
'''simple docstring'''






# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()

class 							_snake_case     (  lowerCAmelCase_						,    lowerCAmelCase_						,    lowerCAmelCase_						,    unittest.TestCase ):
	"""simple docstring"""





	_UpperCamelCase							:  int								=		StableDiffusionControlNetInpaintPipeline
	_UpperCamelCase							:  List[str]								=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	_UpperCamelCase							:  Optional[Any]								=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	_UpperCamelCase							:  Dict								=		frozenset({'''control_image'''} )  # skip `image` and `mask` for now, only test for control_image
	_UpperCamelCase							:  List[Any]								=		TEXT_TO_IMAGE_IMAGE_PARAMS




	def 	__A      (						self				: Union[str, Any]      ):
			torch.manual_seed(0      )
			lowerCAmelCase_   :       Dict    							=UNetaDConditionModel(
			    block_out_channels=(32, 64)					,						layers_per_block=2					,						sample_size=32					,						in_channels=9					,						out_channels=4					,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,						up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')					,						cross_attention_dim=32					,						)
			torch.manual_seed(0      )
			lowerCAmelCase_   :       Optional[int]    							=ControlNetModel(
			    block_out_channels=(32, 64)					,						layers_per_block=2					,						in_channels=4					,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,						cross_attention_dim=32					,						conditioning_embedding_out_channels=(16, 32)					,						)
			torch.manual_seed(0      )
			lowerCAmelCase_   :       Union[str, Any]    							=DDIMScheduler(
			    beta_start=0.0_0_0_8_5					,						beta_end=0.0_1_2					,						beta_schedule='''scaled_linear'''					,						clip_sample=UpperCamelCase_					,						set_alpha_to_one=UpperCamelCase_					,						)
			torch.manual_seed(0      )
			lowerCAmelCase_   :       List[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_   :       Optional[Any]    							=CLIPTextConfig(
			    bos_token_id=0					,						eos_token_id=2					,						hidden_size=32					,						intermediate_size=37					,						layer_norm_eps=1E-05					,						num_attention_heads=4					,						num_hidden_layers=5					,						pad_token_id=1					,						vocab_size=1000					,						)
			lowerCAmelCase_   :       Optional[Any]    							=CLIPTextModel(UpperCamelCase_      )
			lowerCAmelCase_   :       Any    							=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

			lowerCAmelCase_   :       str    							={
			    '''unet''': unet,
			    '''controlnet''': controlnet,
			    '''scheduler''': scheduler,
			    '''vae''': vae,
			    '''text_encoder''': text_encoder,
			    '''tokenizer''': tokenizer,
			    '''safety_checker''': None,
			    '''feature_extractor''': None,
			}
			return components




	def 	__A      (						self				: Any					,						UpperCamelCase_				: List[str]					,						UpperCamelCase_				: Optional[int]=0      ):
			if str(UpperCamelCase_      ).startswith('''mps'''      ):
					lowerCAmelCase_   :       Any    							=torch.manual_seed(UpperCamelCase_      )
			else:
					lowerCAmelCase_   :       Tuple    							=torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )

			lowerCAmelCase_   :       List[Any]    							=2
			lowerCAmelCase_   :       Tuple    							=randn_tensor(
			    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					,						generator=UpperCamelCase_					,						device=torch.device(UpperCamelCase_      )					,						)
			lowerCAmelCase_   :       Dict    							=floats_tensor((1, 3, 32, 32)					,						rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
			lowerCAmelCase_   :       Any    							=init_image.cpu().permute(0					,						2					,						3					,						1      )[0]

			lowerCAmelCase_   :       Any    							=Image.fromarray(np.uinta(UpperCamelCase_      )      ).convert('''RGB'''      ).resize((64, 64)      )
			lowerCAmelCase_   :       Any    							=Image.fromarray(np.uinta(init_image + 4      )      ).convert('''RGB'''      ).resize((64, 64)      )

			lowerCAmelCase_   :       Optional[Any]    							={
			    '''prompt''': '''A painting of a squirrel eating a burger''',
			    '''generator''': generator,
			    '''num_inference_steps''': 2,
			    '''guidance_scale''': 6.0,
			    '''output_type''': '''numpy''',
			    '''image''': image,
			    '''mask_image''': mask_image,
			    '''control_image''': control_image,
			}

			return inputs




	def 	__A      (						self				: Any      ):
			return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3      )




	@unittest.skipIf(
	    torch_device != '''cuda''' or not is_xformers_available()					,						reason='''XFormers attention is only available with CUDA and `xformers` installed'''					,						)
	def 	__A      (						self				: int      ):
			self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3      )





	def 	__A      (						self				: Any      ):
			self._test_inference_batch_single_identical(expected_max_diff=2E-3      )







class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





	_UpperCamelCase							:  int								=		StableDiffusionControlNetInpaintPipeline
	_UpperCamelCase							:  int								=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	_UpperCamelCase							:  str								=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	_UpperCamelCase							:  Dict								=		frozenset([] )




	def 	__A      (						self				: str      ):
			torch.manual_seed(0      )
			lowerCAmelCase_   :       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					,						)
			torch.manual_seed(0      )
			lowerCAmelCase_   :       int    							=ControlNetModel(
			    block_out_channels=(32, 64)					,						layers_per_block=2					,						in_channels=4					,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,						cross_attention_dim=32					,						conditioning_embedding_out_channels=(16, 32)					,						)
			torch.manual_seed(0      )
			lowerCAmelCase_   :       Optional[int]    							=DDIMScheduler(
			    beta_start=0.0_0_0_8_5					,						beta_end=0.0_1_2					,						beta_schedule='''scaled_linear'''					,						clip_sample=UpperCamelCase_					,						set_alpha_to_one=UpperCamelCase_					,						)
			torch.manual_seed(0      )
			lowerCAmelCase_   :       Tuple    							=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]    							=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					,						)
			lowerCAmelCase_   :       str    							=CLIPTextModel(UpperCamelCase_      )
			lowerCAmelCase_   :       List[str]    							=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

			lowerCAmelCase_   :       Tuple    							={
			    '''unet''': unet,
			    '''controlnet''': controlnet,
			    '''scheduler''': scheduler,
			    '''vae''': vae,
			    '''text_encoder''': text_encoder,
			    '''tokenizer''': tokenizer,
			    '''safety_checker''': None,
			    '''feature_extractor''': None,
			}
			return components







class 							_snake_case     (  lowerCAmelCase_						,    lowerCAmelCase_						,    unittest.TestCase ):
	"""simple docstring"""





	_UpperCamelCase							:  Any								=		StableDiffusionControlNetInpaintPipeline
	_UpperCamelCase							:  Optional[Any]								=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	_UpperCamelCase							:  Union[str, Any]								=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS




	def 	__A      (						self				: int      ):
			torch.manual_seed(0      )
			lowerCAmelCase_   :       Any    							=UNetaDConditionModel(
			    block_out_channels=(32, 64)					,						layers_per_block=2					,						sample_size=32					,						in_channels=9					,						out_channels=4					,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,						up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')					,						cross_attention_dim=32					,						)
			torch.manual_seed(0      )

			def init_weights(UpperCamelCase_				: Any      ):
					if isinstance(UpperCamelCase_					,						torch.nn.Convad      ):
							torch.nn.init.normal(m.weight      )
							m.bias.data.fill_(1.0      )

			lowerCAmelCase_   :       int    							=ControlNetModel(
			    block_out_channels=(32, 64)					,						layers_per_block=2					,						in_channels=4					,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,						cross_attention_dim=32					,						conditioning_embedding_out_channels=(16, 32)					,						)
			controlneta.controlnet_down_blocks.apply(UpperCamelCase_      )

			torch.manual_seed(0      )
			lowerCAmelCase_   :       Dict    							=ControlNetModel(
			    block_out_channels=(32, 64)					,						layers_per_block=2					,						in_channels=4					,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,						cross_attention_dim=32					,						conditioning_embedding_out_channels=(16, 32)					,						)
			controlneta.controlnet_down_blocks.apply(UpperCamelCase_      )

			torch.manual_seed(0      )
			lowerCAmelCase_   :       Optional[Any]    							=DDIMScheduler(
			    beta_start=0.0_0_0_8_5					,						beta_end=0.0_1_2					,						beta_schedule='''scaled_linear'''					,						clip_sample=UpperCamelCase_					,						set_alpha_to_one=UpperCamelCase_					,						)
			torch.manual_seed(0      )
			lowerCAmelCase_   :       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_   :       List[Any]    							=CLIPTextConfig(
			    bos_token_id=0					,						eos_token_id=2					,						hidden_size=32					,						intermediate_size=37					,						layer_norm_eps=1E-05					,						num_attention_heads=4					,						num_hidden_layers=5					,						pad_token_id=1					,						vocab_size=1000					,						)
			lowerCAmelCase_   :       Tuple    							=CLIPTextModel(UpperCamelCase_      )
			lowerCAmelCase_   :       Union[str, Any]    							=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

			lowerCAmelCase_   :       Any    							=MultiControlNetModel([controlneta, controlneta]      )

			lowerCAmelCase_   :       Tuple    							={
			    '''unet''': unet,
			    '''controlnet''': controlnet,
			    '''scheduler''': scheduler,
			    '''vae''': vae,
			    '''text_encoder''': text_encoder,
			    '''tokenizer''': tokenizer,
			    '''safety_checker''': None,
			    '''feature_extractor''': None,
			}
			return components




	def 	__A      (						self				: Tuple					,						UpperCamelCase_				: Union[str, Any]					,						UpperCamelCase_				: Union[str, Any]=0      ):
			if str(UpperCamelCase_      ).startswith('''mps'''      ):
					lowerCAmelCase_   :       Dict    							=torch.manual_seed(UpperCamelCase_      )
			else:
					lowerCAmelCase_   :       List[str]    							=torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )

			lowerCAmelCase_   :       Dict    							=2

			lowerCAmelCase_   :       Any    							=[
			    randn_tensor(
			        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					,						generator=UpperCamelCase_					,						device=torch.device(UpperCamelCase_      )					,						),
			    randn_tensor(
			        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					,						generator=UpperCamelCase_					,						device=torch.device(UpperCamelCase_      )					,						),
			]
			lowerCAmelCase_   :       Optional[int]    							=floats_tensor((1, 3, 32, 32)					,						rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
			lowerCAmelCase_   :       Union[str, Any]    							=init_image.cpu().permute(0					,						2					,						3					,						1      )[0]

			lowerCAmelCase_   :       List[Any]    							=Image.fromarray(np.uinta(UpperCamelCase_      )      ).convert('''RGB'''      ).resize((64, 64)      )
			lowerCAmelCase_   :       List[str]    							=Image.fromarray(np.uinta(init_image + 4      )      ).convert('''RGB'''      ).resize((64, 64)      )

			lowerCAmelCase_   :       int    							={
			    '''prompt''': '''A painting of a squirrel eating a burger''',
			    '''generator''': generator,
			    '''num_inference_steps''': 2,
			    '''guidance_scale''': 6.0,
			    '''output_type''': '''numpy''',
			    '''image''': image,
			    '''mask_image''': mask_image,
			    '''control_image''': control_image,
			}

			return inputs




	def 	__A      (						self				: Any      ):
			lowerCAmelCase_   :       Optional[int]    							=self.get_dummy_components()
			lowerCAmelCase_   :       Tuple    							=self.pipeline_class(**UpperCamelCase_      )
			pipe.to(UpperCamelCase_      )

			lowerCAmelCase_   :       Tuple    							=1_0.0
			lowerCAmelCase_   :       List[Any]    							=4

			lowerCAmelCase_   :       Optional[int]    							=self.get_dummy_inputs(UpperCamelCase_      )
			lowerCAmelCase_   :       Tuple    							=steps
			lowerCAmelCase_   :       Union[str, Any]    							=scale
			lowerCAmelCase_   :       Optional[Any]    							=pipe(**UpperCamelCase_      )[0]

			lowerCAmelCase_   :       Optional[int]    							=self.get_dummy_inputs(UpperCamelCase_      )
			lowerCAmelCase_   :       Optional[int]    							=steps
			lowerCAmelCase_   :       str    							=scale
			lowerCAmelCase_   :       Tuple    							=pipe(**UpperCamelCase_					,						control_guidance_start=0.1					,						control_guidance_end=0.2      )[0]

			lowerCAmelCase_   :       Tuple    							=self.get_dummy_inputs(UpperCamelCase_      )
			lowerCAmelCase_   :       Optional[int]    							=steps
			lowerCAmelCase_   :       List[Any]    							=scale
			lowerCAmelCase_   :       Optional[Any]    							=pipe(**UpperCamelCase_					,						control_guidance_start=[0.1, 0.3]					,						control_guidance_end=[0.2, 0.7]      )[0]

			lowerCAmelCase_   :       int    							=self.get_dummy_inputs(UpperCamelCase_      )
			lowerCAmelCase_   :       Optional[Any]    							=steps
			lowerCAmelCase_   :       Union[str, Any]    							=scale
			lowerCAmelCase_   :       Dict    							=pipe(**UpperCamelCase_					,						control_guidance_start=0.4					,						control_guidance_end=[0.5, 0.8]      )[0]

			# make sure that all outputs are different
			assert np.sum(np.abs(output_a - output_a      )      ) > 1E-3
			assert np.sum(np.abs(output_a - output_a      )      ) > 1E-3
			assert np.sum(np.abs(output_a - output_a      )      ) > 1E-3




	def 	__A      (						self				: Dict      ):
			return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3      )




	@unittest.skipIf(
	    torch_device != '''cuda''' or not is_xformers_available()					,						reason='''XFormers attention is only available with CUDA and `xformers` installed'''					,						)
	def 	__A      (						self				: Any      ):
			self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3      )




	def 	__A      (						self				: Optional[int]      ):
			self._test_inference_batch_single_identical(expected_max_diff=2E-3      )





	def 	__A      (						self				: str      ):
			lowerCAmelCase_   :       Tuple    							=self.get_dummy_components()
			lowerCAmelCase_   :       Dict    							=self.pipeline_class(**UpperCamelCase_      )
			pipe.to(UpperCamelCase_      )
			pipe.set_progress_bar_config(disable=UpperCamelCase_      )
			with tempfile.TemporaryDirectory() as tmpdir:
					try:
							# save_pretrained is not implemented for Multi-ControlNet
							pipe.save_pretrained(UpperCamelCase_      )
					except NotImplementedError:
							pass







@slow
@require_torch_gpu
class 							_snake_case     (  unittest.TestCase ):
	"""simple docstring"""





	def 	__A      (						self				: Optional[Any]      ):
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()




	def 	__A      (						self				: str      ):
			lowerCAmelCase_   :       Union[str, Any]    							=ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''      )

			lowerCAmelCase_   :       Optional[int]    							=StableDiffusionControlNetInpaintPipeline.from_pretrained(
			    '''runwayml/stable-diffusion-inpainting'''					,						safety_checker=UpperCamelCase_					,						controlnet=UpperCamelCase_      )
			pipe.enable_model_cpu_offload()
			pipe.set_progress_bar_config(disable=UpperCamelCase_      )

			lowerCAmelCase_   :       Any    							=torch.Generator(device='''cpu'''      ).manual_seed(0      )
			lowerCAmelCase_   :       List[Any]    							=load_image(
			    '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''      ).resize((512, 512)      )

			lowerCAmelCase_   :       List[Any]    							=load_image(
			    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
			    '''/stable_diffusion_inpaint/input_bench_mask.png'''      ).resize((512, 512)      )

			lowerCAmelCase_   :       List[str]    							='''pitch black hole'''

			lowerCAmelCase_   :       Tuple    							=load_image(
			    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''      ).resize((512, 512)      )

			lowerCAmelCase_   :       Tuple    							=pipe(
			    UpperCamelCase_					,						image=UpperCamelCase_					,						mask_image=UpperCamelCase_					,						control_image=UpperCamelCase_					,						generator=UpperCamelCase_					,						output_type='''np'''					,						num_inference_steps=3					,						)

			lowerCAmelCase_   :       Union[str, Any]    							=output.images[0]

			assert image.shape == (512, 512, 3)

			lowerCAmelCase_   :       str    							=load_numpy(
			    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy'''      )

			assert np.abs(expected_image - image      ).max() < 9E-2





	def 	__A      (						self				: List[str]      ):
			lowerCAmelCase_   :       List[str]    							=ControlNetModel.from_pretrained('''lllyasviel/control_v11p_sd15_inpaint'''      )

			lowerCAmelCase_   :       Union[str, Any]    							=StableDiffusionControlNetInpaintPipeline.from_pretrained(
			    '''runwayml/stable-diffusion-v1-5'''					,						safety_checker=UpperCamelCase_					,						controlnet=UpperCamelCase_      )
			lowerCAmelCase_   :       Optional[int]    							=DDIMScheduler.from_config(pipe.scheduler.config      )
			pipe.enable_model_cpu_offload()
			pipe.set_progress_bar_config(disable=UpperCamelCase_      )

			lowerCAmelCase_   :       List[Any]    							=torch.Generator(device='''cpu'''      ).manual_seed(33      )

			lowerCAmelCase_   :       Any    							=load_image(
			    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'''      )
			lowerCAmelCase_   :       Dict    							=init_image.resize((512, 512)      )

			lowerCAmelCase_   :       Optional[int]    							=load_image(
			    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'''      )
			lowerCAmelCase_   :       Any    							=mask_image.resize((512, 512)      )

			lowerCAmelCase_   :       List[str]    							='''a handsome man with ray-ban sunglasses'''

			def make_inpaint_condition(UpperCamelCase_				: Any					,						UpperCamelCase_				: List[str]      ):
					lowerCAmelCase_   :       Union[str, Any]    							=np.array(image.convert('''RGB'''      )      ).astype(np.floataa      ) / 2_5_5.0
					lowerCAmelCase_   :       Dict    							=np.array(image_mask.convert('''L'''      )      ).astype(np.floataa      ) / 2_5_5.0

					assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
					lowerCAmelCase_   :       Union[str, Any]    							=-1.0  # set as masked pixel
					lowerCAmelCase_   :       Any    							=np.expand_dims(UpperCamelCase_					,						0      ).transpose(0					,						3					,						1					,						2      )
					lowerCAmelCase_   :       List[str]    							=torch.from_numpy(UpperCamelCase_      )
					return image

			lowerCAmelCase_   :       Optional[int]    							=make_inpaint_condition(UpperCamelCase_					,						UpperCamelCase_      )

			lowerCAmelCase_   :       List[str]    							=pipe(
			    UpperCamelCase_					,						image=UpperCamelCase_					,						mask_image=UpperCamelCase_					,						control_image=UpperCamelCase_					,						guidance_scale=9.0					,						eta=1.0					,						generator=UpperCamelCase_					,						num_inference_steps=20					,						output_type='''np'''					,						)
			lowerCAmelCase_   :       List[str]    							=output.images[0]

			assert image.shape == (512, 512, 3)

			lowerCAmelCase_   :       Union[str, Any]    							=load_numpy(
			    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy'''      )

			assert np.abs(expected_image - image      ).max() < 9E-2








'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowercase	=    {'''configuration_detr''': ['''DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DetrConfig''', '''DetrOnnxConfig''']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    ['''DetrFeatureExtractor''']
							__lowercase	=    ['''DetrImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''DetrForObjectDetection''',
							    '''DetrForSegmentation''',
							    '''DetrModel''',
							    '''DetrPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_detr import DetrFeatureExtractor
														from .image_processing_detr import DetrImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_detr import (
														    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DetrForObjectDetection,
														    DetrForSegmentation,
														    DetrModel,
														    DetrPreTrainedModel,
														)

else:
							import sys

							__lowercase	=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






