										snake_case__							:       str				      =       image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)
										snake_case__							:       int				      =       np.array([0.5753, 0.6114, 0.5001, 0.5034, 0.5470, 0.4729, 0.4971, 0.4867, 0.4867]   )

										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 :  Dict   ):
										snake_case__							:       str				      =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										snake_case__							:       Optional[Any]				      =       self.dummy_cond_unet
										snake_case__							:       Optional[Any]				      =       PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_   )
										snake_case__							:       Optional[Any]				      =       self.dummy_vae
										snake_case__							:       Any				      =       self.dummy_text_encoder
										snake_case__							:       str				      =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

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

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

										snake_case__							:       int				      =       output.images

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

										snake_case__							:       str				      =       image[0, -3:, -3:, -1]
										snake_case__							:       int				      =       image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)
										snake_case__							:       Optional[int]				      =       np.array([0.5122, 0.5712, 0.4825, 0.5053, 0.5646, 0.4769, 0.5179, 0.4894, 0.4994]   )

										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 :  List[str]   ):
										snake_case__							:       Optional[Any]				      =       StableDiffusionPipeline.from_pretrained(
										    '''hf-internal-testing/tiny-stable-diffusion-lms-pipe'''   ,safety_checker=SCREAMING_SNAKE_CASE_   )
										assert isinstance(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   )
										assert isinstance(pipe.scheduler   ,SCREAMING_SNAKE_CASE_   )
										assert pipe.safety_checker is None

										snake_case__							:       Optional[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(SCREAMING_SNAKE_CASE_   )
																snake_case__							:       int				      =       StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_   )

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

										# put models in fp16
										snake_case__							:       int				      =       unet.half()
										snake_case__							:       Any				      =       vae.half()
										snake_case__							:       int				      =       bert.half()

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

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

										assert image.shape == (1, 6_4, 6_4, 3)







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


				'''simple docstring'''


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

				def    _lowerCamelCase       ( self :  Union[str, Any]   ):
										snake_case__							:       Optional[Any]				      =       '''cuda'''
										snake_case__							:       str				      =       StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''   )
										snake_case__							:       Optional[int]				      =       pipe.to(SCREAMING_SNAKE_CASE_   )
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

										snake_case__							:       Union[str, Any]				      =       '''a photo of a cat'''
										snake_case__							:       int				      =       {
										    '''editing_prompt''': ['''sunglasses'''],
										    '''reverse_editing_direction''': [False],
										    '''edit_warmup_steps''': 1_0,
										    '''edit_guidance_scale''': 6,
										    '''edit_threshold''': 0.95,
										    '''edit_momentum_scale''': 0.5,
										    '''edit_mom_beta''': 0.6,
										}

										snake_case__							:       Optional[Any]				      =       3
										snake_case__							:       Any				      =       7

										# no sega enabled
										snake_case__							:       str				      =       torch.Generator(SCREAMING_SNAKE_CASE_   )
										generator.manual_seed(SCREAMING_SNAKE_CASE_   )
										snake_case__							:       Optional[Any]				      =       pipe(
										    [prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=SCREAMING_SNAKE_CASE_   ,num_inference_steps=5_0   ,output_type='''np'''   ,width=5_1_2   ,height=5_1_2   ,)

										snake_case__							:       Tuple				      =       output.images
										snake_case__							:       Optional[Any]				      =       image[0, -3:, -3:, -1]
										snake_case__							:       Union[str, Any]				      =       [
										    0.34673113,
										    0.38492733,
										    0.37597352,
										    0.34086335,
										    0.35650748,
										    0.35579205,
										    0.3384763,
										    0.34340236,
										    0.3573271,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

										# with sega enabled
										# generator = torch.manual_seed(seed)
										generator.manual_seed(SCREAMING_SNAKE_CASE_   )
										snake_case__							:       Tuple				      =       pipe(
										    [prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=SCREAMING_SNAKE_CASE_   ,num_inference_steps=5_0   ,output_type='''np'''   ,width=5_1_2   ,height=5_1_2   ,**SCREAMING_SNAKE_CASE_   ,)

										snake_case__							:       int				      =       output.images
										snake_case__							:       int				      =       image[0, -3:, -3:, -1]
										snake_case__							:       Union[str, Any]				      =       [
										    0.41887826,
										    0.37728766,
										    0.30138272,
										    0.41416335,
										    0.41664985,
										    0.36283392,
										    0.36191246,
										    0.43364465,
										    0.43001732,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

				def    _lowerCamelCase       ( self :  int   ):
										snake_case__							:       int				      =       '''cuda'''
										snake_case__							:       Optional[Any]				      =       StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''   )
										snake_case__							:       Optional[int]				      =       pipe.to(SCREAMING_SNAKE_CASE_   )
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

										snake_case__							:       int				      =       '''an image of a crowded boulevard, realistic, 4k'''
										snake_case__							:       Tuple				      =       {
										    '''editing_prompt''': '''crowd, crowded, people''',
										    '''reverse_editing_direction''': True,
										    '''edit_warmup_steps''': 1_0,
										    '''edit_guidance_scale''': 8.3,
										    '''edit_threshold''': 0.9,
										    '''edit_momentum_scale''': 0.5,
										    '''edit_mom_beta''': 0.6,
										}

										snake_case__							:       Optional[Any]				      =       9
										snake_case__							:       Union[str, Any]				      =       7

										# no sega enabled
										snake_case__							:       Any				      =       torch.Generator(SCREAMING_SNAKE_CASE_   )
										generator.manual_seed(SCREAMING_SNAKE_CASE_   )
										snake_case__							:       Optional[int]				      =       pipe(
										    [prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=SCREAMING_SNAKE_CASE_   ,num_inference_steps=5_0   ,output_type='''np'''   ,width=5_1_2   ,height=5_1_2   ,)

										snake_case__							:       int				      =       output.images
										snake_case__							:       Union[str, Any]				      =       image[0, -3:, -3:, -1]
										snake_case__							:       Union[str, Any]				      =       [
										    0.43497998,
										    0.91814065,
										    0.7540739,
										    0.55580205,
										    0.8467265,
										    0.5389691,
										    0.62574506,
										    0.58897763,
										    0.50926757,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

										# with sega enabled
										# generator = torch.manual_seed(seed)
										generator.manual_seed(SCREAMING_SNAKE_CASE_   )
										snake_case__							:       Union[str, Any]				      =       pipe(
										    [prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=SCREAMING_SNAKE_CASE_   ,num_inference_steps=5_0   ,output_type='''np'''   ,width=5_1_2   ,height=5_1_2   ,**SCREAMING_SNAKE_CASE_   ,)

										snake_case__							:       int				      =       output.images
										snake_case__							:       int				      =       image[0, -3:, -3:, -1]
										snake_case__							:       Any				      =       [
										    0.3089719,
										    0.30500144,
										    0.29016042,
										    0.30630964,
										    0.325687,
										    0.29419225,
										    0.2908091,
										    0.28723598,
										    0.27696294,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

				def    _lowerCamelCase       ( self :  Tuple   ):
										snake_case__							:       Optional[int]				      =       '''cuda'''
										snake_case__							:       Union[str, Any]				      =       StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''   )
										snake_case__							:       Tuple				      =       pipe.to(SCREAMING_SNAKE_CASE_   )
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

										snake_case__							:       List[str]				      =       '''a castle next to a river'''
										snake_case__							:       Dict				      =       {
										    '''editing_prompt''': ['''boat on a river, boat''', '''monet, impression, sunrise'''],
										    '''reverse_editing_direction''': False,
										    '''edit_warmup_steps''': [1_5, 1_8],
										    '''edit_guidance_scale''': 6,
										    '''edit_threshold''': [0.9, 0.8],
										    '''edit_momentum_scale''': 0.5,
										    '''edit_mom_beta''': 0.6,
										}

										snake_case__							:       Dict				      =       4_8
										snake_case__							:       int				      =       7

										# no sega enabled
										snake_case__							:       Optional[int]				      =       torch.Generator(SCREAMING_SNAKE_CASE_   )
										generator.manual_seed(SCREAMING_SNAKE_CASE_   )
										snake_case__							:       List[str]				      =       pipe(
										    [prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=SCREAMING_SNAKE_CASE_   ,num_inference_steps=5_0   ,output_type='''np'''   ,width=5_1_2   ,height=5_1_2   ,)

										snake_case__							:       str				      =       output.images
										snake_case__							:       int				      =       image[0, -3:, -3:, -1]
										snake_case__							:       List[str]				      =       [
										    0.75163555,
										    0.76037145,
										    0.61785,
										    0.9189673,
										    0.8627701,
										    0.85189694,
										    0.8512813,
										    0.87012076,
										    0.8312857,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

										# with sega enabled
										# generator = torch.manual_seed(seed)
										generator.manual_seed(SCREAMING_SNAKE_CASE_   )
										snake_case__							:       List[str]				      =       pipe(
										    [prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=SCREAMING_SNAKE_CASE_   ,num_inference_steps=5_0   ,output_type='''np'''   ,width=5_1_2   ,height=5_1_2   ,**SCREAMING_SNAKE_CASE_   ,)

										snake_case__							:       Union[str, Any]				      =       output.images
										snake_case__							:       Any				      =       image[0, -3:, -3:, -1]
										snake_case__							:       List[str]				      =       [
										    0.73553365,
										    0.7537271,
										    0.74341905,
										    0.66480356,
										    0.6472925,
										    0.63039416,
										    0.64812905,
										    0.6749717,
										    0.6517102,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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





				def    _lowerCamelCase       ( self :  str   ):
										snake_case__							:       Any				      =       '''cuda'''
										snake_case__							:       Any				      =       StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''   ,torch_dtype=torch.floataa   )
										snake_case__							:       Dict				      =       pipe.to(SCREAMING_SNAKE_CASE_   )
										pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_   )

										snake_case__							:       List[Any]				      =       '''a photo of a cat'''
										snake_case__							:       Dict				      =       {
										    '''editing_prompt''': ['''sunglasses'''],
										    '''reverse_editing_direction''': [False],
										    '''edit_warmup_steps''': 1_0,
										    '''edit_guidance_scale''': 6,
										    '''edit_threshold''': 0.95,
										    '''edit_momentum_scale''': 0.5,
										    '''edit_mom_beta''': 0.6,
										}

										snake_case__							:       Dict				      =       3
										snake_case__							:       Optional[int]				      =       7

										# no sega enabled
										snake_case__							:       Dict				      =       torch.Generator(SCREAMING_SNAKE_CASE_   )
										generator.manual_seed(SCREAMING_SNAKE_CASE_   )
										snake_case__							:       List[str]				      =       pipe(
										    [prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=SCREAMING_SNAKE_CASE_   ,num_inference_steps=5_0   ,output_type='''np'''   ,width=5_1_2   ,height=5_1_2   ,)

										snake_case__							:       int				      =       output.images
										snake_case__							:       int				      =       image[0, -3:, -3:, -1]
										snake_case__							:       List[str]				      =       [
										    0.34887695,
										    0.3876953,
										    0.375,
										    0.34423828,
										    0.3581543,
										    0.35717773,
										    0.3383789,
										    0.34570312,
										    0.359375,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

										# with sega enabled
										# generator = torch.manual_seed(seed)
										generator.manual_seed(SCREAMING_SNAKE_CASE_   )
										snake_case__							:       Tuple				      =       pipe(
										    [prompt]   ,generator=SCREAMING_SNAKE_CASE_   ,guidance_scale=SCREAMING_SNAKE_CASE_   ,num_inference_steps=5_0   ,output_type='''np'''   ,width=5_1_2   ,height=5_1_2   ,**SCREAMING_SNAKE_CASE_   ,)

										snake_case__							:       Union[str, Any]				      =       output.images
										snake_case__							:       Tuple				      =       image[0, -3:, -3:, -1]
										snake_case__							:       Tuple				      =       [
										    0.42285156,
										    0.36914062,
										    0.29077148,
										    0.42041016,
										    0.41918945,
										    0.35498047,
										    0.3618164,
										    0.4423828,
										    0.43115234,
										]

										assert image.shape == (1, 5_1_2, 5_1_2, 3)

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


