




# 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 		UpperCAmelCase       (    UpperCAmelCase__	,       UpperCAmelCase__	,       UpperCAmelCase__	,       unittest.TestCase      ):







      '''simple docstring'''





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






      def 	snake_case__						(						self							:					Tuple						):






       """simple docstring"""






       torch.manual_seed(0						)
       snake_case_		   =					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						)
       snake_case_		   =					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						)
       snake_case_		   =					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						)
       snake_case_		   =					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						)
       snake_case_		   =					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=10_00	,     )
       snake_case_		   =					CLIPTextModel(__lowercase						)
       snake_case_		   =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

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






      def 	snake_case__						(						self							:					int	,     __lowercase							:					Optional[int]	,     __lowercase							:					Optional[int]=0						):






       """simple docstring"""






       if str(__lowercase						).startswith("mps"						):
        snake_case_		   =					torch.manual_seed(__lowercase						)
       else:
        snake_case_		   =					torch.Generator(device=__lowercase						).manual_seed(__lowercase						)

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

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

       snake_case_		   =					{
           "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 	snake_case__						(						self							:					List[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 	snake_case__						(						self							:					Optional[Any]						):






       """simple docstring"""






       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3						)






      def 	snake_case__						(						self							:					Union[str, Any]						):






       """simple docstring"""






       self._test_inference_batch_single_identical(expected_max_diff=2E-3						)







class 		UpperCAmelCase       (    UpperCAmelCase__      ):







      '''simple docstring'''





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






      def 	snake_case__						(						self							:					Optional[Any]						):






       """simple docstring"""






       torch.manual_seed(0						)
       snake_case_		   =					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						)
       snake_case_		   =					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						)
       snake_case_		   =					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						)
       snake_case_		   =					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						)
       snake_case_		   =					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=10_00	,     )
       snake_case_		   =					CLIPTextModel(__lowercase						)
       snake_case_		   =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

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







class 		UpperCAmelCase       (    UpperCAmelCase__	,       UpperCAmelCase__	,       unittest.TestCase      ):







      '''simple docstring'''





      lowerCAmelCase_							=       StableDiffusionControlNetInpaintPipeline
      lowerCAmelCase_							=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      lowerCAmelCase_							=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS






      def 	snake_case__						(						self							:					int						):






       """simple docstring"""






       torch.manual_seed(0						)
       snake_case_		   =					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							:					Any						):
        if isinstance(__lowercase	,     torch.nn.Convad						):
         torch.nn.init.normal(m.weight						)
         m.bias.data.fill_(1.0						)

       snake_case_		   =					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						)
       snake_case_		   =					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						)
       snake_case_		   =					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						)
       snake_case_		   =					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						)
       snake_case_		   =					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=10_00	,     )
       snake_case_		   =					CLIPTextModel(__lowercase						)
       snake_case_		   =					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

       snake_case_		   =					MultiControlNetModel([controlneta, controlneta]						)

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






      def 	snake_case__						(						self							:					Optional[Any]	,     __lowercase							:					str	,     __lowercase							:					List[str]=0						):






       """simple docstring"""






       if str(__lowercase						).startswith("mps"						):
        snake_case_		   =					torch.manual_seed(__lowercase						)
       else:
        snake_case_		   =					torch.Generator(device=__lowercase						).manual_seed(__lowercase						)

       snake_case_		   =					2

       snake_case_		   =					[
           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						)	,     ),
       ]
       snake_case_		   =					floats_tensor((1, 3, 32, 32)	,     rng=random.Random(__lowercase						)						).to(__lowercase						)
       snake_case_		   =					init_image.cpu().permute(0	,     2	,     3	,     1						)[0]

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

       snake_case_		   =					{
           "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 	snake_case__						(						self							:					Union[str, Any]						):






       """simple docstring"""






       snake_case_		   =					self.get_dummy_components()
       snake_case_		   =					self.pipeline_class(**__lowercase						)
       pipe.to(__lowercase						)

       snake_case_		   =					10.0
       snake_case_		   =					4

       snake_case_		   =					self.get_dummy_inputs(__lowercase						)
       snake_case_		   =					steps
       snake_case_		   =					scale
       snake_case_		   =					pipe(**__lowercase						)[0]

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

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

       snake_case_		   =					self.get_dummy_inputs(__lowercase						)
       snake_case_		   =					steps
       snake_case_		   =					scale
       snake_case_		   =					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 	snake_case__						(						self							:					Dict						):






       """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 	snake_case__						(						self							:					Optional[Any]						):






       """simple docstring"""






       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3						)






      def 	snake_case__						(						self							:					Optional[Any]						):






       """simple docstring"""






       self._test_inference_batch_single_identical(expected_max_diff=2E-3						)






      def 	snake_case__						(						self							:					Optional[Any]						):






       """simple docstring"""






       snake_case_		   =					self.get_dummy_components()
       snake_case_		   =					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 		UpperCAmelCase       (    unittest.TestCase      ):







      '''simple docstring'''





      def 	snake_case__						(						self							:					List[str]						):






       """simple docstring"""






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






      def 	snake_case__						(						self							:					List[str]						):






       """simple docstring"""






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

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

       snake_case_		   =					torch.Generator(device="cpu"						).manual_seed(0						)
       snake_case_		   =					load_image(
           "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"						).resize((5_12, 5_12)						)

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

       snake_case_		   =					"pitch black hole"

       snake_case_		   =					load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"						).resize((5_12, 5_12)						)

       snake_case_		   =					pipe(
           __lowercase	,     image=__lowercase	,     mask_image=__lowercase	,     control_image=__lowercase	,     generator=__lowercase	,     output_type="np"	,     num_inference_steps=3	,     )

       snake_case_		   =					output.images[0]

       assert image.shape == (5_12, 5_12, 3)

       snake_case_		   =					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 	snake_case__						(						self							:					List[Any]						):






       """simple docstring"""






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

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

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

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

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

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

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

       snake_case_		   =					make_inpaint_condition(__lowercase	,     __lowercase						)

       snake_case_		   =					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"	,     )
       snake_case_		   =					output.images[0]

       assert image.shape == (5_12, 5_12, 3)

       snake_case_		   =					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






def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





	if right <= left:
		return
	snake_case_		   =					snake_case_		   =					left
	snake_case_		   =					right
	snake_case_		   =					sorting[left]
	while i <= b:
		if sorting[i] < pivot:
			snake_case_   ,				snake_case_		   =					sorting[i], sorting[a]
			a += 1
			i += 1
		elif sorting[i] > pivot:
			snake_case_   ,				snake_case_		   =					sorting[i], sorting[b]
			b -= 1
		else:
			i += 1
	quick_sort_apartition(_A					,     _A					,     a - 1							)
	quick_sort_apartition(_A					,     b + 1					,     _A							)


def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





	if left < right:
		snake_case_		   =					lomuto_partition(_A					,     _A					,     _A							)
		quick_sort_lomuto_partition(_A					,     _A					,     pivot_index - 1							)
		quick_sort_lomuto_partition(_A					,     pivot_index + 1					,     _A							)


def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					sorting[right]
	snake_case_		   =					left
	for i in range(_A					,     _A							):
		if sorting[i] < pivot:
			snake_case_   ,				snake_case_		   =					sorting[i], sorting[store_index]
			store_index += 1
	snake_case_   ,				snake_case_		   =					sorting[store_index], sorting[right]
	return store_index


def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





	if len(_A							) <= 1:
		return sorting
	return (
	    three_way_radix_quicksort([i for i in sorting if i < sorting[0]]							)
	    + [i for i in sorting if i == sorting[0]]
	    + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]							)
	)


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)

		lowercase__    :					Tuple					=						input("Enter numbers separated by a comma:\n").strip()
		lowercase__    :					int					=						[int(item) for item in user_input.split(",")]
		quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
		print(unsorted)
