# 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










import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
class 							snake_case_		(      nn.Module):







		def __init__(		self      ,  __lowercase      ,  __lowercase=1.0 )					-> List[str]:
				super().__init__()

				lowerCamelCase :			Optional[Any]    		=cut_size
				lowerCamelCase :			List[str]    		=cut_power







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase )					-> int:
				lowerCamelCase		, lowerCamelCase :			Any    		=pixel_values.shape[2:4]
				lowerCamelCase :			Any    		=min(__lowercase      ,  __lowercase )
				lowerCamelCase :			int    		=min(__lowercase      ,  __lowercase      ,  self.cut_size )
				lowerCamelCase :			Dict    		=[]
				for _ in range(__lowercase ):
						lowerCamelCase :			Union[str, Any]    		=int(torch.rand([] ) ** self.cut_power * (max_size - min_size) + min_size )
						lowerCamelCase :			Any    		=torch.randint(0      ,  sideX - size + 1      ,  () )
						lowerCamelCase :			Any    		=torch.randint(0      ,  sideY - size + 1      ,  () )
						lowerCamelCase :			Optional[Any]    		=pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
						cutouts.append(F.adaptive_avg_poolad(__lowercase      ,  self.cut_size ) )
				return torch.cat(__lowercase )


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Optional[Any]:
		lowerCamelCase :			str    		=F.normalize(SCREAMING_SNAKE_CASE_				,							dim=-1		)
		lowerCamelCase :			Optional[Any]    		=F.normalize(SCREAMING_SNAKE_CASE_				,							dim=-1		)
		return (x - y).norm(dim=-1		).div(2		).arcsin().pow(2		).mul(2		)


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  List[str]:
		for param in model.parameters():
				lowerCamelCase :			List[Any]    		=value



class 							snake_case_		(      _A):







		def __init__(		self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  )					-> List[Any]:
				super().__init__()
				self.register_modules(
				    vae=__lowercase      ,  text_encoder=__lowercase      ,  clip_model=__lowercase      ,  tokenizer=__lowercase      ,  unet=__lowercase      ,  scheduler=__lowercase      ,  feature_extractor=__lowercase      ,  )

				lowerCamelCase :			Optional[Any]    		=transforms.Normalize(mean=feature_extractor.image_mean      ,  std=feature_extractor.image_std )
				lowerCamelCase :			int    		=(
				    feature_extractor.size
				    if isinstance(feature_extractor.size      ,  __lowercase )
				    else feature_extractor.size['''shortest_edge''']
				)
				lowerCamelCase :			int    		=MakeCutouts(self.cut_out_size )

				set_requires_grad(self.text_encoder      ,  __lowercase )
				set_requires_grad(self.clip_model      ,  __lowercase )







		def       __lowercase (		self      ,  __lowercase = "auto" )					-> Dict:
				if slice_size == "auto":
						# half the attention head size is usually a good trade-off between
						# speed and memory
						lowerCamelCase :			int    		=self.unet.config.attention_head_dim // 2
				self.unet.set_attention_slice(__lowercase )







		def       __lowercase (		self )					-> List[str]:
				self.enable_attention_slicing(__lowercase )







		def       __lowercase (		self )					-> List[str]:
				set_requires_grad(self.vae      ,  __lowercase )







		def       __lowercase (		self )					-> List[str]:
				set_requires_grad(self.vae      ,  __lowercase )







		def       __lowercase (		self )					-> Dict:
				set_requires_grad(self.unet      ,  __lowercase )







		def       __lowercase (		self )					-> Union[str, Any]:
				set_requires_grad(self.unet      ,  __lowercase )







		@torch.enable_grad()
		def       __lowercase (		self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase=True      ,  )					-> Union[str, Any]:
				lowerCamelCase :			Dict    		=latents.detach().requires_grad_()

				lowerCamelCase :			int    		=self.scheduler.scale_model_input(__lowercase      ,  __lowercase )

				# predict the noise residual
				lowerCamelCase :			Union[str, Any]    		=self.unet(__lowercase      ,  __lowercase      ,  encoder_hidden_states=__lowercase ).sample

				if isinstance(self.scheduler      ,  (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
						lowerCamelCase :			Optional[int]    		=self.scheduler.alphas_cumprod[timestep]
						lowerCamelCase :			List[str]    		=1 - alpha_prod_t
						# compute predicted original sample from predicted noise also called
						# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						lowerCamelCase :			Tuple    		=(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

						lowerCamelCase :			List[str]    		=torch.sqrt(__lowercase )
						lowerCamelCase :			str    		=pred_original_sample * (fac) + latents * (1 - fac)
				elif isinstance(self.scheduler      ,  __lowercase ):
						lowerCamelCase :			Optional[int]    		=self.scheduler.sigmas[index]
						lowerCamelCase :			str    		=latents - sigma * noise_pred
				else:
						raise ValueError(F"scheduler type {type(self.scheduler )} not supported" )

				lowerCamelCase :			List[Any]    		=1 / self.vae.config.scaling_factor * sample
				lowerCamelCase :			Optional[Any]    		=self.vae.decode(__lowercase ).sample
				lowerCamelCase :			Dict    		=(image / 2 + 0.5).clamp(0      ,  1 )

				if use_cutouts:
						lowerCamelCase :			Tuple    		=self.make_cutouts(__lowercase      ,  __lowercase )
				else:
						lowerCamelCase :			str    		=transforms.Resize(self.cut_out_size )(__lowercase )
				lowerCamelCase :			List[Any]    		=self.normalize(__lowercase ).to(latents.dtype )

				lowerCamelCase :			Optional[Any]    		=self.clip_model.get_image_features(__lowercase )
				lowerCamelCase :			Optional[int]    		=image_embeddings_clip / image_embeddings_clip.norm(p=2      ,  dim=-1      ,  keepdim=__lowercase )

				if use_cutouts:
						lowerCamelCase :			str    		=spherical_dist_loss(__lowercase      ,  __lowercase )
						lowerCamelCase :			Union[str, Any]    		=dists.view([num_cutouts, sample.shape[0], -1] )
						lowerCamelCase :			Tuple    		=dists.sum(2 ).mean(0 ).sum() * clip_guidance_scale
				else:
						lowerCamelCase :			Union[str, Any]    		=spherical_dist_loss(__lowercase      ,  __lowercase ).mean() * clip_guidance_scale

				lowerCamelCase :			List[Any]    		=-torch.autograd.grad(__lowercase      ,  __lowercase )[0]

				if isinstance(self.scheduler      ,  __lowercase ):
						lowerCamelCase :			Any    		=latents.detach() + grads * (sigma**2)
						lowerCamelCase :			Dict    		=noise_pred_original
				else:
						lowerCamelCase :			Union[str, Any]    		=noise_pred_original - torch.sqrt(__lowercase ) * grads
				return noise_pred, latents







		@torch.no_grad()
		def __call__(		self      ,  __lowercase      ,  __lowercase = 5_1_2      ,  __lowercase = 5_1_2      ,  __lowercase = 5_0      ,  __lowercase = 7.5      ,  __lowercase = 1      ,  __lowercase = 0.0      ,  __lowercase = 1_0_0      ,  __lowercase = None      ,  __lowercase = 4      ,  __lowercase = True      ,  __lowercase = None      ,  __lowercase = None      ,  __lowercase = "pil"      ,  __lowercase = True      ,  )					-> Dict:
				if isinstance(__lowercase      ,  __lowercase ):
						lowerCamelCase :			List[str]    		=1
				elif isinstance(__lowercase      ,  __lowercase ):
						lowerCamelCase :			Dict    		=len(__lowercase )
				else:
						raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(__lowercase )}" )

				if height % 8 != 0 or width % 8 != 0:
						raise ValueError(F"`height` and `width` have to be divisible by 8 but are {height} and {width}." )

				# get prompt text embeddings
				lowerCamelCase :			Optional[int]    		=self.tokenizer(
				    __lowercase      ,  padding='''max_length'''      ,  max_length=self.tokenizer.model_max_length      ,  truncation=__lowercase      ,  return_tensors='''pt'''      ,  )
				lowerCamelCase :			Any    		=self.text_encoder(text_input.input_ids.to(self.device ) )[0]
				# duplicate text embeddings for each generation per prompt
				lowerCamelCase :			Union[str, Any]    		=text_embeddings.repeat_interleave(__lowercase      ,  dim=0 )

				if clip_guidance_scale > 0:
						if clip_prompt is not None:
								lowerCamelCase :			Dict    		=self.tokenizer(
								    __lowercase      ,  padding='''max_length'''      ,  max_length=self.tokenizer.model_max_length      ,  truncation=__lowercase      ,  return_tensors='''pt'''      ,  ).input_ids.to(self.device )
						else:
								lowerCamelCase :			List[Any]    		=text_input.input_ids.to(self.device )
						lowerCamelCase :			Dict    		=self.clip_model.get_text_features(__lowercase )
						lowerCamelCase :			int    		=text_embeddings_clip / text_embeddings_clip.norm(p=2      ,  dim=-1      ,  keepdim=__lowercase )
						# duplicate text embeddings clip for each generation per prompt
						lowerCamelCase :			List[Any]    		=text_embeddings_clip.repeat_interleave(__lowercase      ,  dim=0 )

				# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
				# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
				# corresponds to doing no classifier free guidance.
				lowerCamelCase :			int    		=guidance_scale > 1.0
				# get unconditional embeddings for classifier free guidance
				if do_classifier_free_guidance:
						lowerCamelCase :			Union[str, Any]    		=text_input.input_ids.shape[-1]
						lowerCamelCase :			str    		=self.tokenizer(['''''']      ,  padding='''max_length'''      ,  max_length=__lowercase      ,  return_tensors='''pt''' )
						lowerCamelCase :			Any    		=self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
						# duplicate unconditional embeddings for each generation per prompt
						lowerCamelCase :			str    		=uncond_embeddings.repeat_interleave(__lowercase      ,  dim=0 )

						# For classifier free guidance, we need to do two forward passes.
						# Here we concatenate the unconditional and text embeddings into a single batch
						# to avoid doing two forward passes
						lowerCamelCase :			Optional[Any]    		=torch.cat([uncond_embeddings, text_embeddings] )

				# get the initial random noise unless the user supplied it

				# Unlike in other pipelines, latents need to be generated in the target device
				# for 1-to-1 results reproducibility with the CompVis implementation.
				# However this currently doesn't work in `mps`.
				lowerCamelCase :			Optional[int]    		=(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
				lowerCamelCase :			List[Any]    		=text_embeddings.dtype
				if latents is None:
						if self.device.type == "mps":
								# randn does not work reproducibly on mps
								lowerCamelCase :			Optional[int]    		=torch.randn(__lowercase      ,  generator=__lowercase      ,  device='''cpu'''      ,  dtype=__lowercase ).to(
								    self.device )
						else:
								lowerCamelCase :			int    		=torch.randn(__lowercase      ,  generator=__lowercase      ,  device=self.device      ,  dtype=__lowercase )
				else:
						if latents.shape != latents_shape:
								raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}" )
						lowerCamelCase :			Optional[int]    		=latents.to(self.device )

				# set timesteps
				lowerCamelCase :			Optional[Any]    		='''offset''' in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
				lowerCamelCase :			List[Any]    		={}
				if accepts_offset:
						lowerCamelCase :			Tuple    		=1

				self.scheduler.set_timesteps(__lowercase      ,  **__lowercase )

				# Some schedulers like PNDM have timesteps as arrays
				# It's more optimized to move all timesteps to correct device beforehand
				lowerCamelCase :			Optional[int]    		=self.scheduler.timesteps.to(self.device )

				# scale the initial noise by the standard deviation required by the scheduler
				lowerCamelCase :			List[Any]    		=latents * self.scheduler.init_noise_sigma

				# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
				# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
				# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
				# and should be between [0, 1]
				lowerCamelCase :			Tuple    		='''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
				lowerCamelCase :			Union[str, Any]    		={}
				if accepts_eta:
						lowerCamelCase :			List[str]    		=eta

				# check if the scheduler accepts generator
				lowerCamelCase :			Tuple    		='''generator''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
				if accepts_generator:
						lowerCamelCase :			int    		=generator

				for i, t in enumerate(self.progress_bar(__lowercase ) ):
						# expand the latents if we are doing classifier free guidance
						lowerCamelCase :			int    		=torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
						lowerCamelCase :			int    		=self.scheduler.scale_model_input(__lowercase      ,  __lowercase )

						# predict the noise residual
						lowerCamelCase :			Optional[int]    		=self.unet(__lowercase      ,  __lowercase      ,  encoder_hidden_states=__lowercase ).sample

						# perform classifier free guidance
						if do_classifier_free_guidance:
								lowerCamelCase		, lowerCamelCase :			Optional[int]    		=noise_pred.chunk(2 )
								lowerCamelCase :			Union[str, Any]    		=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

						# perform clip guidance
						if clip_guidance_scale > 0:
								lowerCamelCase :			List[Any]    		=(
								    text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
								)
								lowerCamelCase		, lowerCamelCase :			Union[str, Any]    		=self.cond_fn(
								    __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  )

						# compute the previous noisy sample x_t -> x_t-1
						lowerCamelCase :			Dict    		=self.scheduler.step(__lowercase      ,  __lowercase      ,  __lowercase      ,  **__lowercase ).prev_sample

				# scale and decode the image latents with vae
				lowerCamelCase :			Tuple    		=1 / self.vae.config.scaling_factor * latents
				lowerCamelCase :			str    		=self.vae.decode(__lowercase ).sample

				lowerCamelCase :			Optional[int]    		=(image / 2 + 0.5).clamp(0      ,  1 )
				lowerCamelCase :			Union[str, Any]    		=image.cpu().permute(0      ,  2      ,  3      ,  1 ).numpy()

				if output_type == "pil":
						lowerCamelCase :			Optional[Any]    		=self.numpy_to_pil(__lowercase )

				if not return_dict:
						return (image, None)

				return StableDiffusionPipelineOutput(images=__lowercase      ,  nsfw_content_detected=__lowercase )

