

"""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    (							_lowerCamelCase,						_lowerCamelCase,						_lowerCamelCase,						unittest.TestCase					):



    """simple docstring"""



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

    def 					UpperCamelCase						(  self:			Tuple							):






         """simple docstring"""


         torch.manual_seed(0							)
         A__				      =	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__				      =	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__				      =	DDIMScheduler(
             beta_start=0.00_085     ,    beta_end=0.012     ,    beta_schedule="""scaled_linear"""     ,    clip_sample=UpperCamelCase     ,    set_alpha_to_one=UpperCamelCase     ,    )
         torch.manual_seed(0							)
         A__				      =	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__				      =	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=10_00     ,    )
         A__				      =	CLIPTextModel(UpperCamelCase							)
         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 					UpperCamelCase						(  self:			Any     ,    UpperCamelCase:			Any     ,    UpperCamelCase:			List[Any]=0							):






         """simple docstring"""


         if str(UpperCamelCase							).startswith("""mps"""							):
              A__				      =	torch.manual_seed(UpperCamelCase							)
         else:
              A__				      =	torch.Generator(device=UpperCamelCase							).manual_seed(UpperCamelCase							)

         A__				      =	2
         A__				      =	randn_tensor(
             (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)     ,    generator=UpperCamelCase     ,    device=torch.device(UpperCamelCase							)     ,    )
         A__				      =	floats_tensor((1, 3, 32, 32)     ,    rng=random.Random(UpperCamelCase							)							).to(UpperCamelCase							)
         A__				      =	init_image.cpu().permute(0     ,    2     ,    3     ,    1							)[0]

         A__				      =	Image.fromarray(np.uinta(UpperCamelCase							)							).convert("""RGB"""							).resize((64, 64)							)
         A__				      =	Image.fromarray(np.uinta(init_image + 4							)							).convert("""RGB"""							).resize((64, 64)							)

         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 					UpperCamelCase						(  self:			str							):






         """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 					UpperCamelCase						(  self:			Any							):






         """simple docstring"""


         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3							)





    def 					UpperCamelCase						(  self:			str							):






         """simple docstring"""


         self._test_inference_batch_single_identical(expected_max_diff=2e-3							)



class        a    (							_lowerCamelCase					):



    """simple docstring"""



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

    def 					UpperCamelCase						(  self:			List[str]							):






         """simple docstring"""


         torch.manual_seed(0							)
         A__				      =	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__				      =	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__				      =	DDIMScheduler(
             beta_start=0.00_085     ,    beta_end=0.012     ,    beta_schedule="""scaled_linear"""     ,    clip_sample=UpperCamelCase     ,    set_alpha_to_one=UpperCamelCase     ,    )
         torch.manual_seed(0							)
         A__				      =	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__				      =	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=10_00     ,    )
         A__				      =	CLIPTextModel(UpperCamelCase							)
         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        a    (							_lowerCamelCase,						_lowerCamelCase,						unittest.TestCase					):



    """simple docstring"""



    UpperCAmelCase    			=       StableDiffusionControlNetInpaintPipeline
    UpperCAmelCase    			=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
    UpperCAmelCase    			=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS

    def 					UpperCamelCase						(  self:			Optional[int]							):






         """simple docstring"""


         torch.manual_seed(0							)
         A__				      =	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(UpperCamelCase:			List[str]							):
              if isinstance(UpperCamelCase     ,    torch.nn.Convad							):
                   torch.nn.init.normal(m.weight							)
                   m.bias.data.fill_(1.0							)

         A__				      =	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(UpperCamelCase							)

         torch.manual_seed(0							)
         A__				      =	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(UpperCamelCase							)

         torch.manual_seed(0							)
         A__				      =	DDIMScheduler(
             beta_start=0.00_085     ,    beta_end=0.012     ,    beta_schedule="""scaled_linear"""     ,    clip_sample=UpperCamelCase     ,    set_alpha_to_one=UpperCamelCase     ,    )
         torch.manual_seed(0							)
         A__				      =	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__				      =	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=10_00     ,    )
         A__				      =	CLIPTextModel(UpperCamelCase							)
         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 					UpperCamelCase						(  self:			List[Any]     ,    UpperCamelCase:			Tuple     ,    UpperCamelCase:			Tuple=0							):






         """simple docstring"""


         if str(UpperCamelCase							).startswith("""mps"""							):
              A__				      =	torch.manual_seed(UpperCamelCase							)
         else:
              A__				      =	torch.Generator(device=UpperCamelCase							).manual_seed(UpperCamelCase							)

         A__				      =	2

         A__				      =	[
             randn_tensor(
                 (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)     ,    generator=UpperCamelCase     ,    device=torch.device(UpperCamelCase							)     ,    ),
             randn_tensor(
                 (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)     ,    generator=UpperCamelCase     ,    device=torch.device(UpperCamelCase							)     ,    ),
         ]
         A__				      =	floats_tensor((1, 3, 32, 32)     ,    rng=random.Random(UpperCamelCase							)							).to(UpperCamelCase							)
         A__				      =	init_image.cpu().permute(0     ,    2     ,    3     ,    1							)[0]

         A__				      =	Image.fromarray(np.uinta(UpperCamelCase							)							).convert("""RGB"""							).resize((64, 64)							)
         A__				      =	Image.fromarray(np.uinta(init_image + 4							)							).convert("""RGB"""							).resize((64, 64)							)

         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 					UpperCamelCase						(  self:			Any							):






         """simple docstring"""


         A__				      =	self.get_dummy_components()
         A__				      =	self.pipeline_class(**UpperCamelCase							)
         pipe.to(UpperCamelCase							)

         A__				      =	10.0
         A__				      =	4

         A__				      =	self.get_dummy_inputs(UpperCamelCase							)
         A__				      =	steps
         A__				      =	scale
         A__				      =	pipe(**UpperCamelCase							)[0]

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

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

         A__				      =	self.get_dummy_inputs(UpperCamelCase							)
         A__				      =	steps
         A__				      =	scale
         A__				      =	pipe(**UpperCamelCase     ,    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:			Union[str, 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 					UpperCamelCase						(  self:			Tuple							):






         """simple docstring"""


         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3							)

    def 					UpperCamelCase						(  self:			Any							):






         """simple docstring"""


         self._test_inference_batch_single_identical(expected_max_diff=2e-3							)





    def 					UpperCamelCase						(  self:			List[str]							):






         """simple docstring"""


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



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



    """simple docstring"""



    def 					UpperCamelCase						(  self:			Optional[Any]							):






         """simple docstring"""


         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()

    def 					UpperCamelCase						(  self:			List[Any]							):






         """simple docstring"""


         A__				      =	ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""							)

         A__				      =	StableDiffusionControlNetInpaintPipeline.from_pretrained(
             """runwayml/stable-diffusion-inpainting"""     ,    safety_checker=UpperCamelCase     ,    controlnet=UpperCamelCase							)
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=UpperCamelCase							)

         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_12, 5_12)							)

         A__				      =	load_image(
             """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
             """/stable_diffusion_inpaint/input_bench_mask.png"""							).resize((5_12, 5_12)							)

         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_12, 5_12)							)

         A__				      =	pipe(
             UpperCamelCase     ,    image=UpperCamelCase     ,    mask_image=UpperCamelCase     ,    control_image=UpperCamelCase     ,    generator=UpperCamelCase     ,    output_type="""np"""     ,    num_inference_steps=3     ,    )

         A__				      =	output.images[0]

         assert image.shape == (5_12, 5_12, 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 					UpperCamelCase						(  self:			Union[str, Any]							):






         """simple docstring"""


         A__				      =	ControlNetModel.from_pretrained("""lllyasviel/control_v11p_sd15_inpaint"""							)

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

         A__				      =	torch.Generator(device="""cpu"""							).manual_seed(33							)

         A__				      =	load_image(
             """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"""							)
         A__				      =	init_image.resize((5_12, 5_12)							)

         A__				      =	load_image(
             """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"""							)
         A__				      =	mask_image.resize((5_12, 5_12)							)

         A__				      =	"""a handsome man with ray-ban sunglasses"""

         def make_inpaint_condition(UpperCamelCase:			int     ,    UpperCamelCase:			Optional[int]							):
              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(UpperCamelCase     ,    0							).transpose(0     ,    3     ,    1     ,    2							)
              A__				      =	torch.from_numpy(UpperCamelCase							)
              return image

         A__				      =	make_inpaint_condition(UpperCamelCase     ,    UpperCamelCase							)

         A__				      =	pipe(
             UpperCamelCase     ,    image=UpperCamelCase     ,    mask_image=UpperCamelCase     ,    control_image=UpperCamelCase     ,    guidance_scale=9.0     ,    eta=1.0     ,    generator=UpperCamelCase     ,    num_inference_steps=20     ,    output_type="""np"""     ,    )
         A__				      =	output.images[0]

         assert image.shape == (5_12, 5_12, 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









"""simple docstring"""



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


SCREAMING_SNAKE_CASE_       :						Union[str, Any]					=    logging.get_logger(__name__)

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



class        a    (							_lowerCamelCase					):



    """simple docstring"""



    UpperCAmelCase    			=       "swiftformer"

    def __init__(  self:			Tuple     ,    UpperCamelCase:			int=3     ,    UpperCamelCase:			Union[str, Any]=[3, 3, 6, 4]     ,    UpperCamelCase:			List[str]=[48, 56, 1_12, 2_20]     ,    UpperCamelCase:			List[Any]=4     ,    UpperCamelCase:			List[Any]=[True, True, True, True]     ,    UpperCamelCase:			str="gelu"     ,    UpperCamelCase:			Tuple=3     ,    UpperCamelCase:			Dict=2     ,    UpperCamelCase:			int=1     ,    UpperCamelCase:			List[Any]=0.0     ,    UpperCamelCase:			Any=True     ,    UpperCamelCase:			List[Any]=1e-5     ,    UpperCamelCase:			List[Any]=1e-5     ,    **UpperCamelCase:			Any     ,    ):






         """simple docstring"""


         super().__init__(**UpperCamelCase							)
         A__				      =	num_channels
         A__				      =	depths
         A__				      =	embed_dims
         A__				      =	mlp_ratio
         A__				      =	downsamples
         A__				      =	hidden_act
         A__				      =	down_patch_size
         A__				      =	down_stride
         A__				      =	down_pad
         A__				      =	drop_path_rate
         A__				      =	use_layer_scale
         A__				      =	layer_scale_init_value
         A__				      =	batch_norm_eps



class        a    (							_lowerCamelCase					):



    """simple docstring"""



    UpperCAmelCase    			=       version.parse("1.11"					)

    @property
    def 					UpperCamelCase						(  self:			int							):






         """simple docstring"""


         return OrderedDict(
             [
                 ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
             ]							)





    @property
    def 					UpperCamelCase						(  self:			Any							):






         """simple docstring"""


         return 1e-4






