
'''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  UpperCamelCase__  (    SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     unittest.TestCase):
 UpperCAmelCase__    :    Optional[int]										=					StableDiffusionControlNetInpaintPipeline
 UpperCAmelCase__    :    Optional[int]										=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 UpperCAmelCase__    :    List[str]										=					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 	lowercase_						(			self  :str			)				->			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			)
       __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=_A	,				set_alpha_to_one=_A	,				)
       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-05	,				num_attention_heads=4	,				num_hidden_layers=5	,				pad_token_id=1	,				vocab_size=1_000	,				)
       __A       	=			CLIPTextModel(_A			)
       __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 	lowercase_						(			self  :Any	,				_A  :Dict	,				_A  :int=0			)				->			List[str]:







       '''simple docstring'''



       if str(_A			).startswith('mps'			):
             __A       	=			torch.manual_seed(_A			)
       else:
             __A       	=			torch.Generator(device=_A			).manual_seed(_A			)

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

       __A       	=			Image.fromarray(np.uinta(_A			)			).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 	lowercase_						(			self  :List[Any]			)				->			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 	lowercase_						(			self  :Tuple			)				->			int:







       '''simple docstring'''



       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3			)






 def 	lowercase_						(			self  :List[str]			)				->			Optional[int]:







       '''simple docstring'''



       self._test_inference_batch_single_identical(expected_max_diff=2E-3			)


class  UpperCamelCase__  (    SCREAMING_SNAKE_CASE):
 UpperCAmelCase__    :    Dict										=					StableDiffusionControlNetInpaintPipeline
 UpperCAmelCase__    :    List[Any]										=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 UpperCAmelCase__    :    str										=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 UpperCAmelCase__    :    List[Any]										=					frozenset([])






 def 	lowercase_						(			self  :List[str]			)				->			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=_A	,				set_alpha_to_one=_A	,				)
       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-05	,				num_attention_heads=4	,				num_hidden_layers=5	,				pad_token_id=1	,				vocab_size=1_000	,				)
       __A       	=			CLIPTextModel(_A			)
       __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  UpperCamelCase__  (    SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     unittest.TestCase):
 UpperCAmelCase__    :    str										=					StableDiffusionControlNetInpaintPipeline
 UpperCAmelCase__    :    List[str]										=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 UpperCAmelCase__    :    Any										=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS






 def 	lowercase_						(			self  :str			)				->			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(_A  :List[Any]			):
             if isinstance(_A	,				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(_A			)

       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(_A			)

       torch.manual_seed(0			)
       __A       	=			DDIMScheduler(
           beta_start=0.00_085	,				beta_end=0.012	,				beta_schedule='scaled_linear'	,				clip_sample=_A	,				set_alpha_to_one=_A	,				)
       torch.manual_seed(0			)
       __A       	=			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-05	,				num_attention_heads=4	,				num_hidden_layers=5	,				pad_token_id=1	,				vocab_size=1_000	,				)
       __A       	=			CLIPTextModel(_A			)
       __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 	lowercase_						(			self  :Tuple	,				_A  :Tuple	,				_A  :Optional[int]=0			)				->			Optional[Any]:







       '''simple docstring'''



       if str(_A			).startswith('mps'			):
             __A       	=			torch.manual_seed(_A			)
       else:
             __A       	=			torch.Generator(device=_A			).manual_seed(_A			)

       __A       	=			2

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

       __A       	=			Image.fromarray(np.uinta(_A			)			).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 	lowercase_						(			self  :Tuple			)				->			Union[str, Any]:







       '''simple docstring'''



       __A       	=			self.get_dummy_components()
       __A       	=			self.pipeline_class(**_A			)
       pipe.to(_A			)

       __A       	=			10.0
       __A       	=			4

       __A       	=			self.get_dummy_inputs(_A			)
       __A       	=			steps
       __A       	=			scale
       __A       	=			pipe(**_A			)[0]

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

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

       __A       	=			self.get_dummy_inputs(_A			)
       __A       	=			steps
       __A       	=			scale
       __A       	=			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 	lowercase_						(			self  :Dict			)				->			int:







       '''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 	lowercase_						(			self  :List[Any]			)				->			str:







       '''simple docstring'''



       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3			)






 def 	lowercase_						(			self  :List[Any]			)				->			str:







       '''simple docstring'''



       self._test_inference_batch_single_identical(expected_max_diff=2E-3			)






 def 	lowercase_						(			self  :Union[str, Any]			)				->			Union[str, Any]:







       '''simple docstring'''



       __A       	=			self.get_dummy_components()
       __A       	=			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  UpperCamelCase__  (    unittest.TestCase):






 def 	lowercase_						(			self  :Optional[int]			)				->			List[str]:







       '''simple docstring'''



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






 def 	lowercase_						(			self  :Any			)				->			int:







       '''simple docstring'''



       __A       	=			ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'			)

       __A       	=			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       	=			torch.Generator(device='cpu'			).manual_seed(0			)
       __A       	=			load_image(
           'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'			).resize((512, 512)			)

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

       __A       	=			'pitch black hole'

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

       __A       	=			pipe(
           _A	,				image=_A	,				mask_image=_A	,				control_image=_A	,				generator=_A	,				output_type='np'	,				num_inference_steps=3	,				)

       __A       	=			output.images[0]

       assert image.shape == (512, 512, 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 	lowercase_						(			self  :Tuple			)				->			str:







       '''simple docstring'''



       __A       	=			ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'			)

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

       __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((512, 512)			)

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

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

       def make_inpaint_condition(_A  :Optional[Any]	,				_A  :Tuple			):
             __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(_A	,				0			).transpose(0	,				3	,				1	,				2			)
             __A       	=			torch.from_numpy(_A			)
             return image

       __A       	=			make_inpaint_condition(_A	,				_A			)

       __A       	=			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       	=			output.images[0]

       assert image.shape == (512, 512, 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'''





def        snake_case							(		UpperCAmelCase	)->			int:







      """simple docstring"""
      return 1 if input_a == 0 else 0





def        snake_case							(		)->			None:







      """simple docstring"""
      assert not_gate(0	) == 1
      assert not_gate(1	) == 0


if __name__ == "__main__":
 print(not_gate(0))
 print(not_gate(1))






