# 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  __lowerCAmelCase	(							a_,						a_,						a_,						unittest.TestCase    ):



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

 def        __snake_case       (      self							:		Optional[Any]	)			->				Tuple:
        torch.manual_seed(0	)
        _UpperCamelCase  :							Optional[int]	    =	UNetaDConditionModel(
            block_out_channels=(3_2, 6_4)							,      layers_per_block=2							,      sample_size=3_2							,      in_channels=9							,      out_channels=4							,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')							,      cross_attention_dim=3_2							,      )
        torch.manual_seed(0	)
        _UpperCamelCase  :							str	    =	ControlNetModel(
            block_out_channels=(3_2, 6_4)							,      layers_per_block=2							,      in_channels=4							,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,      cross_attention_dim=3_2							,      conditioning_embedding_out_channels=(1_6, 3_2)							,      )
        torch.manual_seed(0	)
        _UpperCamelCase  :							Tuple	    =	DDIMScheduler(
            beta_start=0.0_0_0_8_5							,      beta_end=0.0_1_2							,      beta_schedule='scaled_linear'							,      clip_sample=lowerCamelCase							,      set_alpha_to_one=lowerCamelCase							,      )
        torch.manual_seed(0	)
        _UpperCamelCase  :							List[Any]	    =	AutoencoderKL(
            block_out_channels=[3_2, 6_4]							,      in_channels=3							,      out_channels=3							,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']							,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']							,      latent_channels=4							,      )
        torch.manual_seed(0	)
        _UpperCamelCase  :							Any	    =	CLIPTextConfig(
            bos_token_id=0							,      eos_token_id=2							,      hidden_size=3_2							,      intermediate_size=3_7							,      layer_norm_eps=1e-05							,      num_attention_heads=4							,      num_hidden_layers=5							,      pad_token_id=1							,      vocab_size=1_0_0_0							,      )
        _UpperCamelCase  :							int	    =	CLIPTextModel(lowerCamelCase	)
        _UpperCamelCase  :							str	    =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

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

 def        __snake_case       (      self							:		Union[str, Any]							,      lowerCamelCase							:		Union[str, Any]							,      lowerCamelCase							:		Optional[int]=0	)			->				Any:
        if str(lowerCamelCase	).startswith('mps'	):
               _UpperCamelCase  :							List[Any]	    =	torch.manual_seed(lowerCamelCase	)
        else:
               _UpperCamelCase  :							List[Any]	    =	torch.Generator(device=lowerCamelCase	).manual_seed(lowerCamelCase	)

        _UpperCamelCase  :							Optional[Any]	    =	2
        _UpperCamelCase  :							List[Any]	    =	randn_tensor(
            (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)							,      generator=lowerCamelCase							,      device=torch.device(lowerCamelCase	)							,      )
        _UpperCamelCase  :							Optional[int]	    =	floats_tensor((1, 3, 3_2, 3_2)							,      rng=random.Random(lowerCamelCase	)	).to(lowerCamelCase	)
        _UpperCamelCase  :							Dict	    =	init_image.cpu().permute(0							,      2							,      3							,      1	)[0]

        _UpperCamelCase  :							Tuple	    =	Image.fromarray(np.uinta(lowerCamelCase	)	).convert('RGB'	).resize((6_4, 6_4)	)
        _UpperCamelCase  :							Dict	    =	Image.fromarray(np.uinta(init_image + 4	)	).convert('RGB'	).resize((6_4, 6_4)	)

        _UpperCamelCase  :							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        __snake_case       (      self							:		Dict	)			->				Union[str, 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        __snake_case       (      self							:		Dict	)			->				Optional[Any]:
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3	)

 def        __snake_case       (      self							:		Dict	)			->				str:
        self._test_inference_batch_single_identical(expected_max_diff=2e-3	)







class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						List[Any]					     =			StableDiffusionControlNetInpaintPipeline
 _lowerCamelCase    :						List[str]					     =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 _lowerCamelCase    :						Dict					     =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 _lowerCamelCase    :						Any					     =			frozenset([]    )

 def        __snake_case       (      self							:		int	)			->				List[str]:
        torch.manual_seed(0	)
        _UpperCamelCase  :							Tuple	    =	UNetaDConditionModel(
            block_out_channels=(3_2, 6_4)							,      layers_per_block=2							,      sample_size=3_2							,      in_channels=4							,      out_channels=4							,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')							,      cross_attention_dim=3_2							,      )
        torch.manual_seed(0	)
        _UpperCamelCase  :							Tuple	    =	ControlNetModel(
            block_out_channels=(3_2, 6_4)							,      layers_per_block=2							,      in_channels=4							,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,      cross_attention_dim=3_2							,      conditioning_embedding_out_channels=(1_6, 3_2)							,      )
        torch.manual_seed(0	)
        _UpperCamelCase  :							Union[str, Any]	    =	DDIMScheduler(
            beta_start=0.0_0_0_8_5							,      beta_end=0.0_1_2							,      beta_schedule='scaled_linear'							,      clip_sample=lowerCamelCase							,      set_alpha_to_one=lowerCamelCase							,      )
        torch.manual_seed(0	)
        _UpperCamelCase  :							Optional[int]	    =	AutoencoderKL(
            block_out_channels=[3_2, 6_4]							,      in_channels=3							,      out_channels=3							,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']							,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']							,      latent_channels=4							,      )
        torch.manual_seed(0	)
        _UpperCamelCase  :							Optional[Any]	    =	CLIPTextConfig(
            bos_token_id=0							,      eos_token_id=2							,      hidden_size=3_2							,      intermediate_size=3_7							,      layer_norm_eps=1e-05							,      num_attention_heads=4							,      num_hidden_layers=5							,      pad_token_id=1							,      vocab_size=1_0_0_0							,      )
        _UpperCamelCase  :							List[str]	    =	CLIPTextModel(lowerCamelCase	)
        _UpperCamelCase  :							List[str]	    =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

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







class  __lowerCAmelCase	(							a_,						a_,						unittest.TestCase    ):



 '''simple docstring'''
 _lowerCamelCase    :						str					     =			StableDiffusionControlNetInpaintPipeline
 _lowerCamelCase    :						str					     =			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 _lowerCamelCase    :						Tuple					     =			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS

 def        __snake_case       (      self							:		Union[str, Any]	)			->				Union[str, Any]:
        torch.manual_seed(0	)
        _UpperCamelCase  :							int	    =	UNetaDConditionModel(
            block_out_channels=(3_2, 6_4)							,      layers_per_block=2							,      sample_size=3_2							,      in_channels=9							,      out_channels=4							,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')							,      cross_attention_dim=3_2							,      )
        torch.manual_seed(0	)

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

        _UpperCamelCase  :							List[str]	    =	ControlNetModel(
            block_out_channels=(3_2, 6_4)							,      layers_per_block=2							,      in_channels=4							,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,      cross_attention_dim=3_2							,      conditioning_embedding_out_channels=(1_6, 3_2)							,      )
        controlneta.controlnet_down_blocks.apply(lowerCamelCase	)

        torch.manual_seed(0	)
        _UpperCamelCase  :							Dict	    =	ControlNetModel(
            block_out_channels=(3_2, 6_4)							,      layers_per_block=2							,      in_channels=4							,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,      cross_attention_dim=3_2							,      conditioning_embedding_out_channels=(1_6, 3_2)							,      )
        controlneta.controlnet_down_blocks.apply(lowerCamelCase	)

        torch.manual_seed(0	)
        _UpperCamelCase  :							List[Any]	    =	DDIMScheduler(
            beta_start=0.0_0_0_8_5							,      beta_end=0.0_1_2							,      beta_schedule='scaled_linear'							,      clip_sample=lowerCamelCase							,      set_alpha_to_one=lowerCamelCase							,      )
        torch.manual_seed(0	)
        _UpperCamelCase  :							Optional[int]	    =	AutoencoderKL(
            block_out_channels=[3_2, 6_4]							,      in_channels=3							,      out_channels=3							,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']							,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']							,      latent_channels=4							,      )
        torch.manual_seed(0	)
        _UpperCamelCase  :							Dict	    =	CLIPTextConfig(
            bos_token_id=0							,      eos_token_id=2							,      hidden_size=3_2							,      intermediate_size=3_7							,      layer_norm_eps=1e-05							,      num_attention_heads=4							,      num_hidden_layers=5							,      pad_token_id=1							,      vocab_size=1_0_0_0							,      )
        _UpperCamelCase  :							str	    =	CLIPTextModel(lowerCamelCase	)
        _UpperCamelCase  :							Optional[Any]	    =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

        _UpperCamelCase  :							str	    =	MultiControlNetModel([controlneta, controlneta]	)

        _UpperCamelCase  :							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        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		Dict							,      lowerCamelCase							:		int=0	)			->				str:
        if str(lowerCamelCase	).startswith('mps'	):
               _UpperCamelCase  :							List[str]	    =	torch.manual_seed(lowerCamelCase	)
        else:
               _UpperCamelCase  :							str	    =	torch.Generator(device=lowerCamelCase	).manual_seed(lowerCamelCase	)

        _UpperCamelCase  :							Dict	    =	2

        _UpperCamelCase  :							Tuple	    =	[
            randn_tensor(
                (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)							,      generator=lowerCamelCase							,      device=torch.device(lowerCamelCase	)							,      ),
            randn_tensor(
                (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)							,      generator=lowerCamelCase							,      device=torch.device(lowerCamelCase	)							,      ),
        ]
        _UpperCamelCase  :							Tuple	    =	floats_tensor((1, 3, 3_2, 3_2)							,      rng=random.Random(lowerCamelCase	)	).to(lowerCamelCase	)
        _UpperCamelCase  :							Optional[int]	    =	init_image.cpu().permute(0							,      2							,      3							,      1	)[0]

        _UpperCamelCase  :							Optional[int]	    =	Image.fromarray(np.uinta(lowerCamelCase	)	).convert('RGB'	).resize((6_4, 6_4)	)
        _UpperCamelCase  :							Union[str, Any]	    =	Image.fromarray(np.uinta(init_image + 4	)	).convert('RGB'	).resize((6_4, 6_4)	)

        _UpperCamelCase  :							Union[str, 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        __snake_case       (      self							:		List[str]	)			->				Union[str, Any]:
        _UpperCamelCase  :							Any	    =	self.get_dummy_components()
        _UpperCamelCase  :							Optional[Any]	    =	self.pipeline_class(**lowerCamelCase	)
        pipe.to(lowerCamelCase	)

        _UpperCamelCase  :							Union[str, Any]	    =	1_0.0
        _UpperCamelCase  :							Tuple	    =	4

        _UpperCamelCase  :							Dict	    =	self.get_dummy_inputs(lowerCamelCase	)
        _UpperCamelCase  :							Union[str, Any]	    =	steps
        _UpperCamelCase  :							str	    =	scale
        _UpperCamelCase  :							List[Any]	    =	pipe(**lowerCamelCase	)[0]

        _UpperCamelCase  :							Tuple	    =	self.get_dummy_inputs(lowerCamelCase	)
        _UpperCamelCase  :							int	    =	steps
        _UpperCamelCase  :							List[str]	    =	scale
        _UpperCamelCase  :							Union[str, Any]	    =	pipe(**lowerCamelCase							,      control_guidance_start=0.1							,      control_guidance_end=0.2	)[0]

        _UpperCamelCase  :							List[Any]	    =	self.get_dummy_inputs(lowerCamelCase	)
        _UpperCamelCase  :							List[str]	    =	steps
        _UpperCamelCase  :							List[Any]	    =	scale
        _UpperCamelCase  :							Any	    =	pipe(**lowerCamelCase							,      control_guidance_start=[0.1, 0.3]							,      control_guidance_end=[0.2, 0.7]	)[0]

        _UpperCamelCase  :							int	    =	self.get_dummy_inputs(lowerCamelCase	)
        _UpperCamelCase  :							List[str]	    =	steps
        _UpperCamelCase  :							Optional[int]	    =	scale
        _UpperCamelCase  :							Any	    =	pipe(**lowerCamelCase							,      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        __snake_case       (      self							:		Any	)			->				List[str]:
        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        __snake_case       (      self							:		Optional[Any]	)			->				Union[str, Any]:
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3	)

 def        __snake_case       (      self							:		Optional[int]	)			->				Dict:
        self._test_inference_batch_single_identical(expected_max_diff=2e-3	)

 def        __snake_case       (      self							:		Tuple	)			->				Optional[Any]:
        _UpperCamelCase  :							Any	    =	self.get_dummy_components()
        _UpperCamelCase  :							Dict	    =	self.pipeline_class(**lowerCamelCase	)
        pipe.to(lowerCamelCase	)
        pipe.set_progress_bar_config(disable=lowerCamelCase	)
        with tempfile.TemporaryDirectory() as tmpdir:
               try:
                      # save_pretrained is not implemented for Multi-ControlNet
                      pipe.save_pretrained(lowerCamelCase	)
               except NotImplementedError:
                      pass







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



 '''simple docstring'''

 def        __snake_case       (      self							:		Optional[Any]	)			->				Dict:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

 def        __snake_case       (      self							:		str	)			->				List[str]:
        _UpperCamelCase  :							Union[str, Any]	    =	ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'	)

        _UpperCamelCase  :							List[Any]	    =	StableDiffusionControlNetInpaintPipeline.from_pretrained(
            'runwayml/stable-diffusion-inpainting'							,      safety_checker=lowerCamelCase							,      controlnet=lowerCamelCase	)
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=lowerCamelCase	)

        _UpperCamelCase  :							Union[str, Any]	    =	torch.Generator(device='cpu'	).manual_seed(0	)
        _UpperCamelCase  :							List[Any]	    =	load_image(
            'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'	).resize((5_1_2, 5_1_2)	)

        _UpperCamelCase  :							Optional[Any]	    =	load_image(
            'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
            '/stable_diffusion_inpaint/input_bench_mask.png'	).resize((5_1_2, 5_1_2)	)

        _UpperCamelCase  :							int	    =	'pitch black hole'

        _UpperCamelCase  :							List[str]	    =	load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'	).resize((5_1_2, 5_1_2)	)

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

        _UpperCamelCase  :							List[Any]	    =	output.images[0]

        assert image.shape == (5_1_2, 5_1_2, 3)

        _UpperCamelCase  :							Union[str, Any]	    =	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        __snake_case       (      self							:		Optional[Any]	)			->				Any:
        _UpperCamelCase  :							str	    =	ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'	)

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

        _UpperCamelCase  :							Optional[Any]	    =	torch.Generator(device='cpu'	).manual_seed(3_3	)

        _UpperCamelCase  :							str	    =	load_image(
            'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'	)
        _UpperCamelCase  :							List[str]	    =	init_image.resize((5_1_2, 5_1_2)	)

        _UpperCamelCase  :							int	    =	load_image(
            'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'	)
        _UpperCamelCase  :							int	    =	mask_image.resize((5_1_2, 5_1_2)	)

        _UpperCamelCase  :							Optional[Any]	    =	'a handsome man with ray-ban sunglasses'

        def make_inpaint_condition(lowerCamelCase							:		str							,      lowerCamelCase							:		int	):
               _UpperCamelCase  :							Optional[Any]	    =	np.array(image.convert('RGB'	)	).astype(np.floataa	) / 2_5_5.0
               _UpperCamelCase  :							List[str]	    =	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"
               _UpperCamelCase  :							List[Any]	    =	-1.0  # set as masked pixel
               _UpperCamelCase  :							Any	    =	np.expand_dims(lowerCamelCase							,      0	).transpose(0							,      3							,      1							,      2	)
               _UpperCamelCase  :							Dict	    =	torch.from_numpy(lowerCamelCase	)
               return image

        _UpperCamelCase  :							int	    =	make_inpaint_condition(lowerCamelCase							,      lowerCamelCase	)

        _UpperCamelCase  :							str	    =	pipe(
            lowerCamelCase							,      image=lowerCamelCase							,      mask_image=lowerCamelCase							,      control_image=lowerCamelCase							,      guidance_scale=9.0							,      eta=1.0							,      generator=lowerCamelCase							,      num_inference_steps=2_0							,      output_type='np'							,      )
        _UpperCamelCase  :							str	    =	output.images[0]

        assert image.shape == (5_1_2, 5_1_2, 3)

        _UpperCamelCase  :							List[str]	    =	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_tokenizers_available, is_torch_available


__A       			=      {
    """configuration_realm""": ["""REALM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RealmConfig"""],
    """tokenization_realm""": ["""RealmTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       			=      ["""RealmTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A       			=      [
     """REALM_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """RealmEmbedder""",
     """RealmForOpenQA""",
     """RealmKnowledgeAugEncoder""",
     """RealmPreTrainedModel""",
     """RealmReader""",
     """RealmScorer""",
     """load_tf_weights_in_realm""",
 ]
 __A       			=      ["""RealmRetriever"""]


if TYPE_CHECKING:
 from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
 from .tokenization_realm import RealmTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_realm import RealmTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_realm import (
      REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
      RealmEmbedder,
      RealmForOpenQA,
      RealmKnowledgeAugEncoder,
      RealmPreTrainedModel,
      RealmReader,
      RealmScorer,
      load_tf_weights_in_realm,
  )
  from .retrieval_realm import RealmRetriever


else:
 import sys

 __A       			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
