

# 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 						__magic_name__      (							lowerCamelCase__		,	lowerCamelCase__		,	lowerCamelCase__		,	unittest.TestCase							):

   '''simple docstring'''




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



   def 			_UpperCAmelCase			(    self	)							->   Any:






     """simple docstring"""






     torch.manual_seed(0	)
     a__  =UNetaDConditionModel(
         block_out_channels=(32, 64),   layers_per_block=2,   sample_size=32,   in_channels=9,   out_channels=4,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),   up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''),   cross_attention_dim=32,   )
     torch.manual_seed(0	)
     a__  =ControlNetModel(
         block_out_channels=(32, 64),   layers_per_block=2,   in_channels=4,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),   cross_attention_dim=32,   conditioning_embedding_out_channels=(16, 32),   )
     torch.manual_seed(0	)
     a__  =DDIMScheduler(
         beta_start=0.00085,   beta_end=0.012,   beta_schedule='''scaled_linear''',   clip_sample=lowercase_,   set_alpha_to_one=lowercase_,   )
     torch.manual_seed(0	)
     a__  =AutoencoderKL(
         block_out_channels=[32, 64],   in_channels=3,   out_channels=3,   down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],   up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],   latent_channels=4,   )
     torch.manual_seed(0	)
     a__  =CLIPTextConfig(
         bos_token_id=0,   eos_token_id=2,   hidden_size=32,   intermediate_size=37,   layer_norm_eps=1E-05,   num_attention_heads=4,   num_hidden_layers=5,   pad_token_id=1,   vocab_size=1000,   )
     a__  =CLIPTextModel(lowercase_	)
     a__  =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

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



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_=0	)							->   Dict:






     """simple docstring"""






     if str(lowercase_	).startswith('''mps'''	):
       a__  =torch.manual_seed(lowercase_	)
     else:
       a__  =torch.Generator(device=lowercase_	).manual_seed(lowercase_	)

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

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

     a__  ={
         '''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 			_UpperCAmelCase			(    self	)							->   Optional[Any]:






     """simple docstring"""






     return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3	)



   @unittest.skipIf(
       torch_device != '''cuda''' or not is_xformers_available(),   reason='''XFormers attention is only available with CUDA and `xformers` installed''',   )
   def 			_UpperCAmelCase			(    self	)							->   List[str]:






     """simple docstring"""






     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3	)



   def 			_UpperCAmelCase			(    self	)							->   List[str]:






     """simple docstring"""






     self._test_inference_batch_single_identical(expected_max_diff=2E-3	)







class 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__			:     int    =       StableDiffusionControlNetInpaintPipeline
   lowerCamelCase__			:     str    =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   lowerCamelCase__			:     Optional[int]    =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   lowerCamelCase__			:     Optional[int]    =       frozenset([]							)



   def 			_UpperCAmelCase			(    self	)							->   List[Any]:






     """simple docstring"""






     torch.manual_seed(0	)
     a__  =UNetaDConditionModel(
         block_out_channels=(32, 64),   layers_per_block=2,   sample_size=32,   in_channels=4,   out_channels=4,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),   up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''),   cross_attention_dim=32,   )
     torch.manual_seed(0	)
     a__  =ControlNetModel(
         block_out_channels=(32, 64),   layers_per_block=2,   in_channels=4,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),   cross_attention_dim=32,   conditioning_embedding_out_channels=(16, 32),   )
     torch.manual_seed(0	)
     a__  =DDIMScheduler(
         beta_start=0.00085,   beta_end=0.012,   beta_schedule='''scaled_linear''',   clip_sample=lowercase_,   set_alpha_to_one=lowercase_,   )
     torch.manual_seed(0	)
     a__  =AutoencoderKL(
         block_out_channels=[32, 64],   in_channels=3,   out_channels=3,   down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],   up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],   latent_channels=4,   )
     torch.manual_seed(0	)
     a__  =CLIPTextConfig(
         bos_token_id=0,   eos_token_id=2,   hidden_size=32,   intermediate_size=37,   layer_norm_eps=1E-05,   num_attention_heads=4,   num_hidden_layers=5,   pad_token_id=1,   vocab_size=1000,   )
     a__  =CLIPTextModel(lowercase_	)
     a__  =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

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







class 						__magic_name__      (							lowerCamelCase__		,	lowerCamelCase__		,	unittest.TestCase							):

   '''simple docstring'''




   lowerCamelCase__			:     Dict    =       StableDiffusionControlNetInpaintPipeline
   lowerCamelCase__			:     Tuple    =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   lowerCamelCase__			:     Union[str, Any]    =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS



   def 			_UpperCAmelCase			(    self	)							->   Optional[Any]:






     """simple docstring"""






     torch.manual_seed(0	)
     a__  =UNetaDConditionModel(
         block_out_channels=(32, 64),   layers_per_block=2,   sample_size=32,   in_channels=9,   out_channels=4,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),   up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''),   cross_attention_dim=32,   )
     torch.manual_seed(0	)

     def init_weights(lowercase_	):
       if isinstance(lowercase_,   torch.nn.Convad	):
         torch.nn.init.normal(m.weight	)
         m.bias.data.fill_(1.0	)

     a__  =ControlNetModel(
         block_out_channels=(32, 64),   layers_per_block=2,   in_channels=4,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),   cross_attention_dim=32,   conditioning_embedding_out_channels=(16, 32),   )
     controlneta.controlnet_down_blocks.apply(lowercase_	)

     torch.manual_seed(0	)
     a__  =ControlNetModel(
         block_out_channels=(32, 64),   layers_per_block=2,   in_channels=4,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),   cross_attention_dim=32,   conditioning_embedding_out_channels=(16, 32),   )
     controlneta.controlnet_down_blocks.apply(lowercase_	)

     torch.manual_seed(0	)
     a__  =DDIMScheduler(
         beta_start=0.00085,   beta_end=0.012,   beta_schedule='''scaled_linear''',   clip_sample=lowercase_,   set_alpha_to_one=lowercase_,   )
     torch.manual_seed(0	)
     a__  =AutoencoderKL(
         block_out_channels=[32, 64],   in_channels=3,   out_channels=3,   down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],   up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],   latent_channels=4,   )
     torch.manual_seed(0	)
     a__  =CLIPTextConfig(
         bos_token_id=0,   eos_token_id=2,   hidden_size=32,   intermediate_size=37,   layer_norm_eps=1E-05,   num_attention_heads=4,   num_hidden_layers=5,   pad_token_id=1,   vocab_size=1000,   )
     a__  =CLIPTextModel(lowercase_	)
     a__  =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

     a__  =MultiControlNetModel([controlneta, controlneta]	)

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



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_=0	)							->   str:






     """simple docstring"""






     if str(lowercase_	).startswith('''mps'''	):
       a__  =torch.manual_seed(lowercase_	)
     else:
       a__  =torch.Generator(device=lowercase_	).manual_seed(lowercase_	)

     a__  =2

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

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

     a__  ={
         '''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 			_UpperCAmelCase			(    self	)							->   Optional[int]:






     """simple docstring"""






     a__  =self.get_dummy_components()
     a__  =self.pipeline_class(**lowercase_	)
     pipe.to(lowercase_	)

     a__  =10.0
     a__  =4

     a__  =self.get_dummy_inputs(lowercase_	)
     a__  =steps
     a__  =scale
     a__  =pipe(**lowercase_	)[0]

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

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

     a__  =self.get_dummy_inputs(lowercase_	)
     a__  =steps
     a__  =scale
     a__  =pipe(**lowercase_,   control_guidance_start=0.4,   control_guidance_end=[0.5, 0.8]	)[0]

     # make sure that all outputs are different
     assert np.sum(np.abs(output_a - output_a	)	) > 1E-3
     assert np.sum(np.abs(output_a - output_a	)	) > 1E-3
     assert np.sum(np.abs(output_a - output_a	)	) > 1E-3



   def 			_UpperCAmelCase			(    self	)							->   List[Any]:






     """simple docstring"""






     return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3	)



   @unittest.skipIf(
       torch_device != '''cuda''' or not is_xformers_available(),   reason='''XFormers attention is only available with CUDA and `xformers` installed''',   )
   def 			_UpperCAmelCase			(    self	)							->   Dict:






     """simple docstring"""






     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3	)



   def 			_UpperCAmelCase			(    self	)							->   Optional[Any]:






     """simple docstring"""






     self._test_inference_batch_single_identical(expected_max_diff=2E-3	)



   def 			_UpperCAmelCase			(    self	)							->   Union[str, Any]:






     """simple docstring"""






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







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

   '''simple docstring'''




   def 			_UpperCAmelCase			(    self	)							->   Optional[Any]:






     """simple docstring"""






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



   def 			_UpperCAmelCase			(    self	)							->   Optional[int]:






     """simple docstring"""






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

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

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

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

     a__  ='''pitch black hole'''

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

     a__  =pipe(
         lowercase_,   image=lowercase_,   mask_image=lowercase_,   control_image=lowercase_,   generator=lowercase_,   output_type='''np''',   num_inference_steps=3,   )

     a__  =output.images[0]

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

     a__  =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 			_UpperCAmelCase			(    self	)							->   List[Any]:






     """simple docstring"""






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

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

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

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

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

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

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

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

     a__  =make_inpaint_condition(lowercase_,   lowercase_	)

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

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

     a__  =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



from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)







class 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   def __init__(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_ = 0.93,   lowercase_ = True,   )							->   Any:






     """simple docstring"""






     a__  =mixing_coeff
     a__  =leapfrog_steps

     super().__init__()
     self.register_modules(
         vae=lowercase_,   text_encoder=lowercase_,   tokenizer=lowercase_,   unet=lowercase_,   scheduler=lowercase_,   )

     a__  =2 ** (len(self.vae.config.block_out_channels	) - 1)
     a__  =VaeImageProcessor(vae_scale_factor=self.vae_scale_factor	)



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_ = None,   lowercase_ = False	)							->   int:






     """simple docstring"""






     a__  =self.tokenizer(
         lowercase_,   padding='''max_length''',   max_length=self.tokenizer.model_max_length,   truncation=lowercase_,   return_tensors='''pt''',   )

     a__  =self.text_encoder(text_inputs.input_ids.to(self.device	)	).last_hidden_state

     a__  =prompt_embeds.to(dtype=self.text_encoder.dtype,   device=self.device	)

     if do_classifier_free_guidance:
       a__  ='''''' if negative_prompt is None else negative_prompt

       a__  =self.tokenizer(
           lowercase_,   padding='''max_length''',   max_length=self.tokenizer.model_max_length,   truncation=lowercase_,   return_tensors='''pt''',   )

       a__  =self.text_encoder(uncond_input.input_ids.to(self.device	)	).last_hidden_state

       a__  =torch.cat([negative_prompt_embeds, prompt_embeds]	)

     return prompt_embeds



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   Optional[Any]:






     """simple docstring"""






     a__  =self.mixing_coeff * x + (1 - self.mixing_coeff) * y
     a__  =self.mixing_coeff * y + (1 - self.mixing_coeff) * x

     return [x, y]



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   Dict:






     """simple docstring"""






     a__  =(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
     a__  =(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

     return [x, y]



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   str:






     """simple docstring"""






     a__  =int(lowercase_	)

     a__  =self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

     return alpha_prod, 1 - alpha_prod



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   )							->   Any:






     """simple docstring"""






     a__  =timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

     a__,				a__  =self._get_alpha_and_beta(lowercase_	)
     a__,				a__  =self._get_alpha_and_beta(lowercase_	)

     a__  =(alpha_prod_t_prev / alpha_prod_t) ** 0.5
     a__  =-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

     a__  =(base - b_t * model_output) / a_t

     return model_input, next_model_input.to(base.dtype	)



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   )							->   Any:






     """simple docstring"""






     a__  =timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

     a__,				a__  =self._get_alpha_and_beta(lowercase_	)
     a__,				a__  =self._get_alpha_and_beta(lowercase_	)

     a__  =(alpha_prod_t_prev / alpha_prod_t) ** 0.5
     a__  =-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
     a__  =a_t * base + b_t * model_output

     return model_input, next_model_input.to(base.dtype	)



   @torch.no_grad()
   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   int:






     """simple docstring"""






     a__  =1 / self.vae.config.scaling_factor * latents
     a__  =self.vae.decode(lowercase_	).sample
     a__  =(image / 2 + 0.5).clamp(0,   1	)
     return image



   @torch.no_grad()
   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_ = None,   )							->   str:






     """simple docstring"""






     a__  =guidance_scale > 1.0

     a__  =image.to(device=self.device,   dtype=text_embeds.dtype	)
     a__  =self.vae.encode(lowercase_	).latent_dist.sample(lowercase_	)

     a__  =self.vae.config.scaling_factor * latent

     a__  =[latent.clone(), latent.clone()]

     for i, t in tqdm(enumerate(lowercase_	),   total=len(lowercase_	)	):
       a__  =self.noise_mixing_layer(x=coupled_latents[0],   y=coupled_latents[1]	)

       # j - model_input index, k - base index
       for j in range(2	):
         a__  =j ^ 1

         if self.leapfrog_steps:
           if i % 2 == 0:
             a__,				a__  =j, k

         a__  =coupled_latents[j]
         a__  =coupled_latents[k]

         a__  =torch.cat([model_input] * 2	) if do_classifier_free_guidance else model_input

         a__  =self.unet(lowercase_,   lowercase_,   encoder_hidden_states=lowercase_	).sample

         if do_classifier_free_guidance:
           a__,				a__  =noise_pred.chunk(2	)
           a__  =noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

         a__,				a__  =self.noise_step(
             base=lowercase_,   model_input=lowercase_,   model_output=lowercase_,   timestep=lowercase_,   )

         a__  =model_input

     return coupled_latents



   @torch.no_grad()
   def __call__(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_ = 3.0,   lowercase_ = 50,   lowercase_ = 0.8,   lowercase_ = None,   lowercase_ = None,   lowercase_ = "pil",   )							->   List[Any]:






     """simple docstring"""






     a__  =guidance_scale > 1.0

     a__  =self.image_processor.preprocess(lowercase_	)

     a__  =self._encode_prompt(lowercase_,   lowercase_,   lowercase_	)
     a__  =self._encode_prompt(lowercase_,   lowercase_,   lowercase_	)

     self.scheduler.set_timesteps(lowercase_,   self.device	)

     a__  =num_inference_steps - int(num_inference_steps * strength	)
     a__  =self.scheduler.timesteps[t_limit:]
     a__  =fwd_timesteps.flip(0	)

     a__  =self.prepare_latents(lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)

     for i, t in tqdm(enumerate(lowercase_	),   total=len(lowercase_	)	):
       # j - model_input index, k - base index
       for k in range(2	):
         a__  =k ^ 1

         if self.leapfrog_steps:
           if i % 2 == 1:
             a__,				a__  =j, k

         a__  =coupled_latents[j]
         a__  =coupled_latents[k]

         a__  =torch.cat([model_input] * 2	) if do_classifier_free_guidance else model_input

         a__  =self.unet(lowercase_,   lowercase_,   encoder_hidden_states=lowercase_	).sample

         if do_classifier_free_guidance:
           a__,				a__  =noise_pred.chunk(2	)
           a__  =noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

         a__,				a__  =self.denoise_step(
             base=lowercase_,   model_input=lowercase_,   model_output=lowercase_,   timestep=lowercase_,   )

         a__  =model_input

       a__  =self.denoise_mixing_layer(x=coupled_latents[0],   y=coupled_latents[1]	)

     # either one is fine
     a__  =coupled_latents[0]

     if output_type not in ["latent", "pt", "np", "pil"]:
       a__  =(
           F"""the output_type {output_type} is outdated. Please make sure to set it to one of these instead: """
           '''`pil`, `np`, `pt`, `latent`'''
       )
       deprecate('''Unsupported output_type''',   '''1.0.0''',   lowercase_,   standard_warn=lowercase_	)
       a__  ='''np'''

     if output_type == "latent":
       a__  =final_latent
     else:
       a__  =self.decode_latents(lowercase_	)
       a__  =self.image_processor.postprocess(lowercase_,   output_type=lowercase_	)

     return image
