





# 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       ):


    """simple docstring"""


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



    def  UpperCAmelCase_		(      self					):
         torch.manual_seed(0					)
         __A     :						List[str]          =    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     :						Union[str, 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     :						str          =    DDIMScheduler(
             beta_start=0.0_0_0_8_5					, beta_end=0.0_1_2					, beta_schedule='scaled_linear'					, clip_sample=_A					, set_alpha_to_one=_A					, )
         torch.manual_seed(0					)
         __A     :						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     :						int          =    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     :						List[Any]          =    CLIPTextModel(_A					)
         __A     :						Dict          =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

         __A     :						Optional[Any]          =    {
             '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					, _A					, _A=0					):
         if str(_A					).startswith('mps'					):
              __A     :						Dict          =    torch.manual_seed(_A					)
         else:
              __A     :						Union[str, Any]          =    torch.Generator(device=_A					).manual_seed(_A					)

         __A     :						Optional[Any]          =    2
         __A     :						int          =    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     :						Optional[Any]          =    init_image.cpu().permute(0					, 2					, 3					, 1					)[0]

         __A     :						Dict          =    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     :						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  UpperCAmelCase_		(      self					):
         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					):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3					)





    def  UpperCAmelCase_		(      self					):
         self._test_inference_batch_single_identical(expected_max_diff=2e-3					)





class 						_A( snake_case__       ):


    """simple docstring"""


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



    def  UpperCAmelCase_		(      self					):
         torch.manual_seed(0					)
         __A     :						Optional[int]          =    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     :						Optional[Any]          =    DDIMScheduler(
             beta_start=0.0_0_0_8_5					, beta_end=0.0_1_2					, beta_schedule='scaled_linear'					, clip_sample=_A					, set_alpha_to_one=_A					, )
         torch.manual_seed(0					)
         __A     :						List[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     :						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     :						List[str]          =    CLIPTextModel(_A					)
         __A     :						List[Any]          =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

         __A     :						Any          =    {
             '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       ):


    """simple docstring"""


    UpperCamelCase   :  Optional[int]    =     StableDiffusionControlNetInpaintPipeline
    UpperCamelCase   :  Any    =     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    UpperCamelCase   :  str    =     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS



    def  UpperCAmelCase_		(      self					):
         torch.manual_seed(0					)
         __A     :						List[str]          =    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     :						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     :						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)					, )
         controlneta.controlnet_down_blocks.apply(_A					)

         torch.manual_seed(0					)
         __A     :						Any          =    DDIMScheduler(
             beta_start=0.0_0_0_8_5					, beta_end=0.0_1_2					, beta_schedule='scaled_linear'					, clip_sample=_A					, set_alpha_to_one=_A					, )
         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     :						Dict          =    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     :						Dict          =    CLIPTextModel(_A					)
         __A     :						Optional[int]          =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

         __A     :						Tuple          =    MultiControlNetModel([controlneta, controlneta]					)

         __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  UpperCAmelCase_		(      self					, _A					, _A=0					):
         if str(_A					).startswith('mps'					):
              __A     :						Union[str, Any]          =    torch.manual_seed(_A					)
         else:
              __A     :						Optional[int]          =    torch.Generator(device=_A					).manual_seed(_A					)

         __A     :						Dict          =    2

         __A     :						Dict          =    [
             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     :						Tuple          =    floats_tensor((1, 3, 32, 32)					, rng=random.Random(_A					)					).to(_A					)
         __A     :						Optional[int]          =    init_image.cpu().permute(0					, 2					, 3					, 1					)[0]

         __A     :						Dict          =    Image.fromarray(np.uinta(_A					)					).convert('RGB'					).resize((64, 64)					)
         __A     :						Any          =    Image.fromarray(np.uinta(init_image + 4					)					).convert('RGB'					).resize((64, 64)					)

         __A     :						Optional[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  UpperCAmelCase_		(      self					):
         __A     :						Dict          =    self.get_dummy_components()
         __A     :						Optional[Any]          =    self.pipeline_class(**_A					)
         pipe.to(_A					)

         __A     :						Dict          =    10.0
         __A     :						Tuple          =    4

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

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

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

         __A     :						int          =    self.get_dummy_inputs(_A					)
         __A     :						str          =    steps
         __A     :						Tuple          =    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  UpperCAmelCase_		(      self					):
         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					):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3					)



    def  UpperCAmelCase_		(      self					):
         self._test_inference_batch_single_identical(expected_max_diff=2e-3					)





    def  UpperCAmelCase_		(      self					):
         __A     :						List[str]          =    self.get_dummy_components()
         __A     :						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 						_A( unittest.TestCase       ):


    """simple docstring"""


    def  UpperCAmelCase_		(      self					):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()



    def  UpperCAmelCase_		(      self					):
         __A     :						Any          =    ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'					)

         __A     :						List[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     :						Dict          =    load_image(
             'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'					).resize((512, 512)					)

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

         __A     :						Union[str, Any]          =    'pitch black hole'

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

         __A     :						Optional[int]          =    pipe(
             _A					, image=_A					, mask_image=_A					, control_image=_A					, generator=_A					, output_type='np'					, num_inference_steps=3					, )

         __A     :						int          =    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  UpperCAmelCase_		(      self					):
         __A     :						int          =    ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'					)

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

         __A     :						Dict          =    torch.Generator(device='cpu'					).manual_seed(33					)

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

         __A     :						Any          =    'a handsome man with ray-ban sunglasses'

         def make_inpaint_condition(_A					, _A					):
              __A     :						List[Any]          =    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     :						Any          =    -1.0  # set as masked pixel
              __A     :						Any          =    np.expand_dims(_A					, 0					).transpose(0					, 3					, 1					, 2					)
              __A     :						Optional[int]          =    torch.from_numpy(_A					)
              return image

         __A     :						List[str]          =    make_inpaint_condition(_A					, _A					)

         __A     :						Union[str, 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     :						Dict          =    output.images[0]

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

         __A     :						Union[str, 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











from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase						:       str        =					logging.get_logger(__name__)

UpperCAmelCase						:       Optional[Any]        =					{
    '''MBZUAI/swiftformer-xs''': '''https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json''',
}





class 						_A( snake_case__       ):


				"""simple docstring"""


				UpperCamelCase   :  Union[str, Any]    =     '''swiftformer'''



				def __init__(      self					, _A=3					, _A=[3, 3, 6, 4]					, _A=[48, 56, 112, 220]					, _A=4					, _A=[True, True, True, True]					, _A="gelu"					, _A=3					, _A=2					, _A=1					, _A=0.0					, _A=True					, _A=1e-5					, _A=1e-5					, **_A					, ):
									super().__init__(**_A					)
									__A     :						List[Any]          =    num_channels
									__A     :						List[Any]          =    depths
									__A     :						int          =    embed_dims
									__A     :						Union[str, Any]          =    mlp_ratio
									__A     :						List[str]          =    downsamples
									__A     :						str          =    hidden_act
									__A     :						Any          =    down_patch_size
									__A     :						Any          =    down_stride
									__A     :						Any          =    down_pad
									__A     :						List[Any]          =    drop_path_rate
									__A     :						str          =    use_layer_scale
									__A     :						Dict          =    layer_scale_init_value
									__A     :						Union[str, Any]          =    batch_norm_eps





class 						_A( snake_case__       ):


				"""simple docstring"""


				UpperCamelCase   :  Optional[Any]    =     version.parse('''1.11'''       )



				@property
				def  UpperCAmelCase_		(      self					):
									return OrderedDict(
									    [
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									    ]					)





				@property
				def  UpperCAmelCase_		(      self					):
									return 1e-4




