


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase		=						{
    """configuration_wav2vec2_conformer""": [
        """WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Wav2Vec2ConformerConfig""",
    ],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase		=						[
		    """WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """Wav2Vec2ConformerForAudioFrameClassification""",
		    """Wav2Vec2ConformerForCTC""",
		    """Wav2Vec2ConformerForPreTraining""",
		    """Wav2Vec2ConformerForSequenceClassification""",
		    """Wav2Vec2ConformerForXVector""",
		    """Wav2Vec2ConformerModel""",
		    """Wav2Vec2ConformerPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_wavaveca_conformer import (
		    WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    WavaVecaConformerConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_wavaveca_conformer import (
				    WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    WavaVecaConformerForAudioFrameClassification,
				    WavaVecaConformerForCTC,
				    WavaVecaConformerForPreTraining,
				    WavaVecaConformerForSequenceClassification,
				    WavaVecaConformerForXVector,
				    WavaVecaConformerModel,
				    WavaVecaConformerPreTrainedModel,
				)

else:
		import sys

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









"""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(					__A			,			__A			,			__A			,			unittest.TestCase       ):
    _A       = StableDiffusionControlNetInpaintPipeline
    _A       = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    _A       = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    _A       = frozenset({'''control_image'''}       )  # skip `image` and `mask` for now, only test for control_image
    _A       = TEXT_TO_IMAGE_IMAGE_PARAMS




    def   A   (   self			):





      '''simple docstring'''



      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			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			)
      _SCREAMING_SNAKE_CASE        =			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			)
      _SCREAMING_SNAKE_CASE        =			DDIMScheduler(
          beta_start=0.00_085    ,							beta_end=0.012    ,							beta_schedule='''scaled_linear'''    ,							clip_sample=A_    ,							set_alpha_to_one=A_    ,							)
      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			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			)
      _SCREAMING_SNAKE_CASE        =			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=1_000    ,							)
      _SCREAMING_SNAKE_CASE        =			CLIPTextModel(A_			)
      _SCREAMING_SNAKE_CASE        =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

      _SCREAMING_SNAKE_CASE        =			{
          '''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    ,							A_    ,							A_=0			):





      '''simple docstring'''



      if str(A_			).startswith('''mps'''			):
        _SCREAMING_SNAKE_CASE        =			torch.manual_seed(A_			)
      else:
        _SCREAMING_SNAKE_CASE        =			torch.Generator(device=A_			).manual_seed(A_			)

      _SCREAMING_SNAKE_CASE        =			2
      _SCREAMING_SNAKE_CASE        =			randn_tensor(
          (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)    ,							generator=A_    ,							device=torch.device(A_			)    ,							)
      _SCREAMING_SNAKE_CASE        =			floats_tensor((1, 3, 32, 32)    ,							rng=random.Random(A_			)			).to(A_			)
      _SCREAMING_SNAKE_CASE        =			init_image.cpu().permute(0    ,							2    ,							3    ,							1			)[0]

      _SCREAMING_SNAKE_CASE        =			Image.fromarray(np.uinta(A_			)			).convert('''RGB'''			).resize((64, 64)			)
      _SCREAMING_SNAKE_CASE        =			Image.fromarray(np.uinta(init_image + 4			)			).convert('''RGB'''			).resize((64, 64)			)

      _SCREAMING_SNAKE_CASE        =			{
          '''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			):





      '''simple docstring'''



      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			):





      '''simple docstring'''



      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3			)




    def   A   (   self			):





      '''simple docstring'''



      self._test_inference_batch_single_identical(expected_max_diff=2e-3			)




class    __snake_case(					__A       ):
    _A       = StableDiffusionControlNetInpaintPipeline
    _A       = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    _A       = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    _A       = frozenset([]       )




    def   A   (   self			):





      '''simple docstring'''



      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			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			)
      _SCREAMING_SNAKE_CASE        =			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			)
      _SCREAMING_SNAKE_CASE        =			DDIMScheduler(
          beta_start=0.00_085    ,							beta_end=0.012    ,							beta_schedule='''scaled_linear'''    ,							clip_sample=A_    ,							set_alpha_to_one=A_    ,							)
      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			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			)
      _SCREAMING_SNAKE_CASE        =			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=1_000    ,							)
      _SCREAMING_SNAKE_CASE        =			CLIPTextModel(A_			)
      _SCREAMING_SNAKE_CASE        =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

      _SCREAMING_SNAKE_CASE        =			{
          '''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(					__A			,			__A			,			unittest.TestCase       ):
    _A       = StableDiffusionControlNetInpaintPipeline
    _A       = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    _A       = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS




    def   A   (   self			):





      '''simple docstring'''



      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			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(A_			):
        if isinstance(A_    ,							torch.nn.Convad			):
          torch.nn.init.normal(m.weight			)
          m.bias.data.fill_(1.0			)

      _SCREAMING_SNAKE_CASE        =			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(A_			)

      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			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(A_			)

      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			DDIMScheduler(
          beta_start=0.00_085    ,							beta_end=0.012    ,							beta_schedule='''scaled_linear'''    ,							clip_sample=A_    ,							set_alpha_to_one=A_    ,							)
      torch.manual_seed(0			)
      _SCREAMING_SNAKE_CASE        =			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			)
      _SCREAMING_SNAKE_CASE        =			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=1_000    ,							)
      _SCREAMING_SNAKE_CASE        =			CLIPTextModel(A_			)
      _SCREAMING_SNAKE_CASE        =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

      _SCREAMING_SNAKE_CASE        =			MultiControlNetModel([controlneta, controlneta]			)

      _SCREAMING_SNAKE_CASE        =			{
          '''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    ,							A_    ,							A_=0			):





      '''simple docstring'''



      if str(A_			).startswith('''mps'''			):
        _SCREAMING_SNAKE_CASE        =			torch.manual_seed(A_			)
      else:
        _SCREAMING_SNAKE_CASE        =			torch.Generator(device=A_			).manual_seed(A_			)

      _SCREAMING_SNAKE_CASE        =			2

      _SCREAMING_SNAKE_CASE        =			[
          randn_tensor(
              (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)    ,							generator=A_    ,							device=torch.device(A_			)    ,							),
          randn_tensor(
              (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)    ,							generator=A_    ,							device=torch.device(A_			)    ,							),
      ]
      _SCREAMING_SNAKE_CASE        =			floats_tensor((1, 3, 32, 32)    ,							rng=random.Random(A_			)			).to(A_			)
      _SCREAMING_SNAKE_CASE        =			init_image.cpu().permute(0    ,							2    ,							3    ,							1			)[0]

      _SCREAMING_SNAKE_CASE        =			Image.fromarray(np.uinta(A_			)			).convert('''RGB'''			).resize((64, 64)			)
      _SCREAMING_SNAKE_CASE        =			Image.fromarray(np.uinta(init_image + 4			)			).convert('''RGB'''			).resize((64, 64)			)

      _SCREAMING_SNAKE_CASE        =			{
          '''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			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.get_dummy_components()
      _SCREAMING_SNAKE_CASE        =			self.pipeline_class(**A_			)
      pipe.to(A_			)

      _SCREAMING_SNAKE_CASE        =			10.0
      _SCREAMING_SNAKE_CASE        =			4

      _SCREAMING_SNAKE_CASE        =			self.get_dummy_inputs(A_			)
      _SCREAMING_SNAKE_CASE        =			steps
      _SCREAMING_SNAKE_CASE        =			scale
      _SCREAMING_SNAKE_CASE        =			pipe(**A_			)[0]

      _SCREAMING_SNAKE_CASE        =			self.get_dummy_inputs(A_			)
      _SCREAMING_SNAKE_CASE        =			steps
      _SCREAMING_SNAKE_CASE        =			scale
      _SCREAMING_SNAKE_CASE        =			pipe(**A_    ,							control_guidance_start=0.1    ,							control_guidance_end=0.2			)[0]

      _SCREAMING_SNAKE_CASE        =			self.get_dummy_inputs(A_			)
      _SCREAMING_SNAKE_CASE        =			steps
      _SCREAMING_SNAKE_CASE        =			scale
      _SCREAMING_SNAKE_CASE        =			pipe(**A_    ,							control_guidance_start=[0.1, 0.3]    ,							control_guidance_end=[0.2, 0.7]			)[0]

      _SCREAMING_SNAKE_CASE        =			self.get_dummy_inputs(A_			)
      _SCREAMING_SNAKE_CASE        =			steps
      _SCREAMING_SNAKE_CASE        =			scale
      _SCREAMING_SNAKE_CASE        =			pipe(**A_    ,							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			):





      '''simple docstring'''



      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			):





      '''simple docstring'''



      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3			)




    def   A   (   self			):





      '''simple docstring'''



      self._test_inference_batch_single_identical(expected_max_diff=2e-3			)




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.get_dummy_components()
      _SCREAMING_SNAKE_CASE        =			self.pipeline_class(**A_			)
      pipe.to(A_			)
      pipe.set_progress_bar_config(disable=A_			)
      with tempfile.TemporaryDirectory() as tmpdir:
        try:
          # save_pretrained is not implemented for Multi-ControlNet
          pipe.save_pretrained(A_			)
        except NotImplementedError:
          pass




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




    def   A   (   self			):





      '''simple docstring'''



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




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''			)

      _SCREAMING_SNAKE_CASE        =			StableDiffusionControlNetInpaintPipeline.from_pretrained(
          '''runwayml/stable-diffusion-inpainting'''    ,							safety_checker=A_    ,							controlnet=A_			)
      pipe.enable_model_cpu_offload()
      pipe.set_progress_bar_config(disable=A_			)

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

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

      _SCREAMING_SNAKE_CASE        =			'''pitch black hole'''

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

      _SCREAMING_SNAKE_CASE        =			pipe(
          A_    ,							image=A_    ,							mask_image=A_    ,							control_image=A_    ,							generator=A_    ,							output_type='''np'''    ,							num_inference_steps=3    ,							)

      _SCREAMING_SNAKE_CASE        =			output.images[0]

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

      _SCREAMING_SNAKE_CASE        =			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			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			ControlNetModel.from_pretrained('''lllyasviel/control_v11p_sd15_inpaint'''			)

      _SCREAMING_SNAKE_CASE        =			StableDiffusionControlNetInpaintPipeline.from_pretrained(
          '''runwayml/stable-diffusion-v1-5'''    ,							safety_checker=A_    ,							controlnet=A_			)
      _SCREAMING_SNAKE_CASE        =			DDIMScheduler.from_config(pipe.scheduler.config			)
      pipe.enable_model_cpu_offload()
      pipe.set_progress_bar_config(disable=A_			)

      _SCREAMING_SNAKE_CASE        =			torch.Generator(device='''cpu'''			).manual_seed(33			)

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

      _SCREAMING_SNAKE_CASE        =			load_image(
          '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'''			)
      _SCREAMING_SNAKE_CASE        =			mask_image.resize((512, 512)			)

      _SCREAMING_SNAKE_CASE        =			'''a handsome man with ray-ban sunglasses'''

      def make_inpaint_condition(A_    ,							A_			):
        _SCREAMING_SNAKE_CASE        =			np.array(image.convert('''RGB'''			)			).astype(np.floataa			) / 255.0
        _SCREAMING_SNAKE_CASE        =			np.array(image_mask.convert('''L'''			)			).astype(np.floataa			) / 255.0

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

      _SCREAMING_SNAKE_CASE        =			make_inpaint_condition(A_    ,							A_			)

      _SCREAMING_SNAKE_CASE        =			pipe(
          A_    ,							image=A_    ,							mask_image=A_    ,							control_image=A_    ,							guidance_scale=9.0    ,							eta=1.0    ,							generator=A_    ,							num_inference_steps=20    ,							output_type='''np'''    ,							)
      _SCREAMING_SNAKE_CASE        =			output.images[0]

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

      _SCREAMING_SNAKE_CASE        =			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
