# 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 math
import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class        _A			(      __UpperCAmelCase       ):
       UpperCamelCase__    :       torch.FloatTensor
       UpperCamelCase__    :       Optional[torch.FloatTensor]		   =		None





def 	__snake_case      (     _UpperCAmelCase  ,					_UpperCAmelCase=0.9_99  ,					_UpperCAmelCase="cosine"  ,					):
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(_UpperCAmelCase							):
            return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2							) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(_UpperCAmelCase							):
            return math.exp(t * -12.0							)

    else:
        raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}'							)

    __a					=							[]
    for i in range(_UpperCAmelCase							):
        __a					=							i / num_diffusion_timesteps
        __a					=							(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(_UpperCAmelCase							) / alpha_bar_fn(_UpperCAmelCase							)  ,					_UpperCAmelCase							)							)
    return torch.tensor(_UpperCAmelCase  ,					dtype=torch.floataa							)

class        _A			(      __UpperCAmelCase ,__UpperCAmelCase       ):
       UpperCamelCase__    :       List[Any]		   =		[e.name for e in KarrasDiffusionSchedulers]
       UpperCamelCase__    :       Optional[Any]		   =		1



       @register_to_config
       def __init__(						self				:	Tuple	,						__SCREAMING_SNAKE_CASE				:	int = 1_000	,						__SCREAMING_SNAKE_CASE				:	float = 0.00_01	,						__SCREAMING_SNAKE_CASE				:	float = 0.02	,						__SCREAMING_SNAKE_CASE				:	str = "linear"	,						__SCREAMING_SNAKE_CASE				:	Optional[Union[np.ndarray, List[float]]] = None	,						__SCREAMING_SNAKE_CASE				:	Optional[bool] = False	,						__SCREAMING_SNAKE_CASE				:	str = "epsilon"	,						__SCREAMING_SNAKE_CASE				:	str = "linspace"	,						__SCREAMING_SNAKE_CASE				:	int = 0	,						):


           '''simple docstring'''

           if trained_betas is not None:
               __a					=							torch.tensor(__SCREAMING_SNAKE_CASE	,						dtype=torch.floataa)
           elif beta_schedule == "linear":
               __a					=							torch.linspace(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						dtype=torch.floataa)
           elif beta_schedule == "scaled_linear":
               # this schedule is very specific to the latent diffusion model.
               __a					=							(
                   torch.linspace(beta_start**0.5	,						beta_end**0.5	,						__SCREAMING_SNAKE_CASE	,						dtype=torch.floataa) ** 2
               )
           elif beta_schedule == "squaredcos_cap_v2":
               # Glide cosine schedule
               __a					=							betas_for_alpha_bar(__SCREAMING_SNAKE_CASE)
           else:
               raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}')

           __a					=							1.0 - self.betas
           __a					=							torch.cumprod(self.alphas	,						dim=0)

           __a					=							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
           __a					=							np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
           __a					=							torch.from_numpy(__SCREAMING_SNAKE_CASE)

           # setable values
           __a					=							None
           __a					=							use_karras_sigmas
           self.set_timesteps(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE)
           __a					=							[]
           __a					=							False



       @property
       def 						_lowerCamelCase     (						self				:	Tuple):


           '''simple docstring'''

           if self.config.timestep_spacing in ["linspace", "trailing"]:
               return self.sigmas.max()

           return (self.sigmas.max() ** 2 + 1) ** 0.5



       def 						_lowerCamelCase     (						self				:	Any	,						__SCREAMING_SNAKE_CASE				:	torch.FloatTensor	,						__SCREAMING_SNAKE_CASE				:	Union[float, torch.FloatTensor]):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE	,						torch.Tensor):
               __a					=							timestep.to(self.timesteps.device)
           __a					=							(self.timesteps == timestep).nonzero().item()
           __a					=							self.sigmas[step_index]
           __a					=							sample / ((sigma**2 + 1) ** 0.5)
           __a					=							True
           return sample



       def 						_lowerCamelCase     (						self				:	Union[str, Any]	,						__SCREAMING_SNAKE_CASE				:	str	,						__SCREAMING_SNAKE_CASE				:	int	,						__SCREAMING_SNAKE_CASE				:	Optional[int]):


           '''simple docstring'''

           def lms_derivative(__SCREAMING_SNAKE_CASE				:	str):
               __a					=							1.0
               for k in range(__SCREAMING_SNAKE_CASE):
                   if current_order == k:
                       continue
                   prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
               return prod

           __a					=							integrate.quad(__SCREAMING_SNAKE_CASE	,						self.sigmas[t]	,						self.sigmas[t + 1]	,						epsrel=1E-4)[0]

           return integrated_coeff



       def 						_lowerCamelCase     (						self				:	int	,						__SCREAMING_SNAKE_CASE				:	int	,						__SCREAMING_SNAKE_CASE				:	Union[str, torch.device] = None):


           '''simple docstring'''

           __a					=							num_inference_steps

           # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
           if self.config.timestep_spacing == "linspace":
               __a					=							np.linspace(0	,						self.config.num_train_timesteps - 1	,						__SCREAMING_SNAKE_CASE	,						dtype=__SCREAMING_SNAKE_CASE)[
                   ::-1
               ].copy()
           elif self.config.timestep_spacing == "leading":
               __a					=							self.config.num_train_timesteps // self.num_inference_steps
               # creates integer timesteps by multiplying by ratio
               # casting to int to avoid issues when num_inference_step is power of 3
               __a					=							(np.arange(0	,						__SCREAMING_SNAKE_CASE) * step_ratio).round()[::-1].copy().astype(__SCREAMING_SNAKE_CASE)
               timesteps += self.config.steps_offset
           elif self.config.timestep_spacing == "trailing":
               __a					=							self.config.num_train_timesteps / self.num_inference_steps
               # creates integer timesteps by multiplying by ratio
               # casting to int to avoid issues when num_inference_step is power of 3
               __a					=							(np.arange(self.config.num_train_timesteps	,						0	,						-step_ratio)).round().copy().astype(__SCREAMING_SNAKE_CASE)
               timesteps -= 1
           else:
               raise ValueError(
                   F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.')

           __a					=							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
           __a					=							np.log(__SCREAMING_SNAKE_CASE)
           __a					=							np.interp(__SCREAMING_SNAKE_CASE	,						np.arange(0	,						len(__SCREAMING_SNAKE_CASE))	,						__SCREAMING_SNAKE_CASE)

           if self.use_karras_sigmas:
               __a					=							self._convert_to_karras(in_sigmas=__SCREAMING_SNAKE_CASE)
               __a					=							np.array([self._sigma_to_t(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE) for sigma in sigmas])

           __a					=							np.concatenate([sigmas, [0.0]]).astype(np.floataa)

           __a					=							torch.from_numpy(__SCREAMING_SNAKE_CASE).to(device=__SCREAMING_SNAKE_CASE)
           if str(__SCREAMING_SNAKE_CASE).startswith('''mps'''):
               # mps does not support float64
               __a					=							torch.from_numpy(__SCREAMING_SNAKE_CASE).to(__SCREAMING_SNAKE_CASE	,						dtype=torch.floataa)
           else:
               __a					=							torch.from_numpy(__SCREAMING_SNAKE_CASE).to(device=__SCREAMING_SNAKE_CASE)

           __a					=							[]



       def 						_lowerCamelCase     (						self				:	Optional[int]	,						__SCREAMING_SNAKE_CASE				:	Dict	,						__SCREAMING_SNAKE_CASE				:	Any):


           '''simple docstring'''

           __a					=							np.log(__SCREAMING_SNAKE_CASE)

           # get distribution
           __a					=							log_sigma - log_sigmas[:, np.newaxis]

           # get sigmas range
           __a					=							np.cumsum((dists >= 0)	,						axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)
           __a					=							low_idx + 1

           __a					=							log_sigmas[low_idx]
           __a					=							log_sigmas[high_idx]

           # interpolate sigmas
           __a					=							(low - log_sigma) / (low - high)
           __a					=							np.clip(__SCREAMING_SNAKE_CASE	,						0	,						1)

           # transform interpolation to time range
           __a					=							(1 - w) * low_idx + w * high_idx
           __a					=							t.reshape(sigma.shape)
           return t



       def 						_lowerCamelCase     (						self				:	Optional[Any]	,						__SCREAMING_SNAKE_CASE				:	torch.FloatTensor):


           '''simple docstring'''

           __a					=							in_sigmas[-1].item()
           __a					=							in_sigmas[0].item()

           __a					=							7.0  # 7.0 is the value used in the paper
           __a					=							np.linspace(0	,						1	,						self.num_inference_steps)
           __a					=							sigma_min ** (1 / rho)
           __a					=							sigma_max ** (1 / rho)
           __a					=							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
           return sigmas



       def 						_lowerCamelCase     (						self				:	Union[str, Any]	,						__SCREAMING_SNAKE_CASE				:	torch.FloatTensor	,						__SCREAMING_SNAKE_CASE				:	Union[float, torch.FloatTensor]	,						__SCREAMING_SNAKE_CASE				:	torch.FloatTensor	,						__SCREAMING_SNAKE_CASE				:	int = 4	,						__SCREAMING_SNAKE_CASE				:	bool = True	,						):


           '''simple docstring'''

           if not self.is_scale_input_called:
               warnings.warn(
                   '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
                   '''See `StableDiffusionPipeline` for a usage example.''')

           if isinstance(__SCREAMING_SNAKE_CASE	,						torch.Tensor):
               __a					=							timestep.to(self.timesteps.device)
           __a					=							(self.timesteps == timestep).nonzero().item()
           __a					=							self.sigmas[step_index]

           # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
           if self.config.prediction_type == "epsilon":
               __a					=							sample - sigma * model_output
           elif self.config.prediction_type == "v_prediction":
               # * c_out + input * c_skip
               __a					=							model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
           elif self.config.prediction_type == "sample":
               __a					=							model_output
           else:
               raise ValueError(
                   F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`')

           # 2. Convert to an ODE derivative
           __a					=							(sample - pred_original_sample) / sigma
           self.derivatives.append(__SCREAMING_SNAKE_CASE)
           if len(self.derivatives) > order:
               self.derivatives.pop(0)

           # 3. Compute linear multistep coefficients
           __a					=							min(step_index + 1	,						__SCREAMING_SNAKE_CASE)
           __a					=							[self.get_lms_coefficient(__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE) for curr_order in range(__SCREAMING_SNAKE_CASE)]

           # 4. Compute previous sample based on the derivatives path
           __a					=							sample + sum(
               coeff * derivative for coeff, derivative in zip(__SCREAMING_SNAKE_CASE	,						reversed(self.derivatives)))

           if not return_dict:
               return (prev_sample,)

           return LMSDiscreteSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE	,						pred_original_sample=__SCREAMING_SNAKE_CASE)



       def 						_lowerCamelCase     (						self				:	Optional[Any]	,						__SCREAMING_SNAKE_CASE				:	torch.FloatTensor	,						__SCREAMING_SNAKE_CASE				:	torch.FloatTensor	,						__SCREAMING_SNAKE_CASE				:	torch.FloatTensor	,						):


           '''simple docstring'''

           __a					=							self.sigmas.to(device=original_samples.device	,						dtype=original_samples.dtype)
           if original_samples.device.type == "mps" and torch.is_floating_point(__SCREAMING_SNAKE_CASE):
               # mps does not support float64
               __a					=							self.timesteps.to(original_samples.device	,						dtype=torch.floataa)
               __a					=							timesteps.to(original_samples.device	,						dtype=torch.floataa)
           else:
               __a					=							self.timesteps.to(original_samples.device)
               __a					=							timesteps.to(original_samples.device)

           __a					=							[(schedule_timesteps == t).nonzero().item() for t in timesteps]

           __a					=							sigmas[step_indices].flatten()
           while len(sigma.shape) < len(original_samples.shape):
               __a					=							sigma.unsqueeze(-1)

           __a					=							original_samples + noise * sigma
           return noisy_samples





       def __len__(						self				:	List[str]):


           '''simple docstring'''

           return self.config.num_train_timesteps





