
'''simple docstring'''



import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


__A        =     logging.get_logger(__name__)  # pylint: disable=invalid-name





class       A				(							_SCREAMING_SNAKE_CASE					):



			def __init__(       self							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__							,  )      ->		Any:




									'''simple docstring'''



									super().__init__()

									if safety_checker is None:
															logger.warning(
															    F'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
															    """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
															    """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
															    """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
															    """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
															    """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""							)

									self.register_modules(
									    speech_model=_lowerCAmelCase							,  speech_processor=_lowerCAmelCase							,  vae=_lowerCAmelCase							,  text_encoder=_lowerCAmelCase							,  tokenizer=_lowerCAmelCase							,  unet=_lowerCAmelCase							,  scheduler=_lowerCAmelCase							,  feature_extractor=_lowerCAmelCase							,  )



			def A__			(       self							,  lowerCamelCase__ = "auto"							)      ->		int:




									'''simple docstring'''



									if slice_size == "auto":
															lowercase__							=							self.unet.config.attention_head_dim // 2
									self.unet.set_attention_slice(_lowerCAmelCase							)



			def A__			(       self							)      ->		List[str]:




									'''simple docstring'''



									self.enable_attention_slicing(_lowerCAmelCase							)



			@torch.no_grad()
			def __call__(       self							,  lowerCamelCase__							,  lowerCamelCase__=16_000							,  lowerCamelCase__ = 512							,  lowerCamelCase__ = 512							,  lowerCamelCase__ = 50							,  lowerCamelCase__ = 7.5							,  lowerCamelCase__ = None							,  lowerCamelCase__ = 1							,  lowerCamelCase__ = 0.0							,  lowerCamelCase__ = None							,  lowerCamelCase__ = None							,  lowerCamelCase__ = "pil"							,  lowerCamelCase__ = True							,  lowerCamelCase__ = None							,  lowerCamelCase__ = 1							,  **lowerCamelCase__							,  )      ->		str:




									'''simple docstring'''



									lowercase__							=							self.speech_processor.feature_extractor(
									    _lowerCAmelCase							,  return_tensors="""pt"""							,  sampling_rate=_lowerCAmelCase							).input_features.to(self.device							)
									lowercase__							=							self.speech_model.generate(_lowerCAmelCase							,  max_length=480_000							)

									lowercase__							=							self.speech_processor.tokenizer.batch_decode(_lowerCAmelCase							,  skip_special_tokens=_lowerCAmelCase							,  normalize=_lowerCAmelCase							)[
									    0
									]

									if isinstance(_lowerCAmelCase							,  _lowerCAmelCase							):
															lowercase__							=							1
									elif isinstance(_lowerCAmelCase							,  _lowerCAmelCase							):
															lowercase__							=							len(_lowerCAmelCase							)
									else:
															raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(_lowerCAmelCase							)}'''							)

									if height % 8 != 0 or width % 8 != 0:
															raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''							)

									if (callback_steps is None) or (
									    callback_steps is not None and (not isinstance(_lowerCAmelCase							,  _lowerCAmelCase							) or callback_steps <= 0)
									):
															raise ValueError(
															    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
															    F''' {type(_lowerCAmelCase							)}.'''							)

									# get prompt text embeddings
									lowercase__							=							self.tokenizer(
									    _lowerCAmelCase							,  padding="""max_length"""							,  max_length=self.tokenizer.model_max_length							,  return_tensors="""pt"""							,  )
									lowercase__							=							text_inputs.input_ids

									if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
															lowercase__							=							self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]							)
															logger.warning(
															    """The following part of your input was truncated because CLIP can only handle sequences up to"""
															    F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''							)
															lowercase__							=							text_input_ids[:, : self.tokenizer.model_max_length]
									lowercase__							=							self.text_encoder(text_input_ids.to(self.device							)							)[0]

									# duplicate text embeddings for each generation per prompt, using mps friendly method
									lowercase__			,    lowercase__			,    lowercase__							=							text_embeddings.shape
									lowercase__							=							text_embeddings.repeat(1							,  _lowerCAmelCase							,  1							)
									lowercase__							=							text_embeddings.view(bs_embed * num_images_per_prompt							,  _lowerCAmelCase							,  -1							)

									# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
									# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
									# corresponds to doing no classifier free guidance.
									lowercase__							=							guidance_scale > 1.0
									# get unconditional embeddings for classifier free guidance
									if do_classifier_free_guidance:
															lowercase__							=							42
															if negative_prompt is None:
																					lowercase__							=							[""""""] * batch_size
															elif type(_lowerCAmelCase							) is not type(_lowerCAmelCase							):
																					raise TypeError(
																					    F'''`negative_prompt` should be the same type to `prompt`, but got {type(_lowerCAmelCase							)} !='''
																					    F''' {type(_lowerCAmelCase							)}.'''							)
															elif isinstance(_lowerCAmelCase							,  _lowerCAmelCase							):
																					lowercase__							=							[negative_prompt]
															elif batch_size != len(_lowerCAmelCase							):
																					raise ValueError(
																					    F'''`negative_prompt`: {negative_prompt} has batch size {len(_lowerCAmelCase							)}, but `prompt`:'''
																					    F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
																					    """ the batch size of `prompt`."""							)
															else:
																					lowercase__							=							negative_prompt

															lowercase__							=							text_input_ids.shape[-1]
															lowercase__							=							self.tokenizer(
															    _lowerCAmelCase							,  padding="""max_length"""							,  max_length=_lowerCAmelCase							,  truncation=_lowerCAmelCase							,  return_tensors="""pt"""							,  )
															lowercase__							=							self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]

															# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
															lowercase__							=							uncond_embeddings.shape[1]
															lowercase__							=							uncond_embeddings.repeat(1							,  _lowerCAmelCase							,  1							)
															lowercase__							=							uncond_embeddings.view(batch_size * num_images_per_prompt							,  _lowerCAmelCase							,  -1							)

															# For classifier free guidance, we need to do two forward passes.
															# Here we concatenate the unconditional and text embeddings into a single batch
															# to avoid doing two forward passes
															lowercase__							=							torch.cat([uncond_embeddings, text_embeddings]							)

									# get the initial random noise unless the user supplied it

									# Unlike in other pipelines, latents need to be generated in the target device
									# for 1-to-1 results reproducibility with the CompVis implementation.
									# However this currently doesn't work in `mps`.
									lowercase__							=							(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
									lowercase__							=							text_embeddings.dtype
									if latents is None:
															if self.device.type == "mps":
																					# randn does not exist on mps
																					lowercase__							=							torch.randn(_lowerCAmelCase							,  generator=_lowerCAmelCase							,  device="""cpu"""							,  dtype=_lowerCAmelCase							).to(
																					    self.device							)
															else:
																					lowercase__							=							torch.randn(_lowerCAmelCase							,  generator=_lowerCAmelCase							,  device=self.device							,  dtype=_lowerCAmelCase							)
									else:
															if latents.shape != latents_shape:
																					raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''							)
															lowercase__							=							latents.to(self.device							)

									# set timesteps
									self.scheduler.set_timesteps(_lowerCAmelCase							)

									# Some schedulers like PNDM have timesteps as arrays
									# It's more optimized to move all timesteps to correct device beforehand
									lowercase__							=							self.scheduler.timesteps.to(self.device							)

									# scale the initial noise by the standard deviation required by the scheduler
									lowercase__							=							latents * self.scheduler.init_noise_sigma

									# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
									# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
									# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
									# and should be between [0, 1]
									lowercase__							=							"""eta""" in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
									lowercase__							=							{}
									if accepts_eta:
															lowercase__							=							eta

									for i, t in enumerate(self.progress_bar(_lowerCAmelCase							)							):
															# expand the latents if we are doing classifier free guidance
															lowercase__							=							torch.cat([latents] * 2							) if do_classifier_free_guidance else latents
															lowercase__							=							self.scheduler.scale_model_input(_lowerCAmelCase							,  _lowerCAmelCase							)

															# predict the noise residual
															lowercase__							=							self.unet(_lowerCAmelCase							,  _lowerCAmelCase							,  encoder_hidden_states=_lowerCAmelCase							).sample

															# perform guidance
															if do_classifier_free_guidance:
																					lowercase__			,    lowercase__							=							noise_pred.chunk(2							)
																					lowercase__							=							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

															# compute the previous noisy sample x_t -> x_t-1
															lowercase__							=							self.scheduler.step(_lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase							,  **_lowerCAmelCase							).prev_sample

															# call the callback, if provided
															if callback is not None and i % callback_steps == 0:
																					callback(_lowerCAmelCase							,  _lowerCAmelCase							,  _lowerCAmelCase							)

									lowercase__							=							1 / 0.1_82_15 * latents
									lowercase__							=							self.vae.decode(_lowerCAmelCase							).sample

									lowercase__							=							(image / 2 + 0.5).clamp(0							,  1							)

									# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
									lowercase__							=							image.cpu().permute(0							,  2							,  3							,  1							).float().numpy()

									if output_type == "pil":
															lowercase__							=							self.numpy_to_pil(_lowerCAmelCase							)

									if not return_dict:
															return image

									return StableDiffusionPipelineOutput(images=_lowerCAmelCase							,  nsfw_content_detected=_lowerCAmelCase							)












import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()





class 			lowerCamelCase_				( unittest.TestCase   ):


 '''simple docstring'''







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



 @property
 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     1
       SCREAMING_SNAKE_CASE_      		=     3
       SCREAMING_SNAKE_CASE_      		=     (32, 32)

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



 @property
 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     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  lowerCAmelCase_    (				self    :						Tuple		):
       torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     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  lowerCAmelCase_    (				self    :						Optional[int]		):
       torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     RobertaSeriesConfig(
           hidden_size=32							,							project_dim=32							,							intermediate_size=37							,							layer_norm_eps=1E-05							,							num_attention_heads=4							,							num_hidden_layers=5							,							pad_token_id=1							,							vocab_size=5_006							,							)
       return RobertaSeriesModelWithTransformation(_lowerCAmelCase		)



 @property
 def  lowerCAmelCase_    (				self    :						List[Any]		):
       def extract(*_lowerCAmelCase    :						Optional[int]							,							**_lowerCAmelCase    :						str		):





             class 			lowerCamelCase_				:


              '''simple docstring'''







              def __init__(				self    :						str		):
                    SCREAMING_SNAKE_CASE_      		=     torch.ones([0]		)




              def  lowerCAmelCase_    (				self    :						Union[str, Any]							,							_lowerCAmelCase    :						int		):
                    self.pixel_values.to(_lowerCAmelCase		)
                    return self


             return Out()

       return extract



 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       SCREAMING_SNAKE_CASE_      		=     'cpu'  # ensure determinism for the device-dependent torch.Generator
       SCREAMING_SNAKE_CASE_      		=     self.dummy_cond_unet
       SCREAMING_SNAKE_CASE_      		=     PNDMScheduler(skip_prk_steps=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     self.dummy_vae
       SCREAMING_SNAKE_CASE_      		=     self.dummy_text_encoder
       SCREAMING_SNAKE_CASE_      		=     XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'		)
       SCREAMING_SNAKE_CASE_      		=     77

       SCREAMING_SNAKE_CASE_      		=     self.dummy_image.to(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     init_image / 2 + 0.5

       # make sure here that pndm scheduler skips prk
       SCREAMING_SNAKE_CASE_      		=     AltDiffusionImgaImgPipeline(
           unet=_lowerCAmelCase							,							scheduler=_lowerCAmelCase							,							vae=_lowerCAmelCase							,							text_encoder=_lowerCAmelCase							,							tokenizer=_lowerCAmelCase							,							safety_checker=_lowerCAmelCase							,							feature_extractor=self.dummy_extractor							,							)
       SCREAMING_SNAKE_CASE_      		=     VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor							,							do_normalize=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     alt_pipe.to(_lowerCAmelCase		)
       alt_pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     'A painting of a squirrel eating a burger'
       SCREAMING_SNAKE_CASE_      		=     torch.Generator(device=_lowerCAmelCase		).manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     alt_pipe(
           [prompt]							,							generator=_lowerCAmelCase							,							guidance_scale=6.0							,							num_inference_steps=2							,							output_type='np'							,							image=_lowerCAmelCase							,							)

       SCREAMING_SNAKE_CASE_      		=     output.images

       SCREAMING_SNAKE_CASE_      		=     torch.Generator(device=_lowerCAmelCase		).manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     alt_pipe(
           [prompt]							,							generator=_lowerCAmelCase							,							guidance_scale=6.0							,							num_inference_steps=2							,							output_type='np'							,							image=_lowerCAmelCase							,							return_dict=_lowerCAmelCase							,							)[0]

       SCREAMING_SNAKE_CASE_      		=     image[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE_      		=     image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       SCREAMING_SNAKE_CASE_      		=     np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]		)

       assert np.abs(image_slice.flatten() - expected_slice		).max() < 5E-3
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 5E-3



 @unittest.skipIf(torch_device != 'cuda'							,							'This test requires a GPU'		)
 def  lowerCAmelCase_    (				self    :						Tuple		):
       SCREAMING_SNAKE_CASE_      		=     self.dummy_cond_unet
       SCREAMING_SNAKE_CASE_      		=     PNDMScheduler(skip_prk_steps=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     self.dummy_vae
       SCREAMING_SNAKE_CASE_      		=     self.dummy_text_encoder
       SCREAMING_SNAKE_CASE_      		=     XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'		)
       SCREAMING_SNAKE_CASE_      		=     77

       SCREAMING_SNAKE_CASE_      		=     self.dummy_image.to(_lowerCAmelCase		)

       # put models in fp16
       SCREAMING_SNAKE_CASE_      		=     unet.half()
       SCREAMING_SNAKE_CASE_      		=     vae.half()
       SCREAMING_SNAKE_CASE_      		=     bert.half()

       # make sure here that pndm scheduler skips prk
       SCREAMING_SNAKE_CASE_      		=     AltDiffusionImgaImgPipeline(
           unet=_lowerCAmelCase							,							scheduler=_lowerCAmelCase							,							vae=_lowerCAmelCase							,							text_encoder=_lowerCAmelCase							,							tokenizer=_lowerCAmelCase							,							safety_checker=_lowerCAmelCase							,							feature_extractor=self.dummy_extractor							,							)
       SCREAMING_SNAKE_CASE_      		=     VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor							,							do_normalize=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     alt_pipe.to(_lowerCAmelCase		)
       alt_pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     'A painting of a squirrel eating a burger'
       SCREAMING_SNAKE_CASE_      		=     torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     alt_pipe(
           [prompt]							,							generator=_lowerCAmelCase							,							num_inference_steps=2							,							output_type='np'							,							image=_lowerCAmelCase							,							).images

       assert image.shape == (1, 32, 32, 3)




 @unittest.skipIf(torch_device != 'cuda'							,							'This test requires a GPU'		)
 def  lowerCAmelCase_    (				self    :						str		):
       SCREAMING_SNAKE_CASE_      		=     load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/img2img/sketch-mountains-input.jpg'		)
       # resize to resolution that is divisible by 8 but not 16 or 32
       SCREAMING_SNAKE_CASE_      		=     init_image.resize((760, 504)		)

       SCREAMING_SNAKE_CASE_      		=     'BAAI/AltDiffusion'
       SCREAMING_SNAKE_CASE_      		=     AltDiffusionImgaImgPipeline.from_pretrained(
           _lowerCAmelCase							,							safety_checker=_lowerCAmelCase							,							)
       pipe.to(_lowerCAmelCase		)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase		)
       pipe.enable_attention_slicing()

       SCREAMING_SNAKE_CASE_      		=     'A fantasy landscape, trending on artstation'

       SCREAMING_SNAKE_CASE_      		=     torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     pipe(
           prompt=_lowerCAmelCase							,							image=_lowerCAmelCase							,							strength=0.75							,							guidance_scale=7.5							,							generator=_lowerCAmelCase							,							output_type='np'							,							)
       SCREAMING_SNAKE_CASE_      		=     output.images[0]

       SCREAMING_SNAKE_CASE_      		=     image[255:258, 383:386, -1]

       assert image.shape == (504, 760, 3)
       SCREAMING_SNAKE_CASE_      		=     np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]		)

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





@slow
@require_torch_gpu
class 			lowerCamelCase_				( unittest.TestCase   ):


 '''simple docstring'''







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




 def  lowerCAmelCase_    (				self    :						int		):
       SCREAMING_SNAKE_CASE_      		=     load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
           '/img2img/sketch-mountains-input.jpg'		)
       SCREAMING_SNAKE_CASE_      		=     init_image.resize((768, 512)		)
       SCREAMING_SNAKE_CASE_      		=     load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'		)

       SCREAMING_SNAKE_CASE_      		=     'BAAI/AltDiffusion'
       SCREAMING_SNAKE_CASE_      		=     AltDiffusionImgaImgPipeline.from_pretrained(
           _lowerCAmelCase							,							safety_checker=_lowerCAmelCase							,							)
       pipe.to(_lowerCAmelCase		)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase		)
       pipe.enable_attention_slicing()

       SCREAMING_SNAKE_CASE_      		=     'A fantasy landscape, trending on artstation'

       SCREAMING_SNAKE_CASE_      		=     torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     pipe(
           prompt=_lowerCAmelCase							,							image=_lowerCAmelCase							,							strength=0.75							,							guidance_scale=7.5							,							generator=_lowerCAmelCase							,							output_type='np'							,							)
       SCREAMING_SNAKE_CASE_      		=     output.images[0]

       assert image.shape == (512, 768, 3)
       # img2img is flaky across GPUs even in fp32, so using MAE here
       assert np.abs(expected_image - image		).max() < 1E-2