


"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class 						snake_case_(				a__							,    a__							,    a__							,    unittest.TestCase	):
     __UpperCamelCase     =    StableDiffusionInpaintPipeline
     __UpperCamelCase     =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
     __UpperCamelCase     =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     __UpperCamelCase     =    frozenset(
         []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     __UpperCamelCase     =    frozenset([]	)






     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           torch.manual_seed(0 )
           lowerCAmelCase    :		Dict				=      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    , attention_head_dim=(2, 4)    , use_linear_projection=UpperCamelCase_    , )
           lowerCAmelCase    :		List[Any]				=      PNDMScheduler(skip_prk_steps=UpperCamelCase_ )
           torch.manual_seed(0 )
           lowerCAmelCase    :		Union[str, 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    , sample_size=1_2_8    , )
           torch.manual_seed(0 )
           lowerCAmelCase    :		Union[str, 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    , hidden_act='''gelu'''    , projection_dim=5_1_2    , )
           lowerCAmelCase    :		Any				=      CLIPTextModel(UpperCamelCase_ )
           lowerCAmelCase    :		str				=      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

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






     def    lowerCamelCase__   (		self     :    List[str]    , UpperCamelCase_     :    Optional[int]    , UpperCamelCase_     :    Dict=0 ):
           # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
           lowerCAmelCase    :		Any				=      floats_tensor((1, 3, 3_2, 3_2)    , rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
           lowerCAmelCase    :		str				=      image.cpu().permute(0    , 2    , 3    , 1 )[0]
           lowerCAmelCase    :		Union[str, Any]				=      Image.fromarray(np.uinta(UpperCamelCase_ ) ).convert('''RGB''' ).resize((6_4, 6_4) )
           lowerCAmelCase    :		Any				=      Image.fromarray(np.uinta(image + 4 ) ).convert('''RGB''' ).resize((6_4, 6_4) )
           if str(UpperCamelCase_ ).startswith('''mps''' ):
                 lowerCAmelCase    :		Optional[Any]				=      torch.manual_seed(UpperCamelCase_ )
           else:
                 lowerCAmelCase    :		Dict				=      torch.Generator(device=UpperCamelCase_ ).manual_seed(UpperCamelCase_ )
           lowerCAmelCase    :		Optional[int]				=      {
               '''prompt''': '''A painting of a squirrel eating a burger''',
               '''image''': init_image,
               '''mask_image''': mask_image,
               '''generator''': generator,
               '''num_inference_steps''': 2,
               '''guidance_scale''': 6.0,
               '''output_type''': '''numpy''',
           }
           return inputs






     def    lowerCamelCase__   (		self     :    Dict ):
           lowerCAmelCase    :		Union[str, Any]				=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
           lowerCAmelCase    :		Dict				=      self.get_dummy_components()
           lowerCAmelCase    :		Any				=      StableDiffusionInpaintPipeline(**UpperCamelCase_ )
           lowerCAmelCase    :		Optional[Any]				=      sd_pipe.to(UpperCamelCase_ )
           sd_pipe.set_progress_bar_config(disable=UpperCamelCase_ )

           lowerCAmelCase    :		Optional[int]				=      self.get_dummy_inputs(UpperCamelCase_ )
           lowerCAmelCase    :		Tuple				=      sd_pipe(**UpperCamelCase_ ).images
           lowerCAmelCase    :		Tuple				=      image[0, -3:, -3:, -1]

           assert image.shape == (1, 6_4, 6_4, 3)
           lowerCAmelCase    :		Optional[Any]				=      np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476] )

           assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2






     def    lowerCamelCase__   (		self     :    str ):
           super().test_inference_batch_single_identical(expected_max_diff=3E-3 )



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






     def    lowerCamelCase__   (		self     :    List[Any] ):
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()






     def    lowerCamelCase__   (		self     :    str ):
           lowerCAmelCase    :		Tuple				=      load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/sd2-inpaint/init_image.png''' )
           lowerCAmelCase    :		Tuple				=      load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
           lowerCAmelCase    :		List[str]				=      load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
               '''/yellow_cat_sitting_on_a_park_bench.npy''' )

           lowerCAmelCase    :		Union[str, Any]				=      '''stabilityai/stable-diffusion-2-inpainting'''
           lowerCAmelCase    :		Tuple				=      StableDiffusionInpaintPipeline.from_pretrained(UpperCamelCase_    , safety_checker=UpperCamelCase_ )
           pipe.to(UpperCamelCase_ )
           pipe.set_progress_bar_config(disable=UpperCamelCase_ )
           pipe.enable_attention_slicing()

           lowerCAmelCase    :		int				=      '''Face of a yellow cat, high resolution, sitting on a park bench'''

           lowerCAmelCase    :		List[str]				=      torch.manual_seed(0 )
           lowerCAmelCase    :		int				=      pipe(
               prompt=UpperCamelCase_    , image=UpperCamelCase_    , mask_image=UpperCamelCase_    , generator=UpperCamelCase_    , output_type='''np'''    , )
           lowerCAmelCase    :		Optional[int]				=      output.images[0]

           assert image.shape == (5_1_2, 5_1_2, 3)
           assert np.abs(expected_image - image ).max() < 9E-3






     def    lowerCamelCase__   (		self     :    Tuple ):
           lowerCAmelCase    :		Optional[Any]				=      load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/sd2-inpaint/init_image.png''' )
           lowerCAmelCase    :		str				=      load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )
           lowerCAmelCase    :		int				=      load_numpy(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'''
               '''/yellow_cat_sitting_on_a_park_bench_fp16.npy''' )

           lowerCAmelCase    :		int				=      '''stabilityai/stable-diffusion-2-inpainting'''
           lowerCAmelCase    :		Any				=      StableDiffusionInpaintPipeline.from_pretrained(
               UpperCamelCase_    , torch_dtype=torch.floataa    , safety_checker=UpperCamelCase_    , )
           pipe.to(UpperCamelCase_ )
           pipe.set_progress_bar_config(disable=UpperCamelCase_ )
           pipe.enable_attention_slicing()

           lowerCAmelCase    :		str				=      '''Face of a yellow cat, high resolution, sitting on a park bench'''

           lowerCAmelCase    :		Optional[Any]				=      torch.manual_seed(0 )
           lowerCAmelCase    :		Tuple				=      pipe(
               prompt=UpperCamelCase_    , image=UpperCamelCase_    , mask_image=UpperCamelCase_    , generator=UpperCamelCase_    , output_type='''np'''    , )
           lowerCAmelCase    :		Tuple				=      output.images[0]

           assert image.shape == (5_1_2, 5_1_2, 3)
           assert np.abs(expected_image - image ).max() < 5E-1






     def    lowerCamelCase__   (		self     :    Any ):
           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()
           torch.cuda.reset_peak_memory_stats()

           lowerCAmelCase    :		Tuple				=      load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
               '''/sd2-inpaint/init_image.png''' )
           lowerCAmelCase    :		Any				=      load_image(
               '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''' )

           lowerCAmelCase    :		Union[str, Any]				=      '''stabilityai/stable-diffusion-2-inpainting'''
           lowerCAmelCase    :		List[str]				=      PNDMScheduler.from_pretrained(UpperCamelCase_    , subfolder='''scheduler''' )
           lowerCAmelCase    :		Optional[int]				=      StableDiffusionInpaintPipeline.from_pretrained(
               UpperCamelCase_    , safety_checker=UpperCamelCase_    , scheduler=UpperCamelCase_    , torch_dtype=torch.floataa    , )
           pipe.to(UpperCamelCase_ )
           pipe.set_progress_bar_config(disable=UpperCamelCase_ )
           pipe.enable_attention_slicing(1 )
           pipe.enable_sequential_cpu_offload()

           lowerCAmelCase    :		int				=      '''Face of a yellow cat, high resolution, sitting on a park bench'''

           lowerCAmelCase    :		Tuple				=      torch.manual_seed(0 )
           lowerCAmelCase    :		Dict				=      pipe(
               prompt=UpperCamelCase_    , image=UpperCamelCase_    , mask_image=UpperCamelCase_    , generator=UpperCamelCase_    , num_inference_steps=2    , output_type='''np'''    , )

           lowerCAmelCase    :		List[str]				=      torch.cuda.max_memory_allocated()
           # make sure that less than 2.65 GB is allocated
           assert mem_bytes < 2.65 * 1_0**9









from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


snake_case__					:							int													=     transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)



def _a				(	lowerCamelCase:   List[Any] )	->						List[Any]:







      '''simple docstring'''






      if isinstance(lowerCamelCase					,				torch.Tensor ):
            return image
      elif isinstance(lowerCamelCase					,				PIL.Image.Image ):
            __A          =				[image]

      __A          =				[trans(img.convert('''RGB''' ) ) for img in image]
      __A          =				torch.stack(lowerCamelCase )
      return image







class A_					(			_lowerCamelCase   ):




   def __init__(self	:List[str]    ,   _UpperCamelCase	:List[Any]    ,   _UpperCamelCase	:List[str] )->					List[Any]:
         super().__init__()

         # make sure scheduler can always be converted to DDIM
         __A          =				DDIMScheduler.from_config(scheduler.config )

         self.register_modules(unet=_UpperCamelCase    ,   scheduler=_UpperCamelCase )




   def _lowerCAmelCase      (self	:int    ,   _UpperCamelCase	:Optional[Any] )->					Union[str, Any]:
         if strength < 0 or strength > 1:
               raise ValueError(f"""The value of strength should in [0.0, 1.0] but is {strength}""" )




   def _lowerCAmelCase      (self	:Optional[Any]    ,   _UpperCamelCase	:Dict    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:List[str] )->					Union[str, Any]:
         # get the original timestep using init_timestep
         __A          =				min(int(num_inference_steps * strength )    ,   _UpperCamelCase )

         __A          =				max(num_inference_steps - init_timestep    ,   0 )
         __A          =				self.scheduler.timesteps[t_start:]

         return timesteps, num_inference_steps - t_start




   def _lowerCAmelCase      (self	:str    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:Dict=None )->					List[str]:
         if not isinstance(_UpperCamelCase    ,   (torch.Tensor, PIL.Image.Image, list) ):
               raise ValueError(
                   f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_UpperCamelCase )}""" )

         __A          =				image.to(device=_UpperCamelCase    ,   dtype=_UpperCamelCase )

         if isinstance(_UpperCamelCase    ,   _UpperCamelCase ) and len(_UpperCamelCase ) != batch_size:
               raise ValueError(
                   f"""You have passed a list of generators of length {len(_UpperCamelCase )}, but requested an effective batch"""
                   f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )

         __A          =				init_latents.shape
         __A          =				randn_tensor(_UpperCamelCase    ,   generator=_UpperCamelCase    ,   device=_UpperCamelCase    ,   dtype=_UpperCamelCase )

         # get latents
         print('''add noise to latents at timestep'''    ,   _UpperCamelCase )
         __A          =				self.scheduler.add_noise(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )
         __A          =				init_latents

         return latents






   @torch.no_grad()
   def __call__(self	:List[str]    ,   _UpperCamelCase	:Union[torch.FloatTensor, PIL.Image.Image] = None    ,   _UpperCamelCase	:float = 0.8    ,   _UpperCamelCase	:int = 1    ,   _UpperCamelCase	:Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,   _UpperCamelCase	:float = 0.0    ,   _UpperCamelCase	:int = 50    ,   _UpperCamelCase	:Optional[bool] = None    ,   _UpperCamelCase	:Optional[str] = "pil"    ,   _UpperCamelCase	:bool = True    ,   )->					Union[ImagePipelineOutput, Tuple]:
         self.check_inputs(_UpperCamelCase )

         # 2. Preprocess image
         __A          =				preprocess(_UpperCamelCase )

         # 3. set timesteps
         self.scheduler.set_timesteps(_UpperCamelCase    ,   device=self.device )
         __A   ,						__A          =				self.get_timesteps(_UpperCamelCase    ,   _UpperCamelCase    ,   self.device )
         __A          =				timesteps[:1].repeat(_UpperCamelCase )

         # 4. Prepare latent variables
         __A          =				self.prepare_latents(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   self.unet.dtype    ,   self.device    ,   _UpperCamelCase )
         __A          =				latents

         # 5. Denoising loop
         for t in self.progress_bar(_UpperCamelCase ):
               # 1. predict noise model_output
               __A          =				self.unet(_UpperCamelCase    ,   _UpperCamelCase ).sample

               # 2. predict previous mean of image x_t-1 and add variance depending on eta
               # eta corresponds to η in paper and should be between [0, 1]
               # do x_t -> x_t-1
               __A          =				self.scheduler.step(
                   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   eta=_UpperCamelCase    ,   use_clipped_model_output=_UpperCamelCase    ,   generator=_UpperCamelCase    ,   ).prev_sample

         __A          =				(image / 2 + 0.5).clamp(0    ,   1 )
         __A          =				image.cpu().permute(0    ,   2    ,   3    ,   1 ).numpy()
         if output_type == "pil":
               __A          =				self.numpy_to_pil(_UpperCamelCase )

         if not return_dict:
               return (image, latent_timestep.item())

         return ImagePipelineOutput(images=_UpperCamelCase )





