






"""simple docstring"""


# 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 __a ( snake_case_			,snake_case_			,snake_case_			,unittest.TestCase					):
  __lowercase     :     Any       						=       StableDiffusionControlNetInpaintPipeline
  __lowercase     :     Dict       						=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  __lowercase     :     str       						=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  __lowercase     :     Union[str, Any]       						=       frozenset({'control_image'}					)  # skip `image` and `mask` for now, only test for control_image
  __lowercase     :     Tuple       						=       TEXT_TO_IMAGE_IMAGE_PARAMS



  def     A_					(self	:		Optional[int] )		->				str:
   torch.manual_seed(0 )
   __A						:							Union[str, Any]      =						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						:							Any      =						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						:							Optional[int]      =						DDIMScheduler(
       beta_start=0.0_0085					,      beta_end=0.012					,      beta_schedule='scaled_linear'					,      clip_sample=lowercase_					,      set_alpha_to_one=lowercase_					,      )
   torch.manual_seed(0 )
   __A						:							Dict      =						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						:							Optional[Any]      =						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=1_000					,      )
   __A						:							str      =						CLIPTextModel(lowercase_ )
   __A						:							Union[str, Any]      =						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

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



  def     A_					(self	:		str					,      lowercase_	:		Tuple					,      lowercase_	:		int=0 )		->				Optional[int]:
   if str(lowercase_ ).startswith('mps' ):
    __A						:							Tuple      =						torch.manual_seed(lowercase_ )
   else:
    __A						:							str      =						torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )

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

   __A						:							int      =						Image.fromarray(np.uinta(lowercase_ ) ).convert('RGB' ).resize((64, 64) )
   __A						:							Union[str, Any]      =						Image.fromarray(np.uinta(init_image + 4 ) ).convert('RGB' ).resize((64, 64) )

   __A						:							int      =						{
       '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     A_					(self	:		Dict )		->				Optional[int]:
   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     A_					(self	:		Tuple )		->				List[str]:
   self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )



  def     A_					(self	:		Any )		->				str:
   self._test_inference_batch_single_identical(expected_max_diff=2E-3 )






class __a ( snake_case_					):
  __lowercase     :     Optional[int]       						=       StableDiffusionControlNetInpaintPipeline
  __lowercase     :     str       						=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  __lowercase     :     Union[str, Any]       						=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  __lowercase     :     Optional[int]       						=       frozenset([]					)



  def     A_					(self	:		Dict )		->				str:
   torch.manual_seed(0 )
   __A						:							Any      =						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						:							int      =						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						:							Optional[int]      =						DDIMScheduler(
       beta_start=0.0_0085					,      beta_end=0.012					,      beta_schedule='scaled_linear'					,      clip_sample=lowercase_					,      set_alpha_to_one=lowercase_					,      )
   torch.manual_seed(0 )
   __A						:							Dict      =						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						:							Optional[Any]      =						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=1_000					,      )
   __A						:							Tuple      =						CLIPTextModel(lowercase_ )
   __A						:							str      =						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

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






class __a ( snake_case_			,snake_case_			,unittest.TestCase					):
  __lowercase     :     Union[str, Any]       						=       StableDiffusionControlNetInpaintPipeline
  __lowercase     :     Any       						=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  __lowercase     :     str       						=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS



  def     A_					(self	:		Any )		->				List[Any]:
   torch.manual_seed(0 )
   __A						:							List[Any]      =						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_	:		Optional[int] ):
    if isinstance(lowercase_					,      torch.nn.Convad ):
     torch.nn.init.normal(m.weight )
     m.bias.data.fill_(1.0 )

   __A						:							Any      =						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						:							Any      =						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						:							Union[str, Any]      =						DDIMScheduler(
       beta_start=0.0_0085					,      beta_end=0.012					,      beta_schedule='scaled_linear'					,      clip_sample=lowercase_					,      set_alpha_to_one=lowercase_					,      )
   torch.manual_seed(0 )
   __A						:							List[str]      =						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						:							List[str]      =						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=1_000					,      )
   __A						:							Optional[Any]      =						CLIPTextModel(lowercase_ )
   __A						:							Any      =						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

   __A						:							List[Any]      =						MultiControlNetModel([controlneta, controlneta] )

   __A						:							List[Any]      =						{
       'unet': unet,
       'controlnet': controlnet,
       'scheduler': scheduler,
       'vae': vae,
       'text_encoder': text_encoder,
       'tokenizer': tokenizer,
       'safety_checker': None,
       'feature_extractor': None,
   }
   return components



  def     A_					(self	:		Optional[Any]					,      lowercase_	:		Dict					,      lowercase_	:		List[str]=0 )		->				List[Any]:
   if str(lowercase_ ).startswith('mps' ):
    __A						:							Optional[int]      =						torch.manual_seed(lowercase_ )
   else:
    __A						:							Dict      =						torch.Generator(device=lowercase_ ).manual_seed(lowercase_ )

   __A						:							Optional[int]      =						2

   __A						:							Dict      =						[
       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						:							Any      =						floats_tensor((1, 3, 32, 32)					,      rng=random.Random(lowercase_ ) ).to(lowercase_ )
   __A						:							str      =						init_image.cpu().permute(0					,      2					,      3					,      1 )[0]

   __A						:							List[Any]      =						Image.fromarray(np.uinta(lowercase_ ) ).convert('RGB' ).resize((64, 64) )
   __A						:							Optional[int]      =						Image.fromarray(np.uinta(init_image + 4 ) ).convert('RGB' ).resize((64, 64) )

   __A						:							str      =						{
       '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     A_					(self	:		Union[str, Any] )		->				int:
   __A						:							Tuple      =						self.get_dummy_components()
   __A						:							Optional[int]      =						self.pipeline_class(**lowercase_ )
   pipe.to(lowercase_ )

   __A						:							List[Any]      =						10.0
   __A						:							Optional[Any]      =						4

   __A						:							Dict      =						self.get_dummy_inputs(lowercase_ )
   __A						:							Optional[Any]      =						steps
   __A						:							Optional[Any]      =						scale
   __A						:							Dict      =						pipe(**lowercase_ )[0]

   __A						:							Optional[Any]      =						self.get_dummy_inputs(lowercase_ )
   __A						:							Any      =						steps
   __A						:							List[str]      =						scale
   __A						:							Tuple      =						pipe(**lowercase_					,      control_guidance_start=0.1					,      control_guidance_end=0.2 )[0]

   __A						:							List[Any]      =						self.get_dummy_inputs(lowercase_ )
   __A						:							Dict      =						steps
   __A						:							Any      =						scale
   __A						:							Tuple      =						pipe(**lowercase_					,      control_guidance_start=[0.1, 0.3]					,      control_guidance_end=[0.2, 0.7] )[0]

   __A						:							int      =						self.get_dummy_inputs(lowercase_ )
   __A						:							List[str]      =						steps
   __A						:							Optional[Any]      =						scale
   __A						:							Dict      =						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     A_					(self	:		List[Any] )		->				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     A_					(self	:		Tuple )		->				Dict:
   self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )



  def     A_					(self	:		Optional[Any] )		->				Optional[Any]:
   self._test_inference_batch_single_identical(expected_max_diff=2E-3 )



  def     A_					(self	:		Any )		->				int:
   __A						:							Tuple      =						self.get_dummy_components()
   __A						:							int      =						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 __a ( unittest.TestCase					):



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



  def     A_					(self	:		Any )		->				Dict:
   __A						:							List[str]      =						ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny' )

   __A						:							str      =						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						:							Optional[Any]      =						torch.Generator(device='cpu' ).manual_seed(0 )
   __A						:							Tuple      =						load_image(
       'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png' ).resize((512, 512) )

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

   __A						:							Optional[int]      =						'pitch black hole'

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

   __A						:							int      =						pipe(
       lowercase_					,      image=lowercase_					,      mask_image=lowercase_					,      control_image=lowercase_					,      generator=lowercase_					,      output_type='np'					,      num_inference_steps=3					,      )

   __A						:							Optional[int]      =						output.images[0]

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

   __A						:							str      =						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     A_					(self	:		str )		->				int:
   __A						:							Union[str, Any]      =						ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint' )

   __A						:							Tuple      =						StableDiffusionControlNetInpaintPipeline.from_pretrained(
       'runwayml/stable-diffusion-v1-5'					,      safety_checker=lowercase_					,      controlnet=lowercase_ )
   __A						:							List[Any]      =						DDIMScheduler.from_config(pipe.scheduler.config )
   pipe.enable_model_cpu_offload()
   pipe.set_progress_bar_config(disable=lowercase_ )

   __A						:							List[Any]      =						torch.Generator(device='cpu' ).manual_seed(33 )

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

   __A						:							List[Any]      =						load_image(
       'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png' )
   __A						:							Tuple      =						mask_image.resize((512, 512) )

   __A						:							Optional[int]      =						'a handsome man with ray-ban sunglasses'

   def make_inpaint_condition(lowercase_	:		List[Any]					,      lowercase_	:		str ):
    __A						:							List[Any]      =						np.array(image.convert('RGB' ) ).astype(np.floataa ) / 255.0
    __A						:							Tuple      =						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						:							Union[str, Any]      =						-1.0  # set as masked pixel
    __A						:							str      =						np.expand_dims(lowercase_					,      0 ).transpose(0					,      3					,      1					,      2 )
    __A						:							Optional[Any]      =						torch.from_numpy(lowercase_ )
    return image

   __A						:							int      =						make_inpaint_condition(lowercase_					,      lowercase_ )

   __A						:							Dict      =						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						:							List[str]      =						output.images[0]

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

   __A						:							Optional[Any]      =						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













"""simple docstring"""


import importlib.metadata
import subprocess
import sys




def 					a			(  UpperCamelCase_				:					bool = False		)	->		Optional[Any]:
 __A						:							List[Any]      =						False
 if "IPython" in sys.modules:
  __A						:							List[Any]      =						'google.colab' in str(sys.modules['IPython'].get_ipython()		)

 if in_colab:
  if upgrade:
   __A						:							Dict      =						['pip', 'install', '-U', 'torch']
   subprocess.run(UpperCamelCase_  ,						check=UpperCamelCase_		)
  # get the current version of torch
  __A						:							int      =						importlib.metadata.version('torch'		)
  __A						:							int      =						torch_version[: torch_version.rindex('.'		)]
  __A						:							Any      =						F"https://storage.googleapis.com/tpu-pytorch/wheels/colab/torch_xla-{torch_version_trunc}-cp37-cp37m-linux_x86_64.whl"
  __A						:							int      =						['pip', 'install', xla_wheel]
  subprocess.run(UpperCamelCase_  ,						check=UpperCamelCase_		)
 else:
  raise RuntimeError('`install_xla` utility works only on google colab.'		)





