



# 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 						_snake_case (     lowerCamelCase   ,lowerCamelCase   ,lowerCamelCase   ,unittest.TestCase			):





 """simple docstring"""



 lowerCamelCase_       =     StableDiffusionControlNetInpaintPipeline
 lowerCamelCase_       =     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 lowerCamelCase_       =     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 lowerCamelCase_       =     frozenset({'''control_image'''}			)  # skip `image` and `mask` for now, only test for control_image
 lowerCamelCase_       =     TEXT_TO_IMAGE_IMAGE_PARAMS
 def lowercase_						(  self   )		->							int:




   """simple docstring"""







   torch.manual_seed(0   )
   _A          =      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   )
   _A          =      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   )
   _A          =      DDIMScheduler(
       beta_start=0.00_085		,      beta_end=0.012		,      beta_schedule='''scaled_linear'''		,      clip_sample=a		,      set_alpha_to_one=a		,      )
   torch.manual_seed(0   )
   _A          =      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   )
   _A          =      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		,      )
   _A          =      CLIPTextModel(a   )
   _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 lowercase_						(  self		,      a		,      a=0   )		->							Tuple:




   """simple docstring"""







   if str(a   ).startswith('''mps'''   ):
     _A          =      torch.manual_seed(a   )
   else:
     _A          =      torch.Generator(device=a   ).manual_seed(a   )

   _A          =      2
   _A          =      randn_tensor(
       (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)		,      generator=a		,      device=torch.device(a   )		,      )
   _A          =      floats_tensor((1, 3, 3_2, 3_2)		,      rng=random.Random(a   )   ).to(a   )
   _A          =      init_image.cpu().permute(0		,      2		,      3		,      1   )[0]

   _A          =      Image.fromarray(np.uinta(a   )   ).convert('''RGB'''   ).resize((6_4, 6_4)   )
   _A          =      Image.fromarray(np.uinta(init_image + 4   )   ).convert('''RGB'''   ).resize((6_4, 6_4)   )

   _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 lowercase_						(  self   )		->							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 lowercase_						(  self   )		->							Optional[Any]:




   """simple docstring"""







   self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3   )



 def lowercase_						(  self   )		->							str:




   """simple docstring"""







   self._test_inference_batch_single_identical(expected_max_diff=2e-3   )







class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 lowerCamelCase_       =     StableDiffusionControlNetInpaintPipeline
 lowerCamelCase_       =     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 lowerCamelCase_       =     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 lowerCamelCase_       =     frozenset([]			)
 def lowercase_						(  self   )		->							List[str]:




   """simple docstring"""







   torch.manual_seed(0   )
   _A          =      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   )
   _A          =      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   )
   _A          =      DDIMScheduler(
       beta_start=0.00_085		,      beta_end=0.012		,      beta_schedule='''scaled_linear'''		,      clip_sample=a		,      set_alpha_to_one=a		,      )
   torch.manual_seed(0   )
   _A          =      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   )
   _A          =      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		,      )
   _A          =      CLIPTextModel(a   )
   _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 						_snake_case (     lowerCamelCase   ,lowerCamelCase   ,unittest.TestCase			):





 """simple docstring"""



 lowerCamelCase_       =     StableDiffusionControlNetInpaintPipeline
 lowerCamelCase_       =     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 lowerCamelCase_       =     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 def lowercase_						(  self   )		->							Union[str, Any]:




   """simple docstring"""







   torch.manual_seed(0   )
   _A          =      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(a   ):
     if isinstance(a		,      torch.nn.Convad   ):
       torch.nn.init.normal(m.weight   )
       m.bias.data.fill_(1.0   )

   _A          =      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(a   )

   torch.manual_seed(0   )
   _A          =      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(a   )

   torch.manual_seed(0   )
   _A          =      DDIMScheduler(
       beta_start=0.00_085		,      beta_end=0.012		,      beta_schedule='''scaled_linear'''		,      clip_sample=a		,      set_alpha_to_one=a		,      )
   torch.manual_seed(0   )
   _A          =      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   )
   _A          =      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		,      )
   _A          =      CLIPTextModel(a   )
   _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 lowercase_						(  self		,      a		,      a=0   )		->							Optional[Any]:




   """simple docstring"""







   if str(a   ).startswith('''mps'''   ):
     _A          =      torch.manual_seed(a   )
   else:
     _A          =      torch.Generator(device=a   ).manual_seed(a   )

   _A          =      2

   _A          =      [
       randn_tensor(
           (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)		,      generator=a		,      device=torch.device(a   )		,      ),
       randn_tensor(
           (1, 3, 3_2 * controlnet_embedder_scale_factor, 3_2 * controlnet_embedder_scale_factor)		,      generator=a		,      device=torch.device(a   )		,      ),
   ]
   _A          =      floats_tensor((1, 3, 3_2, 3_2)		,      rng=random.Random(a   )   ).to(a   )
   _A          =      init_image.cpu().permute(0		,      2		,      3		,      1   )[0]

   _A          =      Image.fromarray(np.uinta(a   )   ).convert('''RGB'''   ).resize((6_4, 6_4)   )
   _A          =      Image.fromarray(np.uinta(init_image + 4   )   ).convert('''RGB'''   ).resize((6_4, 6_4)   )

   _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 lowercase_						(  self   )		->							Optional[int]:




   """simple docstring"""







   _A          =      self.get_dummy_components()
   _A          =      self.pipeline_class(**a   )
   pipe.to(a   )

   _A          =      10.0
   _A          =      4

   _A          =      self.get_dummy_inputs(a   )
   _A          =      steps
   _A          =      scale
   _A          =      pipe(**a   )[0]

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

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

   _A          =      self.get_dummy_inputs(a   )
   _A          =      steps
   _A          =      scale
   _A          =      pipe(**a		,      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 lowercase_						(  self   )		->							int:




   """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 lowercase_						(  self   )		->							int:




   """simple docstring"""







   self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3   )
 def lowercase_						(  self   )		->							Dict:




   """simple docstring"""







   self._test_inference_batch_single_identical(expected_max_diff=2e-3   )



 def lowercase_						(  self   )		->							Dict:




   """simple docstring"""







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







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





 """simple docstring"""



 def lowercase_						(  self   )		->							Any:




   """simple docstring"""







   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()
 def lowercase_						(  self   )		->							Optional[int]:




   """simple docstring"""







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

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

   _A          =      torch.Generator(device='''cpu'''   ).manual_seed(0   )
   _A          =      load_image(
       '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''   ).resize((5_1_2, 5_1_2)   )

   _A          =      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)   )

   _A          =      '''pitch black hole'''

   _A          =      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)   )

   _A          =      pipe(
       a		,      image=a		,      mask_image=a		,      control_image=a		,      generator=a		,      output_type='''np'''		,      num_inference_steps=3		,      )

   _A          =      output.images[0]

   assert image.shape == (5_1_2, 5_1_2, 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 lowercase_						(  self   )		->							int:




   """simple docstring"""







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

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

   _A          =      torch.Generator(device='''cpu'''   ).manual_seed(3_3   )

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

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

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

   def make_inpaint_condition(a		,      a   ):
     _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(a		,      0   ).transpose(0		,      3		,      1		,      2   )
     _A          =      torch.from_numpy(a   )
     return image

   _A          =      make_inpaint_condition(a		,      a   )

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

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




import math
from timeit import timeit




def       UpperCAmelCase__	(							__snake_case							)				->	int:
  _A          =      0
  _A          =      abs(__snake_case							)
  while True:
    _A          =      n // 10
    digits += 1
    if n == 0:
      break
  return digits




def       UpperCAmelCase__	(							__snake_case							)				->	int:
  return 1 if n == 0 else math.floor(math.log(abs(__snake_case							)  ,			10							) + 1							)




def       UpperCAmelCase__	(							__snake_case							)				->	int:
  return len(str(abs(__snake_case							)							)							)




def       UpperCAmelCase__	(							)				->	None:
  from collections.abc import Callable

  def benchmark_a_function(__snake_case  ,			__snake_case							) -> None:
    _A          =      F'''{func.__name__}({value})'''
    _A          =      timeit(F'''__main__.{call}'''  ,			setup='''import __main__'''							)
    print(F'''{call}: {func(__snake_case							)} -- {timing} seconds'''							)

  for value in (262_144, 1_125_899_906_842_624, 1_267_650_600_228_229_401_496_703_205_376):
    for func in (num_digits, num_digits_fast, num_digits_faster):
      benchmark_a_function(__snake_case  ,			__snake_case							)
    print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()