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


__UpperCamelCase	: Union[str, Any]    		=  logging.get_logger(__name__)  # pylint: disable=invalid-name





class       lowercase__		(     UpperCamelCase_):


						def __init__( self			:       Optional[int]					,	UpperCamelCase__			:       WhisperForConditionalGeneration					,	UpperCamelCase__			:       WhisperProcessor					,	UpperCamelCase__			:       AutoencoderKL					,	UpperCamelCase__			:       CLIPTextModel					,	UpperCamelCase__			:       CLIPTokenizer					,	UpperCamelCase__			:       UNetaDConditionModel					,	UpperCamelCase__			:       Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]					,	UpperCamelCase__			:       StableDiffusionSafetyChecker					,	UpperCamelCase__			:       CLIPImageProcessor					,	):





												'''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=UpperCamelCase__					,	speech_processor=UpperCamelCase__					,	vae=UpperCamelCase__					,	text_encoder=UpperCamelCase__					,	tokenizer=UpperCamelCase__					,	unet=UpperCamelCase__					,	scheduler=UpperCamelCase__					,	feature_extractor=UpperCamelCase__					,	)


						def  __A ( self			:       Any					,	UpperCamelCase__			:       Optional[Union[str, int]] = "auto"		):





												'''simple docstring'''





												if slice_size == "auto":
																		SCREAMING_SNAKE_CASE							:					Dict           =							self.unet.config.attention_head_dim // 2
												self.unet.set_attention_slice(UpperCamelCase__		)


						def  __A ( self			:       Dict		):





												'''simple docstring'''





												self.enable_attention_slicing(UpperCamelCase__		)



						@torch.no_grad()
						def __call__( self			:       List[str]					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Any=1_6000					,	UpperCamelCase__			:       int = 512					,	UpperCamelCase__			:       int = 512					,	UpperCamelCase__			:       int = 50					,	UpperCamelCase__			:       float = 7.5					,	UpperCamelCase__			:       Optional[Union[str, List[str]]] = None					,	UpperCamelCase__			:       Optional[int] = 1					,	UpperCamelCase__			:       float = 0.0					,	UpperCamelCase__			:       Optional[torch.Generator] = None					,	UpperCamelCase__			:       Optional[torch.FloatTensor] = None					,	UpperCamelCase__			:       Optional[str] = "pil"					,	UpperCamelCase__			:       bool = True					,	UpperCamelCase__			:       Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,	UpperCamelCase__			:       int = 1					,	**UpperCamelCase__			:       int					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							self.speech_processor.feature_extractor(
												    UpperCamelCase__					,	return_tensors='''pt'''					,	sampling_rate=UpperCamelCase__		).input_features.to(self.device		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.speech_model.generate(UpperCamelCase__					,	max_length=48_0000		)

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.speech_processor.tokenizer.batch_decode(UpperCamelCase__					,	skip_special_tokens=UpperCamelCase__					,	normalize=UpperCamelCase__		)[
												    0
												]

												if isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					int           =							1
												elif isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					int           =							len(UpperCamelCase__		)
												else:
																		raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase__		)}"""		)

												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(UpperCamelCase__					,	UpperCamelCase__		) or callback_steps <= 0)
												):
																		raise ValueError(
																		    f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
																		    f""" {type(UpperCamelCase__		)}."""		)

												# get prompt text embeddings
												SCREAMING_SNAKE_CASE							:					Any           =							self.tokenizer(
												    UpperCamelCase__					,	padding='''max_length'''					,	max_length=self.tokenizer.model_max_length					,	return_tensors='''pt'''					,	)
												SCREAMING_SNAKE_CASE							:					List[str]           =							text_inputs.input_ids

												if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							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}"""		)
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							text_input_ids[:, : self.tokenizer.model_max_length]
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							self.text_encoder(text_input_ids.to(self.device		)		)[0]

												# duplicate text embeddings for each generation per prompt, using mps friendly method
												SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Any           =							text_embeddings.shape
												SCREAMING_SNAKE_CASE							:					int           =							text_embeddings.repeat(1					,	UpperCamelCase__					,	1		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							text_embeddings.view(bs_embed * num_images_per_prompt					,	UpperCamelCase__					,	-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.
												SCREAMING_SNAKE_CASE							:					Tuple           =							guidance_scale > 1.0
												# get unconditional embeddings for classifier free guidance
												if do_classifier_free_guidance:
																		SCREAMING_SNAKE_CASE							:					List[str]
																		if negative_prompt is None:
																								SCREAMING_SNAKE_CASE							:					List[str]           =							[''''''] * batch_size
																		elif type(UpperCamelCase__		) is not type(UpperCamelCase__		):
																								raise TypeError(
																								    f"""`negative_prompt` should be the same type to `prompt`, but got {type(UpperCamelCase__		)} !="""
																								    f""" {type(UpperCamelCase__		)}."""		)
																		elif isinstance(UpperCamelCase__					,	UpperCamelCase__		):
																								SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[negative_prompt]
																		elif batch_size != len(UpperCamelCase__		):
																								raise ValueError(
																								    f"""`negative_prompt`: {negative_prompt} has batch size {len(UpperCamelCase__		)}, but `prompt`:"""
																								    f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
																								    ''' the batch size of `prompt`.'''		)
																		else:
																								SCREAMING_SNAKE_CASE							:					Dict           =							negative_prompt

																		SCREAMING_SNAKE_CASE							:					int           =							text_input_ids.shape[-1]
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.tokenizer(
																		    UpperCamelCase__					,	padding='''max_length'''					,	max_length=UpperCamelCase__					,	truncation=UpperCamelCase__					,	return_tensors='''pt'''					,	)
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]

																		# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							uncond_embeddings.shape[1]
																		SCREAMING_SNAKE_CASE							:					int           =							uncond_embeddings.repeat(1					,	UpperCamelCase__					,	1		)
																		SCREAMING_SNAKE_CASE							:					Dict           =							uncond_embeddings.view(batch_size * num_images_per_prompt					,	UpperCamelCase__					,	-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
																		SCREAMING_SNAKE_CASE							:					str           =							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`.
												SCREAMING_SNAKE_CASE							:					Dict           =							(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							text_embeddings.dtype
												if latents is None:
																		if self.device.type == "mps":
																								# randn does not exist on mps
																								SCREAMING_SNAKE_CASE							:					str           =							torch.randn(UpperCamelCase__					,	generator=UpperCamelCase__					,	device='''cpu'''					,	dtype=UpperCamelCase__		).to(
																								    self.device		)
																		else:
																								SCREAMING_SNAKE_CASE							:					List[str]           =							torch.randn(UpperCamelCase__					,	generator=UpperCamelCase__					,	device=self.device					,	dtype=UpperCamelCase__		)
												else:
																		if latents.shape != latents_shape:
																								raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""		)
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							latents.to(self.device		)

												# set timesteps
												self.scheduler.set_timesteps(UpperCamelCase__		)

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

												# scale the initial noise by the standard deviation required by the scheduler
												SCREAMING_SNAKE_CASE							:					Any           =							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]
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							'''eta''' in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							{}
												if accepts_eta:
																		SCREAMING_SNAKE_CASE							:					str           =							eta

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

																		# predict the noise residual
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							self.unet(UpperCamelCase__					,	UpperCamelCase__					,	encoder_hidden_states=UpperCamelCase__		).sample

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

																		# compute the previous noisy sample x_t -> x_t-1
																		SCREAMING_SNAKE_CASE							:					List[str]           =							self.scheduler.step(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	**UpperCamelCase__		).prev_sample

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

												SCREAMING_SNAKE_CASE							:					Dict           =							1 / 0.1_8215 * latents
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.vae.decode(UpperCamelCase__		).sample

												SCREAMING_SNAKE_CASE							:					List[str]           =							(image / 2 + 0.5).clamp(0					,	1		)

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

												if output_type == "pil":
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							self.numpy_to_pil(UpperCamelCase__		)

												if not return_dict:
																		return image

												return StableDiffusionPipelineOutput(images=UpperCamelCase__					,	nsfw_content_detected=UpperCamelCase__		)






import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin





@skip_mps
class       lowercase__		(     UpperCamelCase_			,							UpperCamelCase_			,							unittest.TestCase):
						UpperCamelCase_  =	IFPipeline
						UpperCamelCase_  =	TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
						UpperCamelCase_  =	TEXT_TO_IMAGE_BATCH_PARAMS
						UpperCamelCase_  =	PipelineTesterMixin.required_optional_params - {"""latents"""}


						def  __A ( self			:       Tuple		):





												'''simple docstring'''





												return self._get_dummy_components()


						def  __A ( self			:       int					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       int=0		):





												'''simple docstring'''





												if str(UpperCamelCase__		).startswith('''mps'''		):
																		SCREAMING_SNAKE_CASE							:					Tuple           =							torch.manual_seed(UpperCamelCase__		)
												else:
																		SCREAMING_SNAKE_CASE							:					Dict           =							torch.Generator(device=UpperCamelCase__		).manual_seed(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					int           =							{
												    '''prompt''': '''A painting of a squirrel eating a burger''',
												    '''generator''': generator,
												    '''num_inference_steps''': 2,
												    '''output_type''': '''numpy''',
												}

												return inputs


						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												self._test_save_load_optional_components()


						@unittest.skipIf(torch_device != '''cuda'''					,	reason='''float16 requires CUDA'''		)
						def  __A ( self			:       Any		):





												'''simple docstring'''





												super().test_save_load_floataa(expected_max_diff=1E-1		)


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												self._test_attention_slicing_forward_pass(expected_max_diff=1E-2		)


						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												self._test_save_load_local()


						def  __A ( self			:       List[str]		):





												'''simple docstring'''





												self._test_inference_batch_single_identical(
												    expected_max_diff=1E-2					,	)



						@unittest.skipIf(
						    torch_device != '''cuda''' or not is_xformers_available()					,	reason='''XFormers attention is only available with CUDA and `xformers` installed'''					,	)
						def  __A ( self			:       Tuple		):





												'''simple docstring'''





												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3		)






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


						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							IFPipeline.from_pretrained('''DeepFloyd/IF-I-XL-v1.0'''					,	variant='''fp16'''					,	torch_dtype=torch.floataa		)

												SCREAMING_SNAKE_CASE							:					str           =							IFSuperResolutionPipeline.from_pretrained(
												    '''DeepFloyd/IF-II-L-v1.0'''					,	variant='''fp16'''					,	torch_dtype=torch.floataa					,	text_encoder=UpperCamelCase__					,	tokenizer=UpperCamelCase__		)

												# pre compute text embeddings and remove T5 to save memory

												pipe_a.text_encoder.to('''cuda'''		)

												SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					Dict           =							pipe_a.encode_prompt('''anime turtle'''					,	device='''cuda'''		)

												del pipe_a.tokenizer
												del pipe_a.text_encoder
												gc.collect()

												SCREAMING_SNAKE_CASE							:					Tuple           =							None
												SCREAMING_SNAKE_CASE							:					str           =							None

												pipe_a.enable_model_cpu_offload()
												pipe_a.enable_model_cpu_offload()

												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)
												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)

												self._test_if(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)

												pipe_a.remove_all_hooks()
												pipe_a.remove_all_hooks()

												# img2img

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							IFImgaImgPipeline(**pipe_a.components		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							IFImgaImgSuperResolutionPipeline(**pipe_a.components		)

												pipe_a.enable_model_cpu_offload()
												pipe_a.enable_model_cpu_offload()

												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)
												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)

												self._test_if_imgaimg(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)

												pipe_a.remove_all_hooks()
												pipe_a.remove_all_hooks()

												# inpainting

												SCREAMING_SNAKE_CASE							:					Tuple           =							IFInpaintingPipeline(**pipe_a.components		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							IFInpaintingSuperResolutionPipeline(**pipe_a.components		)

												pipe_a.enable_model_cpu_offload()
												pipe_a.enable_model_cpu_offload()

												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)
												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()		)

												self._test_if_inpainting(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												_start_torch_memory_measurement()

												SCREAMING_SNAKE_CASE							:					List[str]           =							torch.Generator(device='''cpu'''		).manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							pipe_a(
												    prompt_embeds=UpperCamelCase__					,	negative_prompt_embeds=UpperCamelCase__					,	num_inference_steps=2					,	generator=UpperCamelCase__					,	output_type='''np'''					,	)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							output.images[0]

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

												SCREAMING_SNAKE_CASE							:					Tuple           =							torch.cuda.max_memory_allocated()
												assert mem_bytes < 13 * 10**9

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy'''		)
												assert_mean_pixel_difference(UpperCamelCase__					,	UpperCamelCase__		)

												# pipeline 2

												_start_torch_memory_measurement()

												SCREAMING_SNAKE_CASE							:					Tuple           =							torch.Generator(device='''cpu'''		).manual_seed(0		)

												SCREAMING_SNAKE_CASE							:					int           =							floats_tensor((1, 3, 64, 64)					,	rng=random.Random(0		)		).to(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Any           =							pipe_a(
												    prompt_embeds=UpperCamelCase__					,	negative_prompt_embeds=UpperCamelCase__					,	image=UpperCamelCase__					,	generator=UpperCamelCase__					,	num_inference_steps=2					,	output_type='''np'''					,	)

												SCREAMING_SNAKE_CASE							:					Tuple           =							output.images[0]

												assert image.shape == (256, 256, 3)

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							torch.cuda.max_memory_allocated()
												assert mem_bytes < 4 * 10**9

												SCREAMING_SNAKE_CASE							:					int           =							load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy'''		)
												assert_mean_pixel_difference(UpperCamelCase__					,	UpperCamelCase__		)


						def  __A ( self			:       Optional[int]					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       int					,	UpperCamelCase__			:       Optional[Any]					,	UpperCamelCase__			:       str		):





												'''simple docstring'''





												_start_torch_memory_measurement()

												SCREAMING_SNAKE_CASE							:					Tuple           =							floats_tensor((1, 3, 64, 64)					,	rng=random.Random(0		)		).to(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Any           =							torch.Generator(device='''cpu'''		).manual_seed(0		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							pipe_a(
												    prompt_embeds=UpperCamelCase__					,	negative_prompt_embeds=UpperCamelCase__					,	image=UpperCamelCase__					,	num_inference_steps=2					,	generator=UpperCamelCase__					,	output_type='''np'''					,	)

												SCREAMING_SNAKE_CASE							:					int           =							output.images[0]

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

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							torch.cuda.max_memory_allocated()
												assert mem_bytes < 10 * 10**9

												SCREAMING_SNAKE_CASE							:					Tuple           =							load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'''		)
												assert_mean_pixel_difference(UpperCamelCase__					,	UpperCamelCase__		)

												# pipeline 2

												_start_torch_memory_measurement()

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							torch.Generator(device='''cpu'''		).manual_seed(0		)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							floats_tensor((1, 3, 256, 256)					,	rng=random.Random(0		)		).to(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							floats_tensor((1, 3, 64, 64)					,	rng=random.Random(0		)		).to(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					str           =							pipe_a(
												    prompt_embeds=UpperCamelCase__					,	negative_prompt_embeds=UpperCamelCase__					,	image=UpperCamelCase__					,	original_image=UpperCamelCase__					,	generator=UpperCamelCase__					,	num_inference_steps=2					,	output_type='''np'''					,	)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							output.images[0]

												assert image.shape == (256, 256, 3)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							torch.cuda.max_memory_allocated()
												assert mem_bytes < 4 * 10**9

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy'''		)
												assert_mean_pixel_difference(UpperCamelCase__					,	UpperCamelCase__		)



						def  __A ( self			:       List[str]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												_start_torch_memory_measurement()

												SCREAMING_SNAKE_CASE							:					str           =							floats_tensor((1, 3, 64, 64)					,	rng=random.Random(0		)		).to(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							floats_tensor((1, 3, 64, 64)					,	rng=random.Random(1		)		).to(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							torch.Generator(device='''cpu'''		).manual_seed(0		)
												SCREAMING_SNAKE_CASE							:					int           =							pipe_a(
												    prompt_embeds=UpperCamelCase__					,	negative_prompt_embeds=UpperCamelCase__					,	image=UpperCamelCase__					,	mask_image=UpperCamelCase__					,	num_inference_steps=2					,	generator=UpperCamelCase__					,	output_type='''np'''					,	)

												SCREAMING_SNAKE_CASE							:					Any           =							output.images[0]

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

												SCREAMING_SNAKE_CASE							:					Any           =							torch.cuda.max_memory_allocated()
												assert mem_bytes < 10 * 10**9

												SCREAMING_SNAKE_CASE							:					List[Any]           =							load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy'''		)
												assert_mean_pixel_difference(UpperCamelCase__					,	UpperCamelCase__		)

												# pipeline 2

												_start_torch_memory_measurement()

												SCREAMING_SNAKE_CASE							:					str           =							torch.Generator(device='''cpu'''		).manual_seed(0		)

												SCREAMING_SNAKE_CASE							:					List[str]           =							floats_tensor((1, 3, 64, 64)					,	rng=random.Random(0		)		).to(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					int           =							floats_tensor((1, 3, 256, 256)					,	rng=random.Random(0		)		).to(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							floats_tensor((1, 3, 256, 256)					,	rng=random.Random(1		)		).to(UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Dict           =							pipe_a(
												    prompt_embeds=UpperCamelCase__					,	negative_prompt_embeds=UpperCamelCase__					,	image=UpperCamelCase__					,	mask_image=UpperCamelCase__					,	original_image=UpperCamelCase__					,	generator=UpperCamelCase__					,	num_inference_steps=2					,	output_type='''np'''					,	)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							output.images[0]

												assert image.shape == (256, 256, 3)

												SCREAMING_SNAKE_CASE							:					Any           =							torch.cuda.max_memory_allocated()
												assert mem_bytes < 4 * 10**9

												SCREAMING_SNAKE_CASE							:					Tuple           =							load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy'''		)
												assert_mean_pixel_difference(UpperCamelCase__					,	UpperCamelCase__		)






def    A			(					):
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()





