
"""simple docstring"""


import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


lowerCAmelCase								=						[nn.LayerNorm]

lowerCAmelCase								=						logging.get_logger(__name__)

lowerCAmelCase								=						version.parse(version.parse(torch.__version__).base_version)

lowerCAmelCase								=						parsed_torch_version_base >= version.parse("""2.0""")
lowerCAmelCase								=						parsed_torch_version_base >= version.parse("""1.12""")
lowerCAmelCase								=						parsed_torch_version_base >= version.parse("""1.11""")
lowerCAmelCase								=						parsed_torch_version_base < version.parse("""1.11""")






def        lowerCAmelCase_       (		snake_case_		:      Union[str, Any]							,   snake_case_		:      Any							,   snake_case_		:      str							,   snake_case_		:      Optional[int]							,   self		:      int      )       ->Optional[int]:
			from torch import _softmax_backward_data

			if is_torch_less_than_1_11:
						return _softmax_backward_data(snake_case_							,   snake_case_							,   parent.dim							,   self      )
			else:
						return _softmax_backward_data(snake_case_							,   snake_case_							,   parent.dim							,   self.dtype      )






def        lowerCAmelCase_       (		snake_case_		:      nn.Linear							,   snake_case_		:      torch.LongTensor							,   snake_case_		:      int = 0      )       ->nn.Linear:
			lowerCamelCase__     :					Dict		=index.to(layer.weight.device      )
			lowerCamelCase__     :					Optional[Any]		=layer.weight.index_select(snake_case_							,   snake_case_      ).clone().detach()
			if layer.bias is not None:
						if dim == 1:
									lowerCamelCase__     :					int		=layer.bias.clone().detach()
						else:
									lowerCamelCase__     :					str		=layer.bias[index].clone().detach()
			lowerCamelCase__     :					Any		=list(layer.weight.size()      )
			lowerCamelCase__     :					Dict		=len(snake_case_      )
			lowerCamelCase__     :					Optional[int]		=nn.Linear(new_size[1]							,   new_size[0]							,   bias=layer.bias is not None      ).to(layer.weight.device      )
			lowerCamelCase__     :					Any		=False
			new_layer.weight.copy_(W.contiguous()      )
			lowerCamelCase__     :					str		=True
			if layer.bias is not None:
						lowerCamelCase__     :					Optional[int]		=False
						new_layer.bias.copy_(b.contiguous()      )
						lowerCamelCase__     :					int		=True
			return new_layer

class    A_     (					nn.Module       ):



							"""simple docstring"""






							def __init__(  self  :int					,							lowerCamelCase_  :str					,							lowerCamelCase_  :Dict	):





										"""simple docstring"""

										super().__init__()
										lowerCamelCase__     :					List[str]		=nf
										lowerCamelCase__     :					str		=nn.Parameter(torch.empty(lowerCamelCase_					,							lowerCamelCase_	)	)
										lowerCamelCase__     :					Union[str, Any]		=nn.Parameter(torch.zeros(lowerCamelCase_	)	)
										nn.init.normal_(self.weight					,							std=0.02	)





							def 				UpperCAmelCase__							(  self  :List[Any]					,							lowerCamelCase_  :Dict	):





										"""simple docstring"""

										lowerCamelCase__     :					List[str]		=x.size()[:-1] + (self.nf,)
										lowerCamelCase__     :					str		=torch.addmm(self.bias					,							x.view(-1					,							x.size(-1	)	)					,							self.weight	)
										lowerCamelCase__     :					Any		=x.view(lowerCamelCase_	)
										return x






def        lowerCAmelCase_       (		snake_case_		:      ConvaD							,   snake_case_		:      torch.LongTensor							,   snake_case_		:      int = 1      )       ->ConvaD:
			lowerCamelCase__     :					List[str]		=index.to(layer.weight.device      )
			lowerCamelCase__     :					Dict		=layer.weight.index_select(snake_case_							,   snake_case_      ).clone().detach()
			if dim == 0:
						lowerCamelCase__     :					Dict		=layer.bias.clone().detach()
			else:
						lowerCamelCase__     :					Optional[int]		=layer.bias[index].clone().detach()
			lowerCamelCase__     :					str		=list(layer.weight.size()      )
			lowerCamelCase__     :					Any		=len(snake_case_      )
			lowerCamelCase__     :					str		=ConvaD(new_size[1]							,   new_size[0]      ).to(layer.weight.device      )
			lowerCamelCase__     :					Dict		=False
			new_layer.weight.copy_(W.contiguous()      )
			lowerCamelCase__     :					Optional[Any]		=True
			lowerCamelCase__     :					List[Any]		=False
			new_layer.bias.copy_(b.contiguous()      )
			lowerCamelCase__     :					Union[str, Any]		=True
			return new_layer






def        lowerCAmelCase_       (		snake_case_		:      Union[nn.Linear, ConvaD]							,   snake_case_		:      torch.LongTensor							,   snake_case_		:      Optional[int] = None      )       ->Union[nn.Linear, ConvaD]:
			if isinstance(snake_case_							,   nn.Linear      ):
						return prune_linear_layer(snake_case_							,   snake_case_							,   dim=0 if dim is None else dim      )
			elif isinstance(snake_case_							,   snake_case_      ):
						return prune_convad_layer(snake_case_							,   snake_case_							,   dim=1 if dim is None else dim      )
			else:
						raise ValueError(f"""Can't prune layer of class {layer.__class__}"""      )






def        lowerCAmelCase_       (		snake_case_		:      Callable[..., torch.Tensor]							,   snake_case_		:      int							,   snake_case_		:      int							,   *snake_case_		:      Any      )       ->torch.Tensor:
			assert len(snake_case_      ) > 0, f"""{input_tensors} has to be a tuple/list of tensors"""

			# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
			lowerCamelCase__     :					Tuple		=len(inspect.signature(snake_case_      ).parameters      )
			if num_args_in_forward_chunk_fn != len(snake_case_      ):
						raise ValueError(
						    f"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(snake_case_      )} input """
						    'tensors are given'      )

			if chunk_size > 0:
						lowerCamelCase__     :					Any		=input_tensors[0].shape[chunk_dim]
						for input_tensor in input_tensors:
									if input_tensor.shape[chunk_dim] != tensor_shape:
												raise ValueError(
												    f"""All input tenors have to be of the same shape: {tensor_shape}, """
												    f"""found shape {input_tensor.shape[chunk_dim]}"""      )

						if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
									raise ValueError(
									    f"""The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk """
									    f"""size {chunk_size}"""      )

						lowerCamelCase__     :					str		=input_tensors[0].shape[chunk_dim] // chunk_size

						# chunk input tensor into tuples
						lowerCamelCase__     :					Optional[Any]		=tuple(input_tensor.chunk(snake_case_							,   dim=snake_case_      ) for input_tensor in input_tensors      )
						# apply forward fn to every tuple
						lowerCamelCase__     :					List[str]		=tuple(forward_fn(*snake_case_      ) for input_tensors_chunk in zip(*snake_case_      )      )
						# concatenate output at same dimension
						return torch.cat(snake_case_							,   dim=snake_case_      )

			return forward_fn(*snake_case_      )






def        lowerCAmelCase_       (		snake_case_		:      List[int]							,   snake_case_		:      int							,   snake_case_		:      int							,   snake_case_		:      Set[int]      )       ->Tuple[Set[int], torch.LongTensor]:
			lowerCamelCase__     :					Union[str, Any]		=torch.ones(snake_case_							,   snake_case_      )
			lowerCamelCase__     :					Optional[int]		=set(snake_case_      ) - already_pruned_heads  # Convert to set and remove already pruned heads
			for head in heads:
						# Compute how many pruned heads are before the head and move the index accordingly
						lowerCamelCase__     :					List[Any]		=head - sum(1 if h < head else 0 for h in already_pruned_heads      )
						lowerCamelCase__     :					List[str]		=0
			lowerCamelCase__     :					Any		=mask.view(-1      ).contiguous().eq(1      )
			lowerCamelCase__     :					torch.LongTensor		=torch.arange(len(snake_case_      )      )[mask].long()
			return heads, index






def        lowerCAmelCase_       (		*snake_case_		:      Union[torch.Tensor, List[torch.Tensor]]							,   snake_case_		:      Optional[str] = None      )       ->Tuple[torch.Tensor, ...]:
			return torch.meshgrid(*snake_case_							,   indexing=snake_case_      )






def        lowerCAmelCase_       (		snake_case_		:      torch.Tensor      )       ->Tuple[torch.device, int, int]:
			return tensor.device, storage_ptr(snake_case_      ), storage_size(snake_case_      )

"""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    A_     (					A__				, A__				, A__				, unittest.TestCase       ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_     				=       StableDiffusionImageVariationPipeline
							SCREAMING_SNAKE_CASE_     				=       IMAGE_VARIATION_PARAMS
							SCREAMING_SNAKE_CASE_     				=       IMAGE_VARIATION_BATCH_PARAMS
							SCREAMING_SNAKE_CASE_     				=       frozenset([]       )
							# TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							SCREAMING_SNAKE_CASE_     				=       frozenset([]       )





							def 				UpperCAmelCase__							(  self  :str	):





										"""simple docstring"""

										torch.manual_seed(0	)
										lowerCamelCase__     :					int		=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__     :					int		=PNDMScheduler(skip_prk_steps=lowerCamelCase_	)
										torch.manual_seed(0	)
										lowerCamelCase__     :					Optional[int]		=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__     :					Optional[int]		=CLIPVisionConfig(
										    hidden_size=32					,							projection_dim=32					,							intermediate_size=37					,							layer_norm_eps=1e-05					,							num_attention_heads=4					,							num_hidden_layers=5					,							image_size=32					,							patch_size=4					,							)
										lowerCamelCase__     :					Optional[Any]		=CLIPVisionModelWithProjection(lowerCamelCase_	)
										lowerCamelCase__     :					List[str]		=CLIPImageProcessor(crop_size=32					,							size=32	)

										lowerCamelCase__     :					Optional[int]		={
										    'unet': unet,
										    'scheduler': scheduler,
										    'vae': vae,
										    'image_encoder': image_encoder,
										    'feature_extractor': feature_extractor,
										    'safety_checker': None,
										}
										return components





							def 				UpperCAmelCase__							(  self  :List[Any]					,							lowerCamelCase_  :List[Any]					,							lowerCamelCase_  :Optional[int]=0	):





										"""simple docstring"""

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





							def 				UpperCAmelCase__							(  self  :Optional[int]	):





										"""simple docstring"""

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

										lowerCamelCase__     :					str		=self.get_dummy_inputs(lowerCamelCase_	)
										lowerCamelCase__     :					List[str]		=sd_pipe(**lowerCamelCase_	).images
										lowerCamelCase__     :					Tuple		=image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)
										lowerCamelCase__     :					str		=np.array([0.52_39, 0.57_23, 0.47_96, 0.50_49, 0.55_50, 0.46_85, 0.53_29, 0.48_91, 0.49_21]	)

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





							def 				UpperCAmelCase__							(  self  :Tuple	):





										"""simple docstring"""

										lowerCamelCase__     :					Any		='cpu'  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase__     :					Any		=self.get_dummy_components()
										lowerCamelCase__     :					Any		=StableDiffusionImageVariationPipeline(**lowerCamelCase_	)
										lowerCamelCase__     :					Dict		=sd_pipe.to(lowerCamelCase_	)
										sd_pipe.set_progress_bar_config(disable=lowerCamelCase_	)

										lowerCamelCase__     :					Any		=self.get_dummy_inputs(lowerCamelCase_	)
										lowerCamelCase__     :					Union[str, Any]		=2 * [inputs['image']]
										lowerCamelCase__     :					Optional[int]		=sd_pipe(**lowerCamelCase_	)

										lowerCamelCase__     :					str		=output.images

										lowerCamelCase__     :					str		=image[-1, -3:, -3:, -1]

										assert image.shape == (2, 64, 64, 3)
										lowerCamelCase__     :					int		=np.array([0.68_92, 0.56_37, 0.58_36, 0.57_71, 0.62_54, 0.64_09, 0.55_80, 0.55_69, 0.52_89]	)

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





							def 				UpperCAmelCase__							(  self  :List[str]	):





										"""simple docstring"""

										super().test_inference_batch_single_identical(expected_max_diff=3e-3	)






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



							"""simple docstring"""






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





										"""simple docstring"""

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





							def 				UpperCAmelCase__							(  self  :Union[str, Any]					,							lowerCamelCase_  :Optional[Any]					,							lowerCamelCase_  :Optional[Any]="cpu"					,							lowerCamelCase_  :Any=torch.floataa					,							lowerCamelCase_  :str=0	):





										"""simple docstring"""

										lowerCamelCase__     :					Union[str, Any]		=torch.Generator(device=lowerCamelCase_	).manual_seed(lowerCamelCase_	)
										lowerCamelCase__     :					Tuple		=load_image(
										    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
										    '/stable_diffusion_imgvar/input_image_vermeer.png'	)
										lowerCamelCase__     :					str		=np.random.RandomState(lowerCamelCase_	).standard_normal((1, 4, 64, 64)	)
										lowerCamelCase__     :					List[str]		=torch.from_numpy(lowerCamelCase_	).to(device=lowerCamelCase_					,							dtype=lowerCamelCase_	)
										lowerCamelCase__     :					str		={
										    'image': init_image,
										    'latents': latents,
										    'generator': generator,
										    'num_inference_steps': 3,
										    'guidance_scale': 7.5,
										    'output_type': 'numpy',
										}
										return inputs





							def 				UpperCAmelCase__							(  self  :str	):





										"""simple docstring"""

										lowerCamelCase__     :					Tuple		=StableDiffusionImageVariationPipeline.from_pretrained(
										    'lambdalabs/sd-image-variations-diffusers'					,							safety_checker=lowerCamelCase_	)
										lowerCamelCase__     :					List[Any]		=sd_pipe.to(lowerCamelCase_	)
										sd_pipe.set_progress_bar_config(disable=lowerCamelCase_	)

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

										assert image.shape == (1, 512, 512, 3)
										lowerCamelCase__     :					List[str]		=np.array([0.8_44_91, 0.9_07_89, 0.7_57_08, 0.7_87_34, 0.8_34_85, 0.7_00_99, 0.6_69_38, 0.6_87_27, 0.6_13_79]	)
										assert np.abs(image_slice - expected_slice	).max() < 6e-3





							def 				UpperCAmelCase__							(  self  :Optional[Any]	):





										"""simple docstring"""

										lowerCamelCase__     :					Optional[Any]		=0

										def callback_fn(lowerCamelCase_  :int					,							lowerCamelCase_  :int					,							lowerCamelCase_  :torch.FloatTensor	) -> None:
													lowerCamelCase__     :					Union[str, Any]		=True
													nonlocal number_of_steps
													number_of_steps += 1
													if step == 1:
																lowerCamelCase__     :					Dict		=latents.detach().cpu().numpy()
																assert latents.shape == (1, 4, 64, 64)
																lowerCamelCase__     :					str		=latents[0, -3:, -3:, -1]
																lowerCamelCase__     :					str		=np.array(
																    [-0.16_21, 0.28_37, -0.79_79, -0.12_21, -1.30_57, 0.76_81, -2.11_91, 0.04_64, 1.63_09]	)

																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__     :					Dict		=latents[0, -3:, -3:, -1]
																lowerCamelCase__     :					Optional[Any]		=np.array([0.62_99, 1.75_00, 1.19_92, -2.15_82, -1.89_94, 0.73_34, -0.70_90, 1.01_37, 1.52_73]	)

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

										lowerCamelCase__     :					str		=False

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

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





							def 				UpperCAmelCase__							(  self  :List[Any]	):





										"""simple docstring"""

										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										lowerCamelCase__     :					str		='fusing/sd-image-variations-diffusers'
										lowerCamelCase__     :					Union[str, Any]		=StableDiffusionImageVariationPipeline.from_pretrained(
										    lowerCamelCase_					,							safety_checker=lowerCamelCase_					,							torch_dtype=torch.floataa	)
										lowerCamelCase__     :					Any		=pipe.to(lowerCamelCase_	)
										pipe.set_progress_bar_config(disable=lowerCamelCase_	)
										pipe.enable_attention_slicing(1	)
										pipe.enable_sequential_cpu_offload()

										lowerCamelCase__     :					Dict		=self.get_inputs(lowerCamelCase_					,							dtype=torch.floataa	)
										lowerCamelCase__     :					Tuple		=pipe(**lowerCamelCase_	)

										lowerCamelCase__     :					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    A_     (					unittest.TestCase       ):



							"""simple docstring"""






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





										"""simple docstring"""

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





							def 				UpperCAmelCase__							(  self  :List[str]					,							lowerCamelCase_  :Optional[int]					,							lowerCamelCase_  :Union[str, Any]="cpu"					,							lowerCamelCase_  :Union[str, Any]=torch.floataa					,							lowerCamelCase_  :Union[str, Any]=0	):





										"""simple docstring"""

										lowerCamelCase__     :					Any		=torch.Generator(device=lowerCamelCase_	).manual_seed(lowerCamelCase_	)
										lowerCamelCase__     :					List[Any]		=load_image(
										    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
										    '/stable_diffusion_imgvar/input_image_vermeer.png'	)
										lowerCamelCase__     :					int		=np.random.RandomState(lowerCamelCase_	).standard_normal((1, 4, 64, 64)	)
										lowerCamelCase__     :					List[str]		=torch.from_numpy(lowerCamelCase_	).to(device=lowerCamelCase_					,							dtype=lowerCamelCase_	)
										lowerCamelCase__     :					str		={
										    'image': init_image,
										    'latents': latents,
										    'generator': generator,
										    'num_inference_steps': 50,
										    'guidance_scale': 7.5,
										    'output_type': 'numpy',
										}
										return inputs





							def 				UpperCAmelCase__							(  self  :Dict	):





										"""simple docstring"""

										lowerCamelCase__     :					List[str]		=StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'	)
										sd_pipe.to(lowerCamelCase_	)
										sd_pipe.set_progress_bar_config(disable=lowerCamelCase_	)

										lowerCamelCase__     :					Optional[Any]		=self.get_inputs(lowerCamelCase_	)
										lowerCamelCase__     :					Dict		=sd_pipe(**lowerCamelCase_	).images[0]

										lowerCamelCase__     :					Optional[int]		=load_numpy(
										    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
										    '/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy'	)
										lowerCamelCase__     :					Dict		=np.abs(expected_image - image	).max()
										assert max_diff < 1e-3





							def 				UpperCAmelCase__							(  self  :Tuple	):





										"""simple docstring"""

										lowerCamelCase__     :					List[Any]		=StableDiffusionImageVariationPipeline.from_pretrained('fusing/sd-image-variations-diffusers'	)
										lowerCamelCase__     :					Optional[Any]		=DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config	)
										sd_pipe.to(lowerCamelCase_	)
										sd_pipe.set_progress_bar_config(disable=lowerCamelCase_	)

										lowerCamelCase__     :					Tuple		=self.get_inputs(lowerCamelCase_	)
										lowerCamelCase__     :					int		=25
										lowerCamelCase__     :					Optional[int]		=sd_pipe(**lowerCamelCase_	).images[0]

										lowerCamelCase__     :					str		=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