
"""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      _SCREAMING_SNAKE_CASE   (     A__      ,					A__      ,					A__      ,					unittest.TestCase				):
			UpperCAmelCase_    :List[Any]												=		StableDiffusionControlNetInpaintPipeline
			UpperCAmelCase_    :Optional[int]												=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			UpperCAmelCase_    :int												=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			UpperCAmelCase_    :Dict												=		frozenset({"control_image"}				)  # skip `image` and `mask` for now, only test for control_image
			UpperCAmelCase_    :Dict												=		TEXT_TO_IMAGE_IMAGE_PARAMS






			def 					__lowerCAmelCase					(   self			)     ->	List[str]:
						torch.manual_seed(0			)
						lowerCAmelCase_       :Dict     	=    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			)
						lowerCAmelCase_       :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			)
						lowerCAmelCase_       :List[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			)
						lowerCAmelCase_       :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			)
						lowerCAmelCase_       :int     	=    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=1000			,  )
						lowerCAmelCase_       :Optional[int]     	=    CLIPTextModel(__A			)
						lowerCAmelCase_       :Union[str, Any]     	=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

						lowerCAmelCase_       :Optional[int]     	=    {
						    """unet""": unet,
						    """controlnet""": controlnet,
						    """scheduler""": scheduler,
						    """vae""": vae,
						    """text_encoder""": text_encoder,
						    """tokenizer""": tokenizer,
						    """safety_checker""": None,
						    """feature_extractor""": None,
						}
						return components






			def 					__lowerCAmelCase					(   self			,  __A			,  __A=0			)     ->	Union[str, Any]:
						if str(__A			).startswith("""mps"""			):
									lowerCAmelCase_       :Optional[int]     	=    torch.manual_seed(__A			)
						else:
									lowerCAmelCase_       :str     	=    torch.Generator(device=__A			).manual_seed(__A			)

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

						lowerCAmelCase_       :int     	=    Image.fromarray(np.uinta(__A			)			).convert("""RGB"""			).resize((64, 64)			)
						lowerCAmelCase_       :int     	=    Image.fromarray(np.uinta(init_image + 4			)			).convert("""RGB"""			).resize((64, 64)			)

						lowerCAmelCase_       :List[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 					__lowerCAmelCase					(   self			)     ->	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 					__lowerCAmelCase					(   self			)     ->	str:
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3			)






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




class      _SCREAMING_SNAKE_CASE   (     A__				):
			UpperCAmelCase_    :Any												=		StableDiffusionControlNetInpaintPipeline
			UpperCAmelCase_    :int												=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			UpperCAmelCase_    :List[Any]												=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			UpperCAmelCase_    :str												=		frozenset([]				)






			def 					__lowerCAmelCase					(   self			)     ->	Any:
						torch.manual_seed(0			)
						lowerCAmelCase_       :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			)
						lowerCAmelCase_       :List[str]     	=    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			)
						lowerCAmelCase_       :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			)
						lowerCAmelCase_       :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			)
						lowerCAmelCase_       :Dict     	=    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=1000			,  )
						lowerCAmelCase_       :List[Any]     	=    CLIPTextModel(__A			)
						lowerCAmelCase_       :Optional[Any]     	=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

						lowerCAmelCase_       :str     	=    {
						    """unet""": unet,
						    """controlnet""": controlnet,
						    """scheduler""": scheduler,
						    """vae""": vae,
						    """text_encoder""": text_encoder,
						    """tokenizer""": tokenizer,
						    """safety_checker""": None,
						    """feature_extractor""": None,
						}
						return components




class      _SCREAMING_SNAKE_CASE   (     A__      ,					A__      ,					unittest.TestCase				):
			UpperCAmelCase_    :Optional[Any]												=		StableDiffusionControlNetInpaintPipeline
			UpperCAmelCase_    :List[str]												=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			UpperCAmelCase_    :Union[str, Any]												=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS






			def 					__lowerCAmelCase					(   self			)     ->	Optional[Any]:
						torch.manual_seed(0			)
						lowerCAmelCase_       :Optional[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(__A			):
									if isinstance(__A			,  torch.nn.Convad			):
												torch.nn.init.normal(m.weight			)
												m.bias.data.fill_(1.0			)

						lowerCAmelCase_       :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)			,  )
						controlneta.controlnet_down_blocks.apply(__A			)

						torch.manual_seed(0			)
						lowerCAmelCase_       :Optional[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			)
						lowerCAmelCase_       :Optional[int]     	=    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			)
						lowerCAmelCase_       :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			)
						lowerCAmelCase_       :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=1000			,  )
						lowerCAmelCase_       :Tuple     	=    CLIPTextModel(__A			)
						lowerCAmelCase_       :Union[str, Any]     	=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)

						lowerCAmelCase_       :Optional[Any]     	=    MultiControlNetModel([controlneta, controlneta]			)

						lowerCAmelCase_       :str     	=    {
						    """unet""": unet,
						    """controlnet""": controlnet,
						    """scheduler""": scheduler,
						    """vae""": vae,
						    """text_encoder""": text_encoder,
						    """tokenizer""": tokenizer,
						    """safety_checker""": None,
						    """feature_extractor""": None,
						}
						return components






			def 					__lowerCAmelCase					(   self			,  __A			,  __A=0			)     ->	int:
						if str(__A			).startswith("""mps"""			):
									lowerCAmelCase_       :Any     	=    torch.manual_seed(__A			)
						else:
									lowerCAmelCase_       :Tuple     	=    torch.Generator(device=__A			).manual_seed(__A			)

						lowerCAmelCase_       :Dict     	=    2

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

						lowerCAmelCase_       :Optional[Any]     	=    Image.fromarray(np.uinta(__A			)			).convert("""RGB"""			).resize((64, 64)			)
						lowerCAmelCase_       :Dict     	=    Image.fromarray(np.uinta(init_image + 4			)			).convert("""RGB"""			).resize((64, 64)			)

						lowerCAmelCase_       :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 					__lowerCAmelCase					(   self			)     ->	Union[str, Any]:
						lowerCAmelCase_       :str     	=    self.get_dummy_components()
						lowerCAmelCase_       :Tuple     	=    self.pipeline_class(**__A			)
						pipe.to(__A			)

						lowerCAmelCase_       :List[str]     	=    1_0.0
						lowerCAmelCase_       :Union[str, Any]     	=    4

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

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

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

						lowerCAmelCase_       :Dict     	=    self.get_dummy_inputs(__A			)
						lowerCAmelCase_       :str     	=    steps
						lowerCAmelCase_       :str     	=    scale
						lowerCAmelCase_       :List[str]     	=    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 					__lowerCAmelCase					(   self			)     ->	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 					__lowerCAmelCase					(   self			)     ->	Tuple:
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3			)






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






			def 					__lowerCAmelCase					(   self			)     ->	List[Any]:
						lowerCAmelCase_       :List[Any]     	=    self.get_dummy_components()
						lowerCAmelCase_       :int     	=    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      _SCREAMING_SNAKE_CASE   (     unittest.TestCase				):






			def 					__lowerCAmelCase					(   self			)     ->	int:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()






			def 					__lowerCAmelCase					(   self			)     ->	int:
						lowerCAmelCase_       :Any     	=    ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""			)

						lowerCAmelCase_       :int     	=    StableDiffusionControlNetInpaintPipeline.from_pretrained(
						    """runwayml/stable-diffusion-inpainting"""			,  safety_checker=__A			,  controlnet=__A			)
						pipe.enable_model_cpu_offload()
						pipe.set_progress_bar_config(disable=__A			)

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

						lowerCAmelCase_       :List[str]     	=    load_image(
						    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
						    """/stable_diffusion_inpaint/input_bench_mask.png"""			).resize((512, 512)			)

						lowerCAmelCase_       :int     	=    """pitch black hole"""

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

						lowerCAmelCase_       :Tuple     	=    pipe(
						    __A			,  image=__A			,  mask_image=__A			,  control_image=__A			,  generator=__A			,  output_type="""np"""			,  num_inference_steps=3			,  )

						lowerCAmelCase_       :int     	=    output.images[0]

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

						lowerCAmelCase_       :List[Any]     	=    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 					__lowerCAmelCase					(   self			)     ->	Optional[int]:
						lowerCAmelCase_       :Optional[int]     	=    ControlNetModel.from_pretrained("""lllyasviel/control_v11p_sd15_inpaint"""			)

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

						lowerCAmelCase_       :List[Any]     	=    torch.Generator(device="""cpu"""			).manual_seed(33			)

						lowerCAmelCase_       :Tuple     	=    load_image(
						    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"""			)
						lowerCAmelCase_       :Optional[int]     	=    init_image.resize((512, 512)			)

						lowerCAmelCase_       :Dict     	=    load_image(
						    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"""			)
						lowerCAmelCase_       :List[str]     	=    mask_image.resize((512, 512)			)

						lowerCAmelCase_       :Any     	=    """a handsome man with ray-ban sunglasses"""

						def make_inpaint_condition(__A			,  __A			):
									lowerCAmelCase_       :List[Any]     	=    np.array(image.convert("""RGB"""			)			).astype(np.floataa			) / 2_5_5.0
									lowerCAmelCase_       :Optional[Any]     	=    np.array(image_mask.convert("""L"""			)			).astype(np.floataa			) / 2_5_5.0

									assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
									lowerCAmelCase_       :int     	=    -1.0  # set as masked pixel
									lowerCAmelCase_       :Union[str, Any]     	=    np.expand_dims(__A			,  0			).transpose(0			,  3			,  1			,  2			)
									lowerCAmelCase_       :Dict     	=    torch.from_numpy(__A			)
									return image

						lowerCAmelCase_       :Optional[Any]     	=    make_inpaint_condition(__A			,  __A			)

						lowerCAmelCase_       :Optional[int]     	=    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"""			,  )
						lowerCAmelCase_       :Dict     	=    output.images[0]

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

						lowerCAmelCase_       :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"""






from __future__ import annotations






def 		_snake_case			(      lowercase__						:    list[int | float]						)    ->							int | float:


			'''simple docstring'''





			if len(lowercase__						) == 0:
						raise ValueError("""find_min() arg is an empty sequence"""						)
			lowerCAmelCase_       :Optional[int]     	=    nums[0]
			for num in nums:
						lowerCAmelCase_       :Optional[Any]     	=    min(lowercase__ ,	lowercase__						)
			return min_num


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)






