import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
		import transformers




def       _snake_case	(   lowerCAmelCase							:   int	):
		"""simple docstring"""







		random.seed(lowerCAmelCase	)
		np.random.seed(lowerCAmelCase	)
		torch.manual_seed(lowerCAmelCase	)
		torch.cuda.manual_seed_all(lowerCAmelCase	)
		# ^^ safe to call this function even if cuda is not available







class 		a__						:

					def __init__(    self      :       Tuple,_A      :       Iterable[torch.nn.Parameter],_A      :       float = 0.9999,_A      :       float = 0.0,_A      :       int = 0,_A      :       bool = False,_A      :       Union[float, int] = 1.0,_A      :       Union[float, int] = 2 / 3,_A      :       Optional[Any] = None,_A      :       Dict[str, Any] = None,**_A      :       str,):







							"""simple docstring"""






							if isinstance(_A,torch.nn.Module    ):
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    (
									    "Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. "
									    "Please pass the parameters of the module instead."
									)
									deprecate(
									    "passing a `torch.nn.Module` to `ExponentialMovingAverage`","1.0.0",_A,standard_warn=_A,)
									SCREAMING_SNAKE_CASE_  :		str				      =    parameters.parameters()

									# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
									SCREAMING_SNAKE_CASE_  :		str				      =    True

							if kwargs.get("max_value",_A    ) is not None:
									SCREAMING_SNAKE_CASE_  :		str				      =    "The `max_value` argument is deprecated. Please use `decay` instead."
									deprecate("max_value","1.0.0",_A,standard_warn=_A    )
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    kwargs["max_value"]

							if kwargs.get("min_value",_A    ) is not None:
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    "The `min_value` argument is deprecated. Please use `min_decay` instead."
									deprecate("min_value","1.0.0",_A,standard_warn=_A    )
									SCREAMING_SNAKE_CASE_  :		int				      =    kwargs["min_value"]

							SCREAMING_SNAKE_CASE_  :		Dict				      =    list(_A    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    [p.clone().detach() for p in parameters]

							if kwargs.get("device",_A    ) is not None:
									SCREAMING_SNAKE_CASE_  :		str				      =    "The `device` argument is deprecated. Please use `to` instead."
									deprecate("device","1.0.0",_A,standard_warn=_A    )
									self.to(device=kwargs["device"]    )

							SCREAMING_SNAKE_CASE_  :		Dict				      =    None

							SCREAMING_SNAKE_CASE_  :		Any				      =    decay
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    min_decay
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    update_after_step
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    use_ema_warmup
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    inv_gamma
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    power
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    0
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    None  # set in `step()`

							SCREAMING_SNAKE_CASE_  :		Dict				      =    model_cls
							SCREAMING_SNAKE_CASE_  :		Any				      =    model_config

					@classmethod
					def 				__UpperCamelCase   (    cls      :       Dict,_A      :       Tuple,_A      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    model_cls.load_config(_A,return_unused_kwargs=_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    model_cls.from_pretrained(_A    )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    cls(model.parameters(),model_cls=_A,model_config=model.config    )

							ema_model.load_state_dict(_A    )
							return ema_model

					def 				__UpperCamelCase   (    self      :       Optional[Any],_A      :       int    ):







							"""simple docstring"""






							if self.model_cls is None:
									raise ValueError("`save_pretrained` can only be used if `model_cls` was defined at __init__."    )

							if self.model_config is None:
									raise ValueError("`save_pretrained` can only be used if `model_config` was defined at __init__."    )

							SCREAMING_SNAKE_CASE_  :		str				      =    self.model_cls.from_config(self.model_config    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.state_dict()
							state_dict.pop("shadow_params",_A    )

							model.register_to_config(**_A    )
							self.copy_to(model.parameters()    )
							model.save_pretrained(_A    )

					def 				__UpperCamelCase   (    self      :       str,_A      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    max(0,optimization_step - self.update_after_step - 1    )

							if step <= 0:
									return 0.0

							if self.use_ema_warmup:
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    1 - (1 + step / self.inv_gamma) ** -self.power
							else:
									SCREAMING_SNAKE_CASE_  :		List[str]				      =    (1 + step) / (10 + step)

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    min(_A,self.decay    )
							# make sure decay is not smaller than min_decay
							SCREAMING_SNAKE_CASE_  :		Any				      =    max(_A,self.min_decay    )
							return cur_decay_value

					@torch.no_grad()
					def 				__UpperCamelCase   (    self      :       str,_A      :       Iterable[torch.nn.Parameter]    ):







							"""simple docstring"""






							if isinstance(_A,torch.nn.Module    ):
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    (
									    "Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. "
									    "Please pass the parameters of the module instead."
									)
									deprecate(
									    "passing a `torch.nn.Module` to `ExponentialMovingAverage.step`","1.0.0",_A,standard_warn=_A,)
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    parameters.parameters()

							SCREAMING_SNAKE_CASE_  :		int				      =    list(_A    )

							self.optimization_step += 1

							# Compute the decay factor for the exponential moving average.
							SCREAMING_SNAKE_CASE_  :		Any				      =    self.get_decay(self.optimization_step    )
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    decay
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    1 - decay

							SCREAMING_SNAKE_CASE_  :		str				      =    contextlib.nullcontext
							if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
									import deepspeed

							for s_param, param in zip(self.shadow_params,_A    ):
									if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
											SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    deepspeed.zero.GatheredParameters(_A,modifier_rank=_A    )

									with context_manager():
											if param.requires_grad:
													s_param.sub_(one_minus_decay * (s_param - param)    )
											else:
													s_param.copy_(_A    )

					def 				__UpperCamelCase   (    self      :       int,_A      :       Iterable[torch.nn.Parameter]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple				      =    list(_A    )
							for s_param, param in zip(self.shadow_params,_A    ):
									param.data.copy_(s_param.to(param.device    ).data    )

					def 				__UpperCamelCase   (    self      :       Dict,_A      :       Any=None,_A      :       Union[str, Any]=None    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    [
							    p.to(device=_A,dtype=_A    ) if p.is_floating_point() else p.to(device=_A    )
							    for p in self.shadow_params
							]

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							return {
							    "decay": self.decay,
							    "min_decay": self.min_decay,
							    "optimization_step": self.optimization_step,
							    "update_after_step": self.update_after_step,
							    "use_ema_warmup": self.use_ema_warmup,
							    "inv_gamma": self.inv_gamma,
							    "power": self.power,
							    "shadow_params": self.shadow_params,
							}

					def 				__UpperCamelCase   (    self      :       Optional[Any],_A      :       Iterable[torch.nn.Parameter]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    [param.detach().cpu().clone() for param in parameters]

					def 				__UpperCamelCase   (    self      :       int,_A      :       Iterable[torch.nn.Parameter]    ):







							"""simple docstring"""






							if self.temp_stored_params is None:
									raise RuntimeError("This ExponentialMovingAverage has no `store()`ed weights " "to `restore()`"    )
							for c_param, param in zip(self.temp_stored_params,_A    ):
									param.data.copy_(c_param.data    )

							# Better memory-wise.
							SCREAMING_SNAKE_CASE_  :		Dict				      =    None






					def 				__UpperCamelCase   (    self      :       Union[str, Any],_A      :       dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    copy.deepcopy(_A    )

							SCREAMING_SNAKE_CASE_  :		str				      =    state_dict.get("decay",self.decay    )
							if self.decay < 0.0 or self.decay > 1.0:
									raise ValueError("Decay must be between 0 and 1"    )

							SCREAMING_SNAKE_CASE_  :		Dict				      =    state_dict.get("min_decay",self.min_decay    )
							if not isinstance(self.min_decay,_A    ):
									raise ValueError("Invalid min_decay"    )

							SCREAMING_SNAKE_CASE_  :		Any				      =    state_dict.get("optimization_step",self.optimization_step    )
							if not isinstance(self.optimization_step,_A    ):
									raise ValueError("Invalid optimization_step"    )

							SCREAMING_SNAKE_CASE_  :		Any				      =    state_dict.get("update_after_step",self.update_after_step    )
							if not isinstance(self.update_after_step,_A    ):
									raise ValueError("Invalid update_after_step"    )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    state_dict.get("use_ema_warmup",self.use_ema_warmup    )
							if not isinstance(self.use_ema_warmup,_A    ):
									raise ValueError("Invalid use_ema_warmup"    )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    state_dict.get("inv_gamma",self.inv_gamma    )
							if not isinstance(self.inv_gamma,(float, int)    ):
									raise ValueError("Invalid inv_gamma"    )

							SCREAMING_SNAKE_CASE_  :		int				      =    state_dict.get("power",self.power    )
							if not isinstance(self.power,(float, int)    ):
									raise ValueError("Invalid power"    )

							SCREAMING_SNAKE_CASE_  :		str				      =    state_dict.get("shadow_params",_A    )
							if shadow_params is not None:
									SCREAMING_SNAKE_CASE_  :		int				      =    shadow_params
									if not isinstance(self.shadow_params,_A    ):
											raise ValueError("shadow_params must be a list"    )
									if not all(isinstance(_A,torch.Tensor    ) for p in self.shadow_params    ):
											raise ValueError("shadow_params must all be Tensors"    )

import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
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

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




def       _snake_case	(   lowerCAmelCase							:   Tuple	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Tuple				      =    [tensor.shape for tensor in tensor_list]
		return all(shape == shapes[0] for shape in shapes[1:]	)






class 		a__						(					A__    ,		A__    ,		A__    ,		unittest.TestCase					):
					A	       =							StableDiffusionLatentUpscalePipeline
					A	       =							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
					    'height',
					    'width',
					    'cross_attention_kwargs',
					    'negative_prompt_embeds',
					    'prompt_embeds',
					}
					A	       =							PipelineTesterMixin.required_optional_params - {'num_images_per_prompt'}
					A	       =							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
					A	       =							frozenset(
					    []					)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
					A	       =							frozenset([]					)

					A	       =							True

					@property
					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    1
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    4
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    (16, 16)

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

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							torch.manual_seed(0    )
							SCREAMING_SNAKE_CASE_  :		str				      =    UNetaDConditionModel(
							    act_fn="gelu",attention_head_dim=8,norm_num_groups=_A,block_out_channels=[32, 32, 64, 64],time_cond_proj_dim=160,conv_in_kernel=1,conv_out_kernel=1,cross_attention_dim=32,down_block_types=(
							        "KDownBlock2D",
							        "KCrossAttnDownBlock2D",
							        "KCrossAttnDownBlock2D",
							        "KCrossAttnDownBlock2D",
							    ),in_channels=8,mid_block_type=_A,only_cross_attention=_A,out_channels=5,resnet_time_scale_shift="scale_shift",time_embedding_type="fourier",timestep_post_act="gelu",up_block_types=("KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KCrossAttnUpBlock2D", "KUpBlock2D"),)
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    AutoencoderKL(
							    block_out_channels=[32, 32, 64, 64],in_channels=3,out_channels=3,down_block_types=[
							        "DownEncoderBlock2D",
							        "DownEncoderBlock2D",
							        "DownEncoderBlock2D",
							        "DownEncoderBlock2D",
							    ],up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],latent_channels=4,)
							SCREAMING_SNAKE_CASE_  :		int				      =    EulerDiscreteScheduler(prediction_type="sample"    )
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    CLIPTextConfig(
							    bos_token_id=0,eos_token_id=2,hidden_size=32,intermediate_size=37,layer_norm_eps=1E-05,num_attention_heads=4,num_hidden_layers=5,pad_token_id=1,vocab_size=1000,hidden_act="quick_gelu",projection_dim=512,)
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    CLIPTextModel(_A    )
							SCREAMING_SNAKE_CASE_  :		int				      =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    {
							    "unet": model.eval(),
							    "vae": vae.eval(),
							    "scheduler": scheduler,
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							}

							return components

					def 				__UpperCamelCase   (    self      :       List[Any],_A      :       int,_A      :       Tuple=0    ):







							"""simple docstring"""






							if str(_A    ).startswith("mps"    ):
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    torch.manual_seed(_A    )
							else:
									SCREAMING_SNAKE_CASE_  :		Dict				      =    torch.Generator(device=_A    ).manual_seed(_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    {
							    "prompt": "A painting of a squirrel eating a burger",
							    "image": self.dummy_image.cpu(),
							    "generator": generator,
							    "num_inference_steps": 2,
							    "output_type": "numpy",
							}
							return inputs

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    "cpu"

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.get_dummy_components()
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.pipeline_class(**_A    )
							pipe.to(_A    )
							pipe.set_progress_bar_config(disable=_A    )

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    self.get_dummy_inputs(_A    )
							SCREAMING_SNAKE_CASE_  :		Dict				      =    pipe(**_A    ).images
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    image[0, -3:, -3:, -1]

							self.assertEqual(image.shape,(1, 256, 256, 3)    )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    np.array(
							    [0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055]    )
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    np.abs(image_slice.flatten() - expected_slice    ).max()
							self.assertLessEqual(_A,1E-3    )

					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							super().test_attention_slicing_forward_pass(expected_max_diff=7E-3    )

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							super().test_cpu_offload_forward_pass(expected_max_diff=3E-3    )

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3    )

					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							super().test_inference_batch_single_identical(expected_max_diff=7E-3    )

					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3    )

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							super().test_save_load_local(expected_max_difference=3E-3    )

					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							super().test_save_load_optional_components(expected_max_difference=3E-3    )






					def 				__UpperCamelCase   (    self      :       Tuple    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    [
							    "DDIMScheduler",
							    "DDPMScheduler",
							    "PNDMScheduler",
							    "HeunDiscreteScheduler",
							    "EulerAncestralDiscreteScheduler",
							    "KDPM2DiscreteScheduler",
							    "KDPM2AncestralDiscreteScheduler",
							    "DPMSolverSDEScheduler",
							]
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.get_dummy_components()
							SCREAMING_SNAKE_CASE_  :		Any				      =    self.pipeline_class(**_A    )

							# make sure that PNDM does not need warm-up
							pipe.scheduler.register_to_config(skip_prk_steps=_A    )

							pipe.to(_A    )
							pipe.set_progress_bar_config(disable=_A    )
							SCREAMING_SNAKE_CASE_  :		Any				      =    self.get_dummy_inputs(_A    )
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    2

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    []
							for scheduler_enum in KarrasDiffusionSchedulers:
									if scheduler_enum.name in skip_schedulers:
											# no sigma schedulers are not supported
											# no schedulers
											continue

									SCREAMING_SNAKE_CASE_  :		Tuple				      =    getattr(_A,scheduler_enum.name    )
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    scheduler_cls.from_config(pipe.scheduler.config    )
									SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    pipe(**_A    )[0]
									outputs.append(_A    )

							assert check_same_shape(_A    )







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

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






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

					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    torch.manual_seed(33    )

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4",torch_dtype=torch.floataa    )
							pipe.to("cuda"    )

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    StableDiffusionLatentUpscalePipeline.from_pretrained(
							    "stabilityai/sd-x2-latent-upscaler",torch_dtype=torch.floataa    )
							upscaler.to("cuda"    )

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    "a photo of an astronaut high resolution, unreal engine, ultra realistic"

							SCREAMING_SNAKE_CASE_  :		str				      =    pipe(_A,generator=_A,output_type="latent"    ).images

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    upscaler(
							    prompt=_A,image=_A,num_inference_steps=20,guidance_scale=0,generator=_A,output_type="np",).images[0]

							SCREAMING_SNAKE_CASE_  :		str				      =    load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy"    )
							assert np.abs((expected_image - image).mean()    ) < 5E-2






					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int				      =    torch.manual_seed(33    )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    StableDiffusionLatentUpscalePipeline.from_pretrained(
							    "stabilityai/sd-x2-latent-upscaler",torch_dtype=torch.floataa    )
							upscaler.to("cuda"    )

							SCREAMING_SNAKE_CASE_  :		Any				      =    "the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png"    )

							SCREAMING_SNAKE_CASE_  :		str				      =    upscaler(
							    prompt=_A,image=_A,num_inference_steps=20,guidance_scale=0,generator=_A,output_type="np",).images[0]

							SCREAMING_SNAKE_CASE_  :		Any				      =    load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy"    )
							assert np.abs((expected_image - image).max()    ) < 5E-2
