



import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__UpperCAmelCase          =       logging.get_logger(__name__)



class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					int        =  ["input_values", "attention_mask"]
       def __init__(			self,			SCREAMING_SNAKE_CASE_ = 1,			SCREAMING_SNAKE_CASE_ = 1_6000,			SCREAMING_SNAKE_CASE_ = 0.0,			SCREAMING_SNAKE_CASE_ = False,			SCREAMING_SNAKE_CASE_ = 80,			SCREAMING_SNAKE_CASE_ = 16,			SCREAMING_SNAKE_CASE_ = 64,			SCREAMING_SNAKE_CASE_ = "hann_window",			SCREAMING_SNAKE_CASE_ = 1.0,			SCREAMING_SNAKE_CASE_ = 80,			SCREAMING_SNAKE_CASE_ = 7600,			SCREAMING_SNAKE_CASE_ = 1e-10,			SCREAMING_SNAKE_CASE_ = 2,			SCREAMING_SNAKE_CASE_ = True,			**SCREAMING_SNAKE_CASE_,			)     ->		Tuple:
          super().__init__(feature_size=SCREAMING_SNAKE_CASE_,			sampling_rate=SCREAMING_SNAKE_CASE_,			padding_value=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Dict							=	do_normalize
          UpperCamelCase    :  List[str]							=	return_attention_mask

          UpperCamelCase    :  Dict							=	num_mel_bins
          UpperCamelCase    :  int							=	hop_length
          UpperCamelCase    :  List[str]							=	win_length
          UpperCamelCase    :  Optional[Any]							=	win_function
          UpperCamelCase    :  Union[str, Any]							=	frame_signal_scale
          UpperCamelCase    :  Union[str, Any]							=	fmin
          UpperCamelCase    :  Dict							=	fmax
          UpperCamelCase    :  str							=	mel_floor
          UpperCamelCase    :  Dict							=	reduction_factor

          UpperCamelCase    :  List[str]							=	win_length * sampling_rate // 1000
          UpperCamelCase    :  int							=	hop_length * sampling_rate // 1000
          UpperCamelCase    :  List[Any]							=	optimal_fft_length(self.sample_size       )
          UpperCamelCase    :  List[Any]							=	(self.n_fft // 2) + 1

          UpperCamelCase    :  Any							=	window_function(window_length=self.sample_size,			name=self.win_function,			periodic=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  List[Any]							=	mel_filter_bank(
              num_frequency_bins=self.n_freqs,			num_mel_filters=self.num_mel_bins,			min_frequency=self.fmin,			max_frequency=self.fmax,			sampling_rate=self.sampling_rate,			norm='slaney',			mel_scale='slaney',			)

          if frame_signal_scale != 1.0:
             warnings.warn(
                 'The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers',			SCREAMING_SNAKE_CASE_,			)
          if reduction_factor != 2.0:
             warnings.warn(
                 'The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers',			SCREAMING_SNAKE_CASE_,			)
       @staticmethod
       # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
       def 					snake_case_       (			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = 0.0       )     ->		List[np.ndarray]:
          if attention_mask is not None:
             UpperCamelCase    :  int							=	np.array(SCREAMING_SNAKE_CASE_,			np.intaa       )
             UpperCamelCase    :  Union[str, Any]							=	[]

             for vector, length in zip(SCREAMING_SNAKE_CASE_,			attention_mask.sum(-1       )       ):
                UpperCamelCase    :  List[Any]							=	(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7       )
                if length < normed_slice.shape[0]:
                   UpperCamelCase    :  str							=	padding_value

                normed_input_values.append(SCREAMING_SNAKE_CASE_       )
          else:
             UpperCamelCase    :  List[str]							=	[(x - x.mean()) / np.sqrt(x.var() + 1e-7       ) for x in input_values]

          return normed_input_values
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			)     ->		np.ndarray:
          UpperCamelCase    :  Union[str, Any]							=	spectrogram(
              SCREAMING_SNAKE_CASE_,			window=self.window,			frame_length=self.sample_size,			hop_length=self.sample_stride,			fft_length=self.n_fft,			mel_filters=self.mel_filters,			mel_floor=self.mel_floor,			log_mel='log10',			)
          return log_mel_spec.T
       def __call__(			self,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = False,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = False,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = None,			**SCREAMING_SNAKE_CASE_,			)     ->		BatchFeature:
          if audio is None and audio_target is None:
             raise ValueError('You must provide either `audio` or `audio_target` values.'       )

          if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                raise ValueError(
                    F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
                    F""" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"""
                    F""" {self.sampling_rate} and not {sampling_rate}."""       )
          else:
             logger.warning(
                 'It is strongly recommended to pass the ``sampling_rate`` argument to this function. '
                 'Failing to do so can result in silent errors that might be hard to debug.'       )

          if audio is not None:
             UpperCamelCase    :  Dict							=	self._process_audio(
                 SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_,			)
          else:
             UpperCamelCase    :  Optional[int]							=	None

          if audio_target is not None:
             UpperCamelCase    :  List[str]							=	self._process_audio(
                 SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_,			)

             if inputs is None:
                return inputs_target
             else:
                UpperCamelCase    :  List[str]							=	inputs_target['input_values']
                UpperCamelCase    :  List[str]							=	inputs_target.get('attention_mask'       )
                if decoder_attention_mask is not None:
                   UpperCamelCase    :  Optional[Any]							=	decoder_attention_mask

          return inputs
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = False,			SCREAMING_SNAKE_CASE_ = False,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = False,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = None,			**SCREAMING_SNAKE_CASE_,			)     ->		BatchFeature:
          UpperCamelCase    :  Any							=	isinstance(SCREAMING_SNAKE_CASE_,			np.ndarray       ) and len(speech.shape       ) > 1
          if is_batched_numpy and len(speech.shape       ) > 2:
             raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""       )
          UpperCamelCase    :  Union[str, Any]							=	is_batched_numpy or (
              isinstance(SCREAMING_SNAKE_CASE_,			(list, tuple)       ) and (isinstance(speech[0],			(np.ndarray, tuple, list)       ))
          )

          if is_batched:
             UpperCamelCase    :  Union[str, Any]							=	[np.asarray(SCREAMING_SNAKE_CASE_,			dtype=np.floataa       ) for speech in speech]
          elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE_,			np.ndarray       ):
             UpperCamelCase    :  Union[str, Any]							=	np.asarray(SCREAMING_SNAKE_CASE_,			dtype=np.floataa       )
          elif isinstance(SCREAMING_SNAKE_CASE_,			np.ndarray       ) and speech.dtype is np.dtype(np.floataa       ):
             UpperCamelCase    :  Tuple							=	speech.astype(np.floataa       )

          # always return batch
          if not is_batched:
             UpperCamelCase    :  Dict							=	[speech]

          # needed to make pad() work on spectrogram inputs
          UpperCamelCase    :  Optional[Any]							=	self.feature_size

          # convert into correct format for padding
          if is_target:
             UpperCamelCase    :  List[Any]							=	[self._extract_mel_features(SCREAMING_SNAKE_CASE_       ) for waveform in speech]
             UpperCamelCase    :  Optional[Any]							=	BatchFeature({'input_values': features}       )
             UpperCamelCase    :  Optional[int]							=	self.num_mel_bins
          else:
             UpperCamelCase    :  List[str]							=	BatchFeature({'input_values': speech}       )

          UpperCamelCase    :  int							=	self.pad(
              SCREAMING_SNAKE_CASE_,			padding=SCREAMING_SNAKE_CASE_,			max_length=SCREAMING_SNAKE_CASE_,			truncation=SCREAMING_SNAKE_CASE_,			pad_to_multiple_of=SCREAMING_SNAKE_CASE_,			return_attention_mask=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_,			)

          UpperCamelCase    :  List[str]							=	feature_size_hack

          # convert input values to correct format
          UpperCamelCase    :  Union[str, Any]							=	padded_inputs['input_values']
          if not isinstance(input_values[0],			np.ndarray       ):
             UpperCamelCase    :  int							=	[np.asarray(SCREAMING_SNAKE_CASE_,			dtype=np.floataa       ) for array in input_values]
          elif (
              not isinstance(SCREAMING_SNAKE_CASE_,			np.ndarray       )
              and isinstance(input_values[0],			np.ndarray       )
              and input_values[0].dtype is np.dtype(np.floataa       )
          ):
             UpperCamelCase    :  Tuple							=	[array.astype(np.floataa       ) for array in input_values]
          elif isinstance(SCREAMING_SNAKE_CASE_,			np.ndarray       ) and input_values.dtype is np.dtype(np.floataa       ):
             UpperCamelCase    :  List[Any]							=	input_values.astype(np.floataa       )

          # convert attention_mask to correct format
          UpperCamelCase    :  Dict							=	padded_inputs.get('attention_mask'       )
          if attention_mask is not None:
             UpperCamelCase    :  List[str]							=	[np.asarray(SCREAMING_SNAKE_CASE_,			dtype=np.intaa       ) for array in attention_mask]

          # zero-mean and unit-variance normalization
          if not is_target and self.do_normalize:
             UpperCamelCase    :  Optional[int]							=	(
                 attention_mask
                 if self._get_padding_strategies(SCREAMING_SNAKE_CASE_,			max_length=SCREAMING_SNAKE_CASE_       ) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             UpperCamelCase    :  Union[str, Any]							=	self.zero_mean_unit_var_norm(
                 padded_inputs['input_values'],			attention_mask=SCREAMING_SNAKE_CASE_,			padding_value=self.padding_value       )

          if return_tensors is not None:
             UpperCamelCase    :  Dict							=	padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE_       )

          return padded_inputs






       def 					snake_case_       (			self       )     ->		Dict[str, Any]:
          UpperCamelCase    :  Optional[Any]							=	super().to_dict()

          # Don't serialize these as they are derived from the other properties.
          UpperCamelCase    :  Union[str, Any]							=	['window', 'mel_filters', 'sample_size', 'sample_stride', 'n_fft', 'n_freqs']
          for name in names:
             if name in output:
                del output[name]

          return output








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



