



from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


__UpperCAmelCase          =       logging.get_logger(__name__)



class 				lowerCAmelCase_		(  a__							):
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )     ->		Dict:
          UpperCamelCase    :  Any							=	feature_size
          UpperCamelCase    :  str							=	sampling_rate
          UpperCamelCase    :  Dict							=	padding_value

          UpperCamelCase    :  Optional[int]							=	kwargs.pop('padding_side',			'right'       )
          UpperCamelCase    :  Dict							=	kwargs.pop('return_attention_mask',			SCREAMING_SNAKE_CASE_       )

          super().__init__(**SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = True,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = False,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = None,			)     ->		BatchFeature:
          # If we have a list of dicts, let's convert it in a dict of lists
          # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
          if isinstance(SCREAMING_SNAKE_CASE_,			(list, tuple)       ) and isinstance(processed_features[0],			(dict, BatchFeature)       ):
             UpperCamelCase    :  Optional[Any]							=	{
                 key: [example[key] for example in processed_features] for key in processed_features[0].keys()
             }

          # The model's main input name, usually `input_values`, has be passed for padding
          if self.model_input_names[0] not in processed_features:
             raise ValueError(
                 'You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'
                 F""" to this method that includes {self.model_input_names[0]}, but you provided"""
                 F""" {list(processed_features.keys()       )}"""       )

          UpperCamelCase    :  List[Any]							=	processed_features[self.model_input_names[0]]
          UpperCamelCase    :  Optional[int]							=	(
              return_attention_mask if return_attention_mask is not None else self.return_attention_mask
          )

          if len(SCREAMING_SNAKE_CASE_       ) == 0:
             if return_attention_mask:
                UpperCamelCase    :  int							=	[]
             return processed_features

          # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
          # and rebuild them afterwards if no return_tensors is specified
          # Note that we lose the specific device the tensor may be on for PyTorch

          UpperCamelCase    :  Dict							=	required_input[0]
          if isinstance(SCREAMING_SNAKE_CASE_,			(list, tuple)       ):
             # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
             UpperCamelCase    :  str							=	0
             while len(required_input[index]       ) == 0:
                index += 1
             if index < len(SCREAMING_SNAKE_CASE_       ):
                UpperCamelCase    :  List[Any]							=	required_input[index][0]

          if return_tensors is None:
             if is_tf_tensor(SCREAMING_SNAKE_CASE_       ):
                UpperCamelCase    :  str							=	'tf'
             elif is_torch_tensor(SCREAMING_SNAKE_CASE_       ):
                UpperCamelCase    :  Tuple							=	'pt'
             elif isinstance(SCREAMING_SNAKE_CASE_,			(int, float, list, tuple, np.ndarray)       ):
                UpperCamelCase    :  Tuple							=	'np'
             else:
                raise ValueError(
                    F"""type of {first_element} unknown: {type(SCREAMING_SNAKE_CASE_       )}. """
                    'Should be one of a python, numpy, pytorch or tensorflow object.'       )

          for key, value in processed_features.items():
             if isinstance(value[0],			(int, float)       ):
                UpperCamelCase    :  Tuple							=	to_numpy(SCREAMING_SNAKE_CASE_       )
             else:
                UpperCamelCase    :  Optional[Any]							=	[to_numpy(SCREAMING_SNAKE_CASE_       ) for v in value]

        # Convert padding_strategy in PaddingStrategy
          UpperCamelCase    :  Optional[Any]							=	self._get_padding_strategies(padding=SCREAMING_SNAKE_CASE_,			max_length=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Union[str, Any]							=	processed_features[self.model_input_names[0]]

          UpperCamelCase    :  Union[str, Any]							=	len(SCREAMING_SNAKE_CASE_       )
          if not all(len(SCREAMING_SNAKE_CASE_       ) == batch_size for v in processed_features.values()       ):
             raise ValueError('Some items in the output dictionary have a different batch size than others.'       )

          UpperCamelCase    :  Any							=	[]
          for i in range(SCREAMING_SNAKE_CASE_       ):
             UpperCamelCase    :  str							=	{k: v[i] for k, v in processed_features.items()}
             # truncation
             UpperCamelCase    :  str							=	self._truncate(
                 SCREAMING_SNAKE_CASE_,			max_length=SCREAMING_SNAKE_CASE_,			pad_to_multiple_of=SCREAMING_SNAKE_CASE_,			truncation=SCREAMING_SNAKE_CASE_,			)
             truncated_inputs.append(SCREAMING_SNAKE_CASE_       )

          if padding_strategy == PaddingStrategy.LONGEST:
             # make sure that `max_length` cannot be longer than the longest truncated length
             UpperCamelCase    :  Optional[Any]							=	max(len(input_slice[self.model_input_names[0]]       ) for input_slice in truncated_inputs       )
             UpperCamelCase    :  int							=	PaddingStrategy.MAX_LENGTH

          UpperCamelCase    :  Dict							=	{}
          for i in range(SCREAMING_SNAKE_CASE_       ):
             # padding
             UpperCamelCase    :  int							=	self._pad(
                 truncated_inputs[i],			max_length=SCREAMING_SNAKE_CASE_,			padding_strategy=SCREAMING_SNAKE_CASE_,			pad_to_multiple_of=SCREAMING_SNAKE_CASE_,			return_attention_mask=SCREAMING_SNAKE_CASE_,			)

             for key, value in outputs.items():
                if key not in batch_outputs:
                   UpperCamelCase    :  Union[str, Any]							=	[]
                if value.dtype is np.dtype(np.floataa       ):
                   UpperCamelCase    :  List[str]							=	value.astype(np.floataa       )
                batch_outputs[key].append(SCREAMING_SNAKE_CASE_       )

          return BatchFeature(SCREAMING_SNAKE_CASE_,			tensor_type=SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = PaddingStrategy.DO_NOT_PAD,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = None,			)     ->		dict:
          UpperCamelCase    :  Any							=	processed_features[self.model_input_names[0]]

          if padding_strategy == PaddingStrategy.LONGEST:
             UpperCamelCase    :  Dict							=	len(SCREAMING_SNAKE_CASE_       )

          if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
             UpperCamelCase    :  Dict							=	((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

          UpperCamelCase    :  Tuple							=	padding_strategy != PaddingStrategy.DO_NOT_PAD and len(SCREAMING_SNAKE_CASE_       ) < max_length

          if return_attention_mask and "attention_mask" not in processed_features:
             UpperCamelCase    :  List[Any]							=	np.ones(len(SCREAMING_SNAKE_CASE_       ),			dtype=np.intaa       )

          if needs_to_be_padded:
             UpperCamelCase    :  Union[str, Any]							=	max_length - len(SCREAMING_SNAKE_CASE_       )
             if self.padding_side == "right":
                if return_attention_mask:
                   UpperCamelCase    :  Any							=	np.pad(
                       processed_features['attention_mask'],			(0, difference)       )
                UpperCamelCase    :  Tuple							=	((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
                UpperCamelCase    :  Optional[Any]							=	np.pad(
                    SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			'constant',			constant_values=self.padding_value       )
             elif self.padding_side == "left":
                if return_attention_mask:
                   UpperCamelCase    :  List[str]							=	np.pad(
                       processed_features['attention_mask'],			(difference, 0)       )
                UpperCamelCase    :  Union[str, Any]							=	((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
                UpperCamelCase    :  Tuple							=	np.pad(
                    SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			'constant',			constant_values=self.padding_value       )
             else:
                raise ValueError('Invalid padding strategy:' + str(self.padding_side       )       )

          return processed_features
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = None,			)     ->		Optional[Any]:
          if not truncation:
             return processed_features
          elif truncation and max_length is None:
             raise ValueError('When setting ``truncation=True``, make sure that ``max_length`` is defined.'       )

          UpperCamelCase    :  Tuple							=	processed_features[self.model_input_names[0]]

          # find `max_length` that fits `pad_to_multiple_of`
          if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
             UpperCamelCase    :  str							=	((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

          UpperCamelCase    :  str							=	len(SCREAMING_SNAKE_CASE_       ) > max_length

          if needs_to_be_truncated:
             UpperCamelCase    :  Optional[int]							=	processed_features[self.model_input_names[0]][:max_length]
             if "attention_mask" in processed_features:
                UpperCamelCase    :  Union[str, Any]							=	processed_features['attention_mask'][:max_length]

          return processed_features






       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_=False,			SCREAMING_SNAKE_CASE_=None       )     ->		Dict:

          # Get padding strategy
          if padding is not False:
             if padding is True:
                UpperCamelCase    :  int							=	PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
             elif not isinstance(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ):
                UpperCamelCase    :  List[Any]							=	PaddingStrategy(SCREAMING_SNAKE_CASE_       )
             elif isinstance(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       ):
                UpperCamelCase    :  Tuple							=	padding
          else:
             UpperCamelCase    :  List[str]							=	PaddingStrategy.DO_NOT_PAD

          # Set max length if needed
          if max_length is None:
             if padding_strategy == PaddingStrategy.MAX_LENGTH:
                raise ValueError(
                    F"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"""       )

        # Test if we have a padding value
          if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
             raise ValueError(
                 'Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'
                 ' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'       )

          return padding_strategy








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 		UpperCamelCase	(   snake_case__       :    Tuple	)	->     List[str]:
   UpperCamelCase    :  Optional[Any]							=	[tensor.shape for tensor in tensor_list]
   return all(shape == shapes[0] for shape in shapes[1:]	)



class 				lowerCAmelCase_		(  a__       ,    a__       ,    a__       ,    unittest.TestCase							):
       UpperCAmelCase__				:					str        =  StableDiffusionLatentUpscalePipeline
       UpperCAmelCase__				:					Optional[Any]        =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
           "height",
           "width",
           "cross_attention_kwargs",
           "negative_prompt_embeds",
           "prompt_embeds",
       }
       UpperCAmelCase__				:					str        =  PipelineTesterMixin.required_optional_params - {"num_images_per_prompt"}
       UpperCAmelCase__				:					Any        =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       UpperCAmelCase__				:					List[str]        =  frozenset(
           []							)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
       UpperCAmelCase__				:					Union[str, Any]        =  frozenset([]							)

       UpperCAmelCase__				:					Optional[int]        =  True
       @property
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          UpperCamelCase    :  List[Any]							=	1
          UpperCamelCase    :  List[str]							=	4
          UpperCamelCase    :  List[str]							=	(16, 16)

          UpperCamelCase    :  Optional[int]							=	floats_tensor((batch_size, num_channels) + sizes,			rng=random.Random(0       )       ).to(SCREAMING_SNAKE_CASE_       )
          return image
       def 					snake_case_       (			self       )     ->		int:
          torch.manual_seed(0       )
          UpperCamelCase    :  int							=	UNetaDConditionModel(
              act_fn='gelu',			attention_head_dim=8,			norm_num_groups=SCREAMING_SNAKE_CASE_,			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=SCREAMING_SNAKE_CASE_,			only_cross_attention=SCREAMING_SNAKE_CASE_,			out_channels=5,			resnet_time_scale_shift='scale_shift',			time_embedding_type='fourier',			timestep_post_act='gelu',			up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D'),			)
          UpperCamelCase    :  Tuple							=	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,			)
          UpperCamelCase    :  Tuple							=	EulerDiscreteScheduler(prediction_type='sample'       )
          UpperCamelCase    :  Optional[int]							=	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,			)
          UpperCamelCase    :  Any							=	CLIPTextModel(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Union[str, Any]							=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )

          UpperCamelCase    :  Optional[int]							=	{
              'unet': model.eval(),
              'vae': vae.eval(),
              'scheduler': scheduler,
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
          }

          return components
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=0       )     ->		List[str]:
          if str(SCREAMING_SNAKE_CASE_       ).startswith('mps'       ):
             UpperCamelCase    :  List[str]							=	torch.manual_seed(SCREAMING_SNAKE_CASE_       )
          else:
             UpperCamelCase    :  Optional[Any]							=	torch.Generator(device=SCREAMING_SNAKE_CASE_       ).manual_seed(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  List[str]							=	{
              '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 					snake_case_       (			self       )     ->		List[Any]:
          UpperCamelCase    :  str							=	'cpu'

          UpperCamelCase    :  Tuple							=	self.get_dummy_components()
          UpperCamelCase    :  Dict							=	self.pipeline_class(**SCREAMING_SNAKE_CASE_       )
          pipe.to(SCREAMING_SNAKE_CASE_       )
          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Union[str, Any]							=	self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  List[Any]							=	pipe(**SCREAMING_SNAKE_CASE_       ).images
          UpperCamelCase    :  str							=	image[0, -3:, -3:, -1]

          self.assertEqual(image.shape,			(1, 256, 256, 3)       )
          UpperCamelCase    :  List[Any]							=	np.array(
              [0.47_22_24_12, 0.41_92_16_33, 0.44_71_74_34, 0.46_87_41_92, 0.42_58_82_58, 0.46_15_07_26, 0.4_67_75_34, 0.45_58_38_32, 0.48_57_90_55]       )
          UpperCamelCase    :  Dict							=	np.abs(image_slice.flatten() - expected_slice       ).max()
          self.assertLessEqual(SCREAMING_SNAKE_CASE_,			1e-3       )
       def 					snake_case_       (			self       )     ->		List[Any]:
          super().test_attention_slicing_forward_pass(expected_max_diff=7e-3       )
       def 					snake_case_       (			self       )     ->		Tuple:
          super().test_cpu_offload_forward_pass(expected_max_diff=3e-3       )
       def 					snake_case_       (			self       )     ->		Dict:
          super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3       )
       def 					snake_case_       (			self       )     ->		Any:
          super().test_inference_batch_single_identical(expected_max_diff=7e-3       )
       def 					snake_case_       (			self       )     ->		List[str]:
          super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3       )
       def 					snake_case_       (			self       )     ->		Optional[int]:
          super().test_save_load_local(expected_max_difference=3e-3       )
       def 					snake_case_       (			self       )     ->		int:
          super().test_save_load_optional_components(expected_max_difference=3e-3       )






       def 					snake_case_       (			self       )     ->		Optional[Any]:
          UpperCamelCase    :  Optional[Any]							=	[
              'DDIMScheduler',
              'DDPMScheduler',
              'PNDMScheduler',
              'HeunDiscreteScheduler',
              'EulerAncestralDiscreteScheduler',
              'KDPM2DiscreteScheduler',
              'KDPM2AncestralDiscreteScheduler',
              'DPMSolverSDEScheduler',
          ]
          UpperCamelCase    :  Tuple							=	self.get_dummy_components()
          UpperCamelCase    :  Tuple							=	self.pipeline_class(**SCREAMING_SNAKE_CASE_       )

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

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

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

             UpperCamelCase    :  List[Any]							=	getattr(SCREAMING_SNAKE_CASE_,			scheduler_enum.name       )
             UpperCamelCase    :  List[str]							=	scheduler_cls.from_config(pipe.scheduler.config       )
             UpperCamelCase    :  List[str]							=	pipe(**SCREAMING_SNAKE_CASE_       )[0]
             outputs.append(SCREAMING_SNAKE_CASE_       )

          assert check_same_shape(SCREAMING_SNAKE_CASE_       )






@require_torch_gpu
@slow
class 				lowerCAmelCase_		(  unittest.TestCase							):
       def 					snake_case_       (			self       )     ->		Union[str, Any]:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
       def 					snake_case_       (			self       )     ->		List[Any]:
          UpperCamelCase    :  Dict							=	torch.manual_seed(33       )

          UpperCamelCase    :  Union[str, Any]							=	StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4',			torch_dtype=torch.floataa       )
          pipe.to('cuda'       )

          UpperCamelCase    :  Tuple							=	StableDiffusionLatentUpscalePipeline.from_pretrained(
              'stabilityai/sd-x2-latent-upscaler',			torch_dtype=torch.floataa       )
          upscaler.to('cuda'       )

          UpperCamelCase    :  Union[str, Any]							=	'a photo of an astronaut high resolution, unreal engine, ultra realistic'

          UpperCamelCase    :  int							=	pipe(SCREAMING_SNAKE_CASE_,			generator=SCREAMING_SNAKE_CASE_,			output_type='latent'       ).images

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

          UpperCamelCase    :  Optional[int]							=	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 					snake_case_       (			self       )     ->		int:
          UpperCamelCase    :  List[Any]							=	torch.manual_seed(33       )

          UpperCamelCase    :  Union[str, Any]							=	StableDiffusionLatentUpscalePipeline.from_pretrained(
              'stabilityai/sd-x2-latent-upscaler',			torch_dtype=torch.floataa       )
          upscaler.to('cuda'       )

          UpperCamelCase    :  Dict							=	'the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'

          UpperCamelCase    :  List[str]							=	load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'       )

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

          UpperCamelCase    :  Optional[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



