
"""simple docstring"""

import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


_A				=					logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
_A				=					direct_transformers_import(Path(__file__).parent)


_A				=					{
    'AutoTokenizer': 'PreTrainedTokenizerBase',
    'AutoFeatureExtractor': 'FeatureExtractionMixin',
    'AutoImageProcessor': 'ImageProcessingMixin',
}






class 			_lowercase  (							__UpperCAmelCase						):
	lowercase_								=      ['feature_extractor', 'tokenizer']
	# Names need to be attr_class for attr in attributes
	lowercase_								=      None
	lowercase_								=      None
	lowercase_								=      None




	def __init__(					self						,					*UpperCAmelCase_						,					**UpperCAmelCase_   )						->				Union[str, Any]:
			# Sanitize args and kwargs
			for key in kwargs:
					if key not in self.attributes:
							raise TypeError(F"""Unexpected keyword argument {key}."""   )
			for arg, attribute_name in zip(UpperCAmelCase_						,					self.attributes   ):
					if attribute_name in kwargs:
							raise TypeError(F"""Got multiple values for argument {attribute_name}."""   )
					else:
							lowerCamelCase	:     Union[str, Any]            =       arg

			if len(UpperCAmelCase_   ) != len(self.attributes   ):
					raise ValueError(
					    F"""This processor requires {len(self.attributes   )} arguments: {', '.join(self.attributes   )}. Got """
					    F"""{len(UpperCAmelCase_   )} arguments instead."""   )

			# Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
			for attribute_name, arg in kwargs.items():
					lowerCamelCase	:     str            =       getattr(self						,					F"""{attribute_name}_class"""   )
					# Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
					lowerCamelCase	:     int            =       AUTO_TO_BASE_CLASS_MAPPING.get(UpperCAmelCase_						,					UpperCAmelCase_   )
					if isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
							lowerCamelCase	:     Optional[int]            =       tuple(getattr(UpperCAmelCase_						,					UpperCAmelCase_   ) for n in class_name if n is not None   )
					else:
							lowerCamelCase	:     Union[str, Any]            =       getattr(UpperCAmelCase_						,					UpperCAmelCase_   )

					if not isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
							raise ValueError(
							    F"""Received a {type(UpperCAmelCase_   ).__name__} for argument {attribute_name}, but a {class_name} was expected."""   )

					setattr(self						,					UpperCAmelCase_						,					UpperCAmelCase_   )




	def __repr__(					self   )						->				Tuple:
			lowerCamelCase	:     List[Any]            =       [F"""- {name}: {repr(getattr(self						,					UpperCAmelCase_   )   )}""" for name in self.attributes]
			lowerCamelCase	:     int            =       '\n'.join(UpperCAmelCase_   )
			return F"""{self.__class__.__name__}:\n{attributes_repr}"""




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_ = False						,					**UpperCAmelCase_   )						->				List[str]:
			os.makedirs(UpperCAmelCase_						,					exist_ok=UpperCAmelCase_   )

			if push_to_hub:
					lowerCamelCase	:     Tuple            =       kwargs.pop('commit_message'						,					UpperCAmelCase_   )
					lowerCamelCase	:     Dict            =       kwargs.pop('repo_id'						,					save_directory.split(os.path.sep   )[-1]   )
					lowerCamelCase	:     Union[str, Any]            =       self._create_repo(UpperCAmelCase_						,					**UpperCAmelCase_   )
					lowerCamelCase	:     int            =       self._get_files_timestamps(UpperCAmelCase_   )
			# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
			# loaded from the Hub.
			if self._auto_class is not None:
					lowerCamelCase	:     Dict            =       [getattr(self						,					UpperCAmelCase_   ) for attribute_name in self.attributes]
					lowerCamelCase	:     List[str]            =       [(a.init_kwargs if isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ) else a) for a in attrs]
					custom_object_save(self						,					UpperCAmelCase_						,					config=UpperCAmelCase_   )

			for attribute_name in self.attributes:
					lowerCamelCase	:     Optional[int]            =       getattr(self						,					UpperCAmelCase_   )
					# Include the processor class in the attribute config so this processor can then be reloaded with the
					# `AutoProcessor` API.
					if hasattr(UpperCAmelCase_						,					'_set_processor_class'   ):
							attribute._set_processor_class(self.__class__.__name__   )
					attribute.save_pretrained(UpperCAmelCase_   )

			if self._auto_class is not None:
					# We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
					for attribute_name in self.attributes:
							lowerCamelCase	:     Optional[int]            =       getattr(self						,					UpperCAmelCase_   )
							if isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
									del attribute.init_kwargs["auto_map"]

			if push_to_hub:
					self._upload_modified_files(
					    UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					commit_message=UpperCAmelCase_						,					token=kwargs.get('use_auth_token'   )						,					)




	@classmethod
	def      _UpperCamelCase       (					cls						,					UpperCAmelCase_						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = False						,					UpperCAmelCase_ = False						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = "main"						,					**UpperCAmelCase_						,					)						->				Optional[Any]:
			lowerCamelCase	:     int            =       cache_dir
			lowerCamelCase	:     List[Any]            =       force_download
			lowerCamelCase	:     Any            =       local_files_only
			lowerCamelCase	:     str            =       revision

			lowerCamelCase	:     str            =       kwargs.pop('use_auth_token'						,					UpperCAmelCase_   )
			if use_auth_token is not None:
					warnings.warn(
					    'The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.'						,					UpperCAmelCase_   )
					if token is not None:
							raise ValueError(
							    '`token` and `use_auth_token` are both specified. Please set only the argument `token`.'   )
					lowerCamelCase	:     Dict            =       use_auth_token

			if token is not None:
					# change to `token` in a follow-up PR
					lowerCamelCase	:     List[str]            =       token

			lowerCamelCase	:     List[str]            =       cls._get_arguments_from_pretrained(UpperCAmelCase_						,					**UpperCAmelCase_   )
			return cls(*UpperCAmelCase_   )




	@classmethod
	def      _UpperCamelCase       (					cls						,					UpperCAmelCase_="AutoProcessor"   )						->				Any:
			if not isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
					lowerCamelCase	:     int            =       auto_class.__name__

			import transformers.models.auto as auto_module

			if not hasattr(UpperCAmelCase_						,					UpperCAmelCase_   ):
					raise ValueError(F"""{auto_class} is not a valid auto class."""   )

			lowerCamelCase	:     Optional[Any]            =       auto_class




	@classmethod
	def      _UpperCamelCase       (					cls						,					UpperCAmelCase_						,					**UpperCAmelCase_   )						->				List[str]:
			lowerCamelCase	:     List[str]            =       []
			for attribute_name in cls.attributes:
					lowerCamelCase	:     Any            =       getattr(cls						,					F"""{attribute_name}_class"""   )
					if isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
							lowerCamelCase	:     List[Any]            =       tuple(getattr(UpperCAmelCase_						,					UpperCAmelCase_   ) if n is not None else None for n in class_name   )
							lowerCamelCase	:     Optional[Any]            =       kwargs.get('use_fast'						,					UpperCAmelCase_   )
							if use_fast and classes[1] is not None:
									lowerCamelCase	:     Any            =       classes[1]
							else:
									lowerCamelCase	:     str            =       classes[0]
					else:
							lowerCamelCase	:     List[str]            =       getattr(UpperCAmelCase_						,					UpperCAmelCase_   )

					args.append(attribute_class.from_pretrained(UpperCAmelCase_						,					**UpperCAmelCase_   )   )
			return args




	@property
	def      _UpperCamelCase       (					self   )						->				List[Any]:
			lowerCamelCase	:     Any            =       getattr(self						,					self.attributes[0]   )
			return getattr(UpperCAmelCase_						,					'model_input_names'						,					UpperCAmelCase_   )


_A				=					copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
					_A				=					ProcessorMixin.push_to_hub.__doc__.format(
					    object='processor', object_class='AutoProcessor', object_files='processor files'
					)








"""simple docstring"""

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 			_lowercase  (							__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase							,			unittest.TestCase						):
	lowercase_								=      StableDiffusionImageVariationPipeline
	lowercase_								=      IMAGE_VARIATION_PARAMS
	lowercase_								=      IMAGE_VARIATION_BATCH_PARAMS
	lowercase_								=      frozenset([]						)
	# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	lowercase_								=      frozenset([]						)




	def      _UpperCamelCase       (					self   )						->				int:
			torch.manual_seed(0   )
			lowerCamelCase	:     List[str]            =       UNetaDConditionModel(
			    block_out_channels=(32, 64)						,					layers_per_block=2						,					sample_size=32						,					in_channels=4						,					out_channels=4						,					down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')						,					up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')						,					cross_attention_dim=32						,					)
			lowerCamelCase	:     List[Any]            =       PNDMScheduler(skip_prk_steps=UpperCAmelCase_   )
			torch.manual_seed(0   )
			lowerCamelCase	:     str            =       AutoencoderKL(
			    block_out_channels=[32, 64]						,					in_channels=3						,					out_channels=3						,					down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']						,					up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']						,					latent_channels=4						,					)
			torch.manual_seed(0   )
			lowerCamelCase	:     int            =       CLIPVisionConfig(
			    hidden_size=32						,					projection_dim=32						,					intermediate_size=37						,					layer_norm_eps=1E-0_5						,					num_attention_heads=4						,					num_hidden_layers=5						,					image_size=32						,					patch_size=4						,					)
			lowerCamelCase	:     Optional[int]            =       CLIPVisionModelWithProjection(UpperCAmelCase_   )
			lowerCamelCase	:     int            =       CLIPImageProcessor(crop_size=32						,					size=32   )

			lowerCamelCase	:     Any            =       {
			    'unet': unet,
			    'scheduler': scheduler,
			    'vae': vae,
			    'image_encoder': image_encoder,
			    'feature_extractor': feature_extractor,
			    'safety_checker': None,
			}
			return components




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_=0   )						->				Optional[int]:
			lowerCamelCase	:     int            =       floats_tensor((1, 3, 32, 32)						,					rng=random.Random(UpperCAmelCase_   )   )
			lowerCamelCase	:     List[str]            =       image.cpu().permute(0						,					2						,					3						,					1   )[0]
			lowerCamelCase	:     str            =       Image.fromarray(np.uinta(UpperCAmelCase_   )   ).convert('RGB'   ).resize((32, 32)   )
			if str(UpperCAmelCase_   ).startswith('mps'   ):
					lowerCamelCase	:     Tuple            =       torch.manual_seed(UpperCAmelCase_   )
			else:
					lowerCamelCase	:     int            =       torch.Generator(device=UpperCAmelCase_   ).manual_seed(UpperCAmelCase_   )
			lowerCamelCase	:     Optional[Any]            =       {
			    'image': image,
			    'generator': generator,
			    'num_inference_steps': 2,
			    'guidance_scale': 6.0,
			    'output_type': 'numpy',
			}
			return inputs




	def      _UpperCamelCase       (					self   )						->				str:
			lowerCamelCase	:     Tuple            =       'cpu'  # ensure determinism for the device-dependent torch.Generator
			lowerCamelCase	:     List[str]            =       self.get_dummy_components()
			lowerCamelCase	:     Tuple            =       StableDiffusionImageVariationPipeline(**UpperCAmelCase_   )
			lowerCamelCase	:     Optional[Any]            =       sd_pipe.to(UpperCAmelCase_   )
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

			lowerCamelCase	:     List[Any]            =       self.get_dummy_inputs(UpperCAmelCase_   )
			lowerCamelCase	:     Any            =       sd_pipe(**UpperCAmelCase_   ).images
			lowerCamelCase	:     Dict            =       image[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)
			lowerCamelCase	:     str            =       np.array([0.5239, 0.5723, 0.4796, 0.5049, 0.5550, 0.4685, 0.5329, 0.4891, 0.4921]   )

			assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3




	def      _UpperCamelCase       (					self   )						->				Any:
			lowerCamelCase	:     List[Any]            =       'cpu'  # ensure determinism for the device-dependent torch.Generator
			lowerCamelCase	:     List[str]            =       self.get_dummy_components()
			lowerCamelCase	:     Union[str, Any]            =       StableDiffusionImageVariationPipeline(**UpperCAmelCase_   )
			lowerCamelCase	:     Any            =       sd_pipe.to(UpperCAmelCase_   )
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

			lowerCamelCase	:     Any            =       self.get_dummy_inputs(UpperCAmelCase_   )
			lowerCamelCase	:     Any            =       2 * [inputs['image']]
			lowerCamelCase	:     Dict            =       sd_pipe(**UpperCAmelCase_   )

			lowerCamelCase	:     List[str]            =       output.images

			lowerCamelCase	:     Dict            =       image[-1, -3:, -3:, -1]

			assert image.shape == (2, 64, 64, 3)
			lowerCamelCase	:     Dict            =       np.array([0.6892, 0.5637, 0.5836, 0.5771, 0.6254, 0.6409, 0.5580, 0.5569, 0.5289]   )

			assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-3




	def      _UpperCamelCase       (					self   )						->				List[str]:
			super().test_inference_batch_single_identical(expected_max_diff=3E-3   )






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




	def      _UpperCamelCase       (					self   )						->				Dict:
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_="cpu"						,					UpperCAmelCase_=torch.floataa						,					UpperCAmelCase_=0   )						->				Optional[int]:
			lowerCamelCase	:     List[Any]            =       torch.Generator(device=UpperCAmelCase_   ).manual_seed(UpperCAmelCase_   )
			lowerCamelCase	:     List[str]            =       load_image(
			    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
			    '/stable_diffusion_imgvar/input_image_vermeer.png'   )
			lowerCamelCase	:     Union[str, Any]            =       np.random.RandomState(UpperCAmelCase_   ).standard_normal((1, 4, 64, 64)   )
			lowerCamelCase	:     int            =       torch.from_numpy(UpperCAmelCase_   ).to(device=UpperCAmelCase_						,					dtype=UpperCAmelCase_   )
			lowerCamelCase	:     Optional[Any]            =       {
			    'image': init_image,
			    'latents': latents,
			    'generator': generator,
			    'num_inference_steps': 3,
			    'guidance_scale': 7.5,
			    'output_type': 'numpy',
			}
			return inputs




	def      _UpperCamelCase       (					self   )						->				Optional[int]:
			lowerCamelCase	:     List[Any]            =       StableDiffusionImageVariationPipeline.from_pretrained(
			    'lambdalabs/sd-image-variations-diffusers'						,					safety_checker=UpperCAmelCase_   )
			lowerCamelCase	:     Dict            =       sd_pipe.to(UpperCAmelCase_   )
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

			lowerCamelCase	:     Optional[int]            =       self.get_inputs(UpperCAmelCase_   )
			lowerCamelCase	:     Any            =       sd_pipe(**UpperCAmelCase_   ).images
			lowerCamelCase	:     Union[str, Any]            =       image[0, -3:, -3:, -1].flatten()

			assert image.shape == (1, 512, 512, 3)
			lowerCamelCase	:     List[str]            =       np.array([0.84491, 0.90789, 0.75708, 0.78734, 0.83485, 0.70099, 0.66938, 0.68727, 0.61379]   )
			assert np.abs(image_slice - expected_slice   ).max() < 6E-3




	def      _UpperCamelCase       (					self   )						->				List[Any]:
			lowerCamelCase	:     Optional[Any]            =       0

			def callback_fn(UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   ) -> None:
					lowerCamelCase	:     int            =       True
					nonlocal number_of_steps
					number_of_steps += 1
					if step == 1:
							lowerCamelCase	:     List[Any]            =       latents.detach().cpu().numpy()
							assert latents.shape == (1, 4, 64, 64)
							lowerCamelCase	:     Dict            =       latents[0, -3:, -3:, -1]
							lowerCamelCase	:     List[Any]            =       np.array(
							    [-0.1621, 0.2837, -0.7979, -0.1221, -1.3057, 0.7681, -2.1191, 0.0464, 1.6309]   )

							assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 5E-2
					elif step == 2:
							lowerCamelCase	:     Union[str, Any]            =       latents.detach().cpu().numpy()
							assert latents.shape == (1, 4, 64, 64)
							lowerCamelCase	:     Optional[int]            =       latents[0, -3:, -3:, -1]
							lowerCamelCase	:     Optional[Any]            =       np.array([0.6299, 1.7500, 1.1992, -2.1582, -1.8994, 0.7334, -0.7090, 1.0137, 1.5273]   )

							assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 5E-2

			lowerCamelCase	:     List[str]            =       False

			lowerCamelCase	:     str            =       StableDiffusionImageVariationPipeline.from_pretrained(
			    'fusing/sd-image-variations-diffusers'						,					safety_checker=UpperCAmelCase_						,					torch_dtype=torch.floataa						,					)
			pipe.to(UpperCAmelCase_   )
			pipe.set_progress_bar_config(disable=UpperCAmelCase_   )
			pipe.enable_attention_slicing()

			lowerCamelCase	:     Dict            =       self.get_inputs(UpperCAmelCase_						,					dtype=torch.floataa   )
			pipe(**UpperCAmelCase_						,					callback=UpperCAmelCase_						,					callback_steps=1   )
			assert callback_fn.has_been_called
			assert number_of_steps == inputs["num_inference_steps"]




	def      _UpperCamelCase       (					self   )						->				str:
			torch.cuda.empty_cache()
			torch.cuda.reset_max_memory_allocated()
			torch.cuda.reset_peak_memory_stats()

			lowerCamelCase	:     Any            =       'fusing/sd-image-variations-diffusers'
			lowerCamelCase	:     Optional[int]            =       StableDiffusionImageVariationPipeline.from_pretrained(
			    UpperCAmelCase_						,					safety_checker=UpperCAmelCase_						,					torch_dtype=torch.floataa   )
			lowerCamelCase	:     Any            =       pipe.to(UpperCAmelCase_   )
			pipe.set_progress_bar_config(disable=UpperCAmelCase_   )
			pipe.enable_attention_slicing(1   )
			pipe.enable_sequential_cpu_offload()

			lowerCamelCase	:     Optional[int]            =       self.get_inputs(UpperCAmelCase_						,					dtype=torch.floataa   )
			lowerCamelCase	:     int            =       pipe(**UpperCAmelCase_   )

			lowerCamelCase	:     List[Any]            =       torch.cuda.max_memory_allocated()
			# make sure that less than 2.6 GB is allocated
			assert mem_bytes < 2.6 * 10**9






@nightly
@require_torch_gpu
class 			_lowercase  (							unittest.TestCase						):




	def      _UpperCamelCase       (					self   )						->				Dict:
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_="cpu"						,					UpperCAmelCase_=torch.floataa						,					UpperCAmelCase_=0   )						->				Any:
			lowerCamelCase	:     List[str]            =       torch.Generator(device=UpperCAmelCase_   ).manual_seed(UpperCAmelCase_   )
			lowerCamelCase	:     Optional[Any]            =       load_image(
			    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
			    '/stable_diffusion_imgvar/input_image_vermeer.png'   )
			lowerCamelCase	:     Union[str, Any]            =       np.random.RandomState(UpperCAmelCase_   ).standard_normal((1, 4, 64, 64)   )
			lowerCamelCase	:     List[Any]            =       torch.from_numpy(UpperCAmelCase_   ).to(device=UpperCAmelCase_						,					dtype=UpperCAmelCase_   )
			lowerCamelCase	:     List[Any]            =       {
			    'image': init_image,
			    'latents': latents,
			    'generator': generator,
			    'num_inference_steps': 50,
			    'guidance_scale': 7.5,
			    'output_type': 'numpy',
			}
			return inputs




	def      _UpperCamelCase       (					self   )						->				Any:
			lowerCamelCase	:     int            =       StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'   )
			sd_pipe.to(UpperCAmelCase_   )
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

			lowerCamelCase	:     int            =       self.get_inputs(UpperCAmelCase_   )
			lowerCamelCase	:     int            =       sd_pipe(**UpperCAmelCase_   ).images[0]

			lowerCamelCase	:     List[Any]            =       load_numpy(
			    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
			    '/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy'   )
			lowerCamelCase	:     str            =       np.abs(expected_image - image   ).max()
			assert max_diff < 1E-3




	def      _UpperCamelCase       (					self   )						->				List[str]:
			lowerCamelCase	:     List[Any]            =       StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'   )
			lowerCamelCase	:     Optional[int]            =       DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config   )
			sd_pipe.to(UpperCAmelCase_   )
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

			lowerCamelCase	:     Union[str, Any]            =       self.get_inputs(UpperCAmelCase_   )
			lowerCamelCase	:     Optional[Any]            =       25
			lowerCamelCase	:     List[str]            =       sd_pipe(**UpperCAmelCase_   ).images[0]

			lowerCamelCase	:     Tuple            =       load_numpy(
			    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
			    '/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy'   )
			lowerCamelCase	:     Dict            =       np.abs(expected_image - image   ).max()
			assert max_diff < 1E-3






