






'''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 							lowerCAmelCase					(							lowerCamelCase__							,      lowerCamelCase__							,      lowerCamelCase__							,      unittest.TestCase   ):







   """simple docstring"""







   _A						=   StableDiffusionControlNetInpaintPipeline
   _A						=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   _A						=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   _A						=   frozenset({'control_image'}   )  # skip `image` and `mask` for now, only test for control_image
   _A						=   TEXT_TO_IMAGE_IMAGE_PARAMS



   def    __magic_name__    ( self			)    ->   Any:
    torch.manual_seed(0			)
    __a    :	Tuple									=						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    :	Optional[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    :	List[Any]									=						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    :	Optional[Any]									=						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-0_5   ,   num_attention_heads=4   ,   num_hidden_layers=5   ,   pad_token_id=1   ,   vocab_size=1000   ,   )
    __a    :	Optional[int]									=						CLIPTextModel(_A			)
    __a    :	List[str]									=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

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



   def    __magic_name__    ( self   ,   _A   ,   _A=0			)    ->   Any:
    if str(_A			).startswith('mps'			):
     __a    :	Optional[Any]									=						torch.manual_seed(_A			)
    else:
     __a    :	int									=						torch.Generator(device=_A			).manual_seed(_A			)

    __a    :	int									=						2
    __a    :	List[str]									=						randn_tensor(
        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)   ,   generator=_A   ,   device=torch.device(_A			)   ,   )
    __a    :	Optional[int]									=						floats_tensor((1, 3, 32, 32)   ,   rng=random.Random(_A			)			).to(_A			)
    __a    :	List[str]									=						init_image.cpu().permute(0   ,   2   ,   3   ,   1			)[0]

    __a    :	Optional[int]									=						Image.fromarray(np.uinta(_A			)			).convert('RGB'			).resize((64, 64)			)
    __a    :	int									=						Image.fromarray(np.uinta(init_image + 4			)			).convert('RGB'			).resize((64, 64)			)

    __a    :	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    __magic_name__    ( self			)    ->   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    __magic_name__    ( self			)    ->   int:
    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3			)



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




class 							lowerCAmelCase					(							lowerCamelCase__   ):







   """simple docstring"""







   _A						=   StableDiffusionControlNetInpaintPipeline
   _A						=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   _A						=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   _A						=   frozenset([]   )



   def    __magic_name__    ( self			)    ->   List[Any]:
    torch.manual_seed(0			)
    __a    :	Union[str, 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    :	Dict									=						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    :	Union[str, Any]									=						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    :	Optional[int]									=						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    :	Union[str, Any]									=						CLIPTextConfig(
        bos_token_id=0   ,   eos_token_id=2   ,   hidden_size=32   ,   intermediate_size=37   ,   layer_norm_eps=1E-0_5   ,   num_attention_heads=4   ,   num_hidden_layers=5   ,   pad_token_id=1   ,   vocab_size=1000   ,   )
    __a    :	Optional[int]									=						CLIPTextModel(_A			)
    __a    :	Dict									=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

    __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




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







   """simple docstring"""







   _A						=   StableDiffusionControlNetInpaintPipeline
   _A						=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
   _A						=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS



   def    __magic_name__    ( self			)    ->   Dict:
    torch.manual_seed(0			)
    __a    :	int									=						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(_A			):
     if isinstance(_A   ,   torch.nn.Convad			):
      torch.nn.init.normal(m.weight			)
      m.bias.data.fill_(1.0			)

    __a    :	Dict									=						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(_A			)

    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(_A			)

    torch.manual_seed(0			)
    __a    :	Any									=						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    :	int									=						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    :	Tuple									=						CLIPTextConfig(
        bos_token_id=0   ,   eos_token_id=2   ,   hidden_size=32   ,   intermediate_size=37   ,   layer_norm_eps=1E-0_5   ,   num_attention_heads=4   ,   num_hidden_layers=5   ,   pad_token_id=1   ,   vocab_size=1000   ,   )
    __a    :	Union[str, Any]									=						CLIPTextModel(_A			)
    __a    :	List[str]									=						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

    __a    :	Optional[int]									=						MultiControlNetModel([controlneta, controlneta]			)

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



   def    __magic_name__    ( self   ,   _A   ,   _A=0			)    ->   Tuple:
    if str(_A			).startswith('mps'			):
     __a    :	List[Any]									=						torch.manual_seed(_A			)
    else:
     __a    :	Tuple									=						torch.Generator(device=_A			).manual_seed(_A			)

    __a    :	Dict									=						2

    __a    :	List[str]									=						[
        randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)   ,   generator=_A   ,   device=torch.device(_A			)   ,   ),
        randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)   ,   generator=_A   ,   device=torch.device(_A			)   ,   ),
    ]
    __a    :	Optional[int]									=						floats_tensor((1, 3, 32, 32)   ,   rng=random.Random(_A			)			).to(_A			)
    __a    :	Any									=						init_image.cpu().permute(0   ,   2   ,   3   ,   1			)[0]

    __a    :	str									=						Image.fromarray(np.uinta(_A			)			).convert('RGB'			).resize((64, 64)			)
    __a    :	Tuple									=						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    __magic_name__    ( self			)    ->   Union[str, Any]:
    __a    :	Union[str, Any]									=						self.get_dummy_components()
    __a    :	str									=						self.pipeline_class(**_A			)
    pipe.to(_A			)

    __a    :	List[str]									=						10.0
    __a    :	Tuple									=						4

    __a    :	List[Any]									=						self.get_dummy_inputs(_A			)
    __a    :	Tuple									=						steps
    __a    :	Optional[Any]									=						scale
    __a    :	Tuple									=						pipe(**_A			)[0]

    __a    :	str									=						self.get_dummy_inputs(_A			)
    __a    :	Optional[int]									=						steps
    __a    :	Optional[Any]									=						scale
    __a    :	Dict									=						pipe(**_A   ,   control_guidance_start=0.1   ,   control_guidance_end=0.2			)[0]

    __a    :	int									=						self.get_dummy_inputs(_A			)
    __a    :	Any									=						steps
    __a    :	List[Any]									=						scale
    __a    :	int									=						pipe(**_A   ,   control_guidance_start=[0.1, 0.3]   ,   control_guidance_end=[0.2, 0.7]			)[0]

    __a    :	Optional[Any]									=						self.get_dummy_inputs(_A			)
    __a    :	Optional[int]									=						steps
    __a    :	Union[str, Any]									=						scale
    __a    :	Union[str, Any]									=						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    __magic_name__    ( self			)    ->   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    __magic_name__    ( self			)    ->   str:
    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3			)



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



   def    __magic_name__    ( self			)    ->   Any:
    __a    :	Union[str, Any]									=						self.get_dummy_components()
    __a    :	Union[str, Any]									=						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 							lowerCAmelCase					(							unittest.TestCase   ):







   """simple docstring"""







   def    __magic_name__    ( self			)    ->   Any:
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()



   def    __magic_name__    ( self			)    ->   Union[str, Any]:
    __a    :	Tuple									=						ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'			)

    __a    :	Union[str, Any]									=						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    :	Union[str, Any]									=						torch.Generator(device='cpu'			).manual_seed(0			)
    __a    :	Union[str, Any]									=						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    :	List[Any]									=						'pitch black hole'

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

    __a    :	List[Any]									=						pipe(
        _A   ,   image=_A   ,   mask_image=_A   ,   control_image=_A   ,   generator=_A   ,   output_type='np'   ,   num_inference_steps=3   ,   )

    __a    :	str									=						output.images[0]

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

    __a    :	List[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    __magic_name__    ( self			)    ->   Optional[int]:
    __a    :	Dict									=						ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'			)

    __a    :	Tuple									=						StableDiffusionControlNetInpaintPipeline.from_pretrained(
        'runwayml/stable-diffusion-v1-5'   ,   safety_checker=_A   ,   controlnet=_A			)
    __a    :	Optional[int]									=						DDIMScheduler.from_config(pipe.scheduler.config			)
    pipe.enable_model_cpu_offload()
    pipe.set_progress_bar_config(disable=_A			)

    __a    :	Optional[int]									=						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    :	List[str]									=						init_image.resize((512, 512)			)

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

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

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

    __a    :	Optional[Any]									=						make_inpaint_condition(_A   ,   _A			)

    __a    :	Any									=						pipe(
        _A   ,   image=_A   ,   mask_image=_A   ,   control_image=_A   ,   guidance_scale=9.0   ,   eta=1.0   ,   generator=_A   ,   num_inference_steps=20   ,   output_type='np'   ,   )
    __a    :	List[Any]									=						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/boy_ray_ban.npy'			)

    assert np.abs(expected_image - image			).max() < 9E-2












'''simple docstring'''


def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):
 if len(SCREAMING_SNAKE_CASE__		) <= 1:
  return data
 else:
  return [
      *quick_sort([e for e in data[1:] if e <= data[0]]		),
      data[0],
      *quick_sort([e for e in data[1:] if e > data[0]]		),
  ]


if __name__ == "__main__":
  import doctest

  doctest.testmod()




