




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase__  =		logging.get_logger(__name__)

UpperCamelCase__  =		{
    'nghuyong/ernie-1.0-base-zh': 'https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-2.0-base-en': 'https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json',
    'nghuyong/ernie-2.0-large-en': 'https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json',
    'nghuyong/ernie-3.0-base-zh': 'https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-medium-zh': 'https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-mini-zh': 'https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-micro-zh': 'https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-nano-zh': 'https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json',
    'nghuyong/ernie-gram-zh': 'https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json',
    'nghuyong/ernie-health-zh': 'https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json',
}


class        __SCREAMING_SNAKE_CASE							(	lowerCamelCase						):
					_lowerCamelCase							=				"""ernie"""

					def __init__(       self				, __lowercase=30_522				, __lowercase=768				, __lowercase=12				, __lowercase=12				, __lowercase=3_072				, __lowercase="gelu"				, __lowercase=0.1				, __lowercase=0.1				, __lowercase=512				, __lowercase=2				, __lowercase=3				, __lowercase=False				, __lowercase=0.02				, __lowercase=1E-12				, __lowercase=0				, __lowercase="absolute"				, __lowercase=True				, __lowercase=None				, **__lowercase				, ):







						'''simple docstring'''




						super().__init__(pad_token_id=__lowercase				, **__lowercase      )

						lowerCamelCase_							:	Tuple							     =							vocab_size
						lowerCamelCase_							:	str							     =							hidden_size
						lowerCamelCase_							:	List[Any]							     =							num_hidden_layers
						lowerCamelCase_							:	Dict							     =							num_attention_heads
						lowerCamelCase_							:	Dict							     =							hidden_act
						lowerCamelCase_							:	Optional[int]							     =							intermediate_size
						lowerCamelCase_							:	int							     =							hidden_dropout_prob
						lowerCamelCase_							:	Union[str, Any]							     =							attention_probs_dropout_prob
						lowerCamelCase_							:	Tuple							     =							max_position_embeddings
						lowerCamelCase_							:	Any							     =							type_vocab_size
						lowerCamelCase_							:	Union[str, Any]							     =							task_type_vocab_size
						lowerCamelCase_							:	List[str]							     =							use_task_id
						lowerCamelCase_							:	Optional[Any]							     =							initializer_range
						lowerCamelCase_							:	Tuple							     =							layer_norm_eps
						lowerCamelCase_							:	Any							     =							position_embedding_type
						lowerCamelCase_							:	Tuple							     =							use_cache
						lowerCamelCase_							:	Optional[int]							     =							classifier_dropout


class        __SCREAMING_SNAKE_CASE							(	lowerCamelCase						):

					@property
					def 			lowercase_							(       self      ):







						'''simple docstring'''




						if self.task == "multiple-choice":
							lowerCamelCase_							:	str							     =							{0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
							lowerCamelCase_							:	Any							     =							{0: 'batch', 1: 'sequence'}
						return OrderedDict(
						    [
						        ('input_ids', dynamic_axis),
						        ('attention_mask', dynamic_axis),
						        ('token_type_ids', dynamic_axis),
						        ('task_type_ids', dynamic_axis),
						    ]      )






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

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						torch.manual_seed(0      )
						lowerCamelCase_							:	List[str]							     =							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_							:	str							     =							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_							:	List[Any]							     =							DDIMScheduler(
						    beta_start=0.0_00_85				, beta_end=0.0_12				, beta_schedule='scaled_linear'				, clip_sample=__lowercase				, set_alpha_to_one=__lowercase				, )
						torch.manual_seed(0      )
						lowerCamelCase_							:	Dict							     =							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_							:	Dict							     =							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				, )
						lowerCamelCase_							:	str							     =							CLIPTextModel(__lowercase      )
						lowerCamelCase_							:	Dict							     =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

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

					def 			lowercase_							(       self				, __lowercase				, __lowercase=0      ):







						'''simple docstring'''




						if str(__lowercase      ).startswith('mps'      ):
							lowerCamelCase_							:	Optional[int]							     =							torch.manual_seed(__lowercase      )
						else:
							lowerCamelCase_							:	List[Any]							     =							torch.Generator(device=__lowercase      ).manual_seed(__lowercase      )

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

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

						lowerCamelCase_							:	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 			lowercase_							(       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 			lowercase_							(       self      ):







						'''simple docstring'''




						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3      )





					def 			lowercase_							(       self      ):







						'''simple docstring'''




						self._test_inference_batch_single_identical(expected_max_diff=2E-3      )


class        __SCREAMING_SNAKE_CASE							(	lowerCamelCase						):
					_lowerCamelCase							=				StableDiffusionControlNetInpaintPipeline
					_lowerCamelCase							=				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
					_lowerCamelCase							=				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					_lowerCamelCase							=				frozenset([]						)

					def 			lowercase_							(       self      ):







						'''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				, )
						torch.manual_seed(0      )
						lowerCamelCase_							:	Union[str, Any]							     =							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[Any]							     =							DDIMScheduler(
						    beta_start=0.0_00_85				, beta_end=0.0_12				, beta_schedule='scaled_linear'				, clip_sample=__lowercase				, set_alpha_to_one=__lowercase				, )
						torch.manual_seed(0      )
						lowerCamelCase_							:	List[Any]							     =							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_							:	str							     =							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				, )
						lowerCamelCase_							:	str							     =							CLIPTextModel(__lowercase      )
						lowerCamelCase_							:	Dict							     =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

						lowerCamelCase_							:	List[Any]							     =							{
						    'unet': unet,
						    'controlnet': controlnet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}
						return components


class        __SCREAMING_SNAKE_CASE							(	lowerCamelCase     ,  lowerCamelCase     ,  unittest.TestCase						):
					_lowerCamelCase							=				StableDiffusionControlNetInpaintPipeline
					_lowerCamelCase							=				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
					_lowerCamelCase							=				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						torch.manual_seed(0      )
						lowerCamelCase_							:	List[str]							     =							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(__lowercase      ):
							if isinstance(__lowercase				, torch.nn.Convad      ):
								torch.nn.init.normal(m.weight      )
								m.bias.data.fill_(1.0      )

						lowerCamelCase_							:	Union[str, Any]							     =							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(__lowercase      )

						torch.manual_seed(0      )
						lowerCamelCase_							:	Tuple							     =							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(__lowercase      )

						torch.manual_seed(0      )
						lowerCamelCase_							:	Optional[Any]							     =							DDIMScheduler(
						    beta_start=0.0_00_85				, beta_end=0.0_12				, beta_schedule='scaled_linear'				, clip_sample=__lowercase				, set_alpha_to_one=__lowercase				, )
						torch.manual_seed(0      )
						lowerCamelCase_							:	Union[str, Any]							     =							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_							:	Tuple							     =							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				, )
						lowerCamelCase_							:	List[Any]							     =							CLIPTextModel(__lowercase      )
						lowerCamelCase_							:	Tuple							     =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'      )

						lowerCamelCase_							:	Union[str, Any]							     =							MultiControlNetModel([controlneta, controlneta]      )

						lowerCamelCase_							:	Optional[Any]							     =							{
						    'unet': unet,
						    'controlnet': controlnet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}
						return components

					def 			lowercase_							(       self				, __lowercase				, __lowercase=0      ):







						'''simple docstring'''




						if str(__lowercase      ).startswith('mps'      ):
							lowerCamelCase_							:	List[Any]							     =							torch.manual_seed(__lowercase      )
						else:
							lowerCamelCase_							:	Optional[Any]							     =							torch.Generator(device=__lowercase      ).manual_seed(__lowercase      )

						lowerCamelCase_							:	Optional[int]							     =							2

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

						lowerCamelCase_							:	int							     =							Image.fromarray(np.uinta(__lowercase      )      ).convert('RGB'      ).resize((64, 64)      )
						lowerCamelCase_							:	str							     =							Image.fromarray(np.uinta(init_image + 4      )      ).convert('RGB'      ).resize((64, 64)      )

						lowerCamelCase_							:	Tuple							     =							{
						    '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 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Tuple							     =							self.get_dummy_components()
						lowerCamelCase_							:	Any							     =							self.pipeline_class(**__lowercase      )
						pipe.to(__lowercase      )

						lowerCamelCase_							:	int							     =							10.0
						lowerCamelCase_							:	int							     =							4

						lowerCamelCase_							:	Optional[int]							     =							self.get_dummy_inputs(__lowercase      )
						lowerCamelCase_							:	Tuple							     =							steps
						lowerCamelCase_							:	List[Any]							     =							scale
						lowerCamelCase_							:	Any							     =							pipe(**__lowercase      )[0]

						lowerCamelCase_							:	Optional[int]							     =							self.get_dummy_inputs(__lowercase      )
						lowerCamelCase_							:	Optional[Any]							     =							steps
						lowerCamelCase_							:	Dict							     =							scale
						lowerCamelCase_							:	Dict							     =							pipe(**__lowercase				, control_guidance_start=0.1				, control_guidance_end=0.2      )[0]

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

						lowerCamelCase_							:	Union[str, Any]							     =							self.get_dummy_inputs(__lowercase      )
						lowerCamelCase_							:	str							     =							steps
						lowerCamelCase_							:	Optional[int]							     =							scale
						lowerCamelCase_							:	str							     =							pipe(**__lowercase				, 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 			lowercase_							(       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 			lowercase_							(       self      ):







						'''simple docstring'''




						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3      )

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						self._test_inference_batch_single_identical(expected_max_diff=2E-3      )





					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[Any]							     =							self.get_dummy_components()
						lowerCamelCase_							:	Optional[int]							     =							self.pipeline_class(**__lowercase      )
						pipe.to(__lowercase      )
						pipe.set_progress_bar_config(disable=__lowercase      )
						with tempfile.TemporaryDirectory() as tmpdir:
							try:
								# save_pretrained is not implemented for Multi-ControlNet
								pipe.save_pretrained(__lowercase      )
							except NotImplementedError:
								pass


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

					def 			lowercase_							(       self      ):







						'''simple docstring'''




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

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Optional[int]							     =							ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'      )

						lowerCamelCase_							:	Union[str, Any]							     =							StableDiffusionControlNetInpaintPipeline.from_pretrained(
						    'runwayml/stable-diffusion-inpainting'				, safety_checker=__lowercase				, controlnet=__lowercase      )
						pipe.enable_model_cpu_offload()
						pipe.set_progress_bar_config(disable=__lowercase      )

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

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

						lowerCamelCase_							:	Tuple							     =							'pitch black hole'

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

						lowerCamelCase_							:	str							     =							pipe(
						    __lowercase				, image=__lowercase				, mask_image=__lowercase				, control_image=__lowercase				, generator=__lowercase				, output_type='np'				, num_inference_steps=3				, )

						lowerCamelCase_							:	Optional[Any]							     =							output.images[0]

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

						lowerCamelCase_							:	Optional[int]							     =							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 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Dict							     =							ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'      )

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

						lowerCamelCase_							:	Tuple							     =							torch.Generator(device='cpu'      ).manual_seed(33      )

						lowerCamelCase_							:	Dict							     =							load_image(
						    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'      )
						lowerCamelCase_							:	Union[str, Any]							     =							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_							:	int							     =							mask_image.resize((512, 512)      )

						lowerCamelCase_							:	Tuple							     =							'a handsome man with ray-ban sunglasses'

						def make_inpaint_condition(__lowercase				, __lowercase      ):
							lowerCamelCase_							:	str							     =							np.array(image.convert('RGB'      )      ).astype(np.floataa      ) / 2_55.0
							lowerCamelCase_							:	str							     =							np.array(image_mask.convert('L'      )      ).astype(np.floataa      ) / 2_55.0

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

						lowerCamelCase_							:	Optional[int]							     =							make_inpaint_condition(__lowercase				, __lowercase      )

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

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

						lowerCamelCase_							:	Dict							     =							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
