
# 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__				(	lowercase__		,  lowercase__		,  lowercase__		,  unittest.TestCase      ):
				lowercase            =    StableDiffusionControlNetInpaintPipeline
				lowercase            =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
				lowercase            =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				lowercase            =    frozenset({'control_image'}      )  # skip `image` and `mask` for now, only test for control_image
				lowercase            =    TEXT_TO_IMAGE_IMAGE_PARAMS






				def 				_lowerCamelCase						(		self   : Optional[Any]  ):






							'''simple docstring'''







							torch.manual_seed(0  )
							lowerCAmelCase__ : Union[str, 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  )
							lowerCAmelCase__ : 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  )
							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__ : Dict   =						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__ : Optional[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=1_000       ,	)
							lowerCAmelCase__ : List[Any]   =						CLIPTextModel(a  )
							lowerCAmelCase__ : str   =						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

							lowerCAmelCase__ : Tuple   =						{
							    '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   : int       ,	a   : int       ,	a   : Optional[int]=0  ):






							'''simple docstring'''







							if str(a  ).startswith('mps'  ):
										lowerCAmelCase__ : int   =						torch.manual_seed(a  )
							else:
										lowerCAmelCase__ : List[str]   =						torch.Generator(device=a  ).manual_seed(a  )

							lowerCAmelCase__ : str   =						2
							lowerCAmelCase__ : str   =						randn_tensor(
							    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)       ,	generator=a       ,	device=torch.device(a  )       ,	)
							lowerCAmelCase__ : List[Any]   =						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__ : List[Any]   =						Image.fromarray(np.uinta(a  )  ).convert('RGB'  ).resize((64, 64)  )
							lowerCAmelCase__ : List[Any]   =						Image.fromarray(np.uinta(init_image + 4  )  ).convert('RGB'  ).resize((64, 64)  )

							lowerCAmelCase__ : Optional[Any]   =						{
							    '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   : List[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 				_lowerCamelCase						(		self   : Optional[int]  ):






							'''simple docstring'''







							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3  )






				def 				_lowerCamelCase						(		self   : List[str]  ):






							'''simple docstring'''







							self._test_inference_batch_single_identical(expected_max_diff=2E-3  )







class 	A__				(	lowercase__      ):
				lowercase            =    StableDiffusionControlNetInpaintPipeline
				lowercase            =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
				lowercase            =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				lowercase            =    frozenset([]      )






				def 				_lowerCamelCase						(		self   : Any  ):






							'''simple docstring'''







							torch.manual_seed(0  )
							lowerCAmelCase__ : str   =						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__ : 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  )
							lowerCAmelCase__ : Dict   =						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__ : Dict   =						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__ : List[Any]   =						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       ,	)
							lowerCAmelCase__ : List[str]   =						CLIPTextModel(a  )
							lowerCAmelCase__ : str   =						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







class 	A__				(	lowercase__		,  lowercase__		,  unittest.TestCase      ):
				lowercase            =    StableDiffusionControlNetInpaintPipeline
				lowercase            =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
				lowercase            =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS






				def 				_lowerCamelCase						(		self   : List[Any]  ):






							'''simple docstring'''







							torch.manual_seed(0  )
							lowerCAmelCase__ : Union[str, 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   : Any  ):
										if isinstance(a       ,	torch.nn.Convad  ):
													torch.nn.init.normal(m.weight  )
													m.bias.data.fill_(1.0  )

							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)       ,	)
							controlneta.controlnet_down_blocks.apply(a  )

							torch.manual_seed(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]   =						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=1_000       ,	)
							lowerCAmelCase__ : Optional[int]   =						CLIPTextModel(a  )
							lowerCAmelCase__ : Dict   =						CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

							lowerCAmelCase__ : Union[str, Any]   =						MultiControlNetModel([controlneta, controlneta]  )

							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   : Any       ,	a   : Optional[int]       ,	a   : Dict=0  ):






							'''simple docstring'''







							if str(a  ).startswith('mps'  ):
										lowerCAmelCase__ : Tuple   =						torch.manual_seed(a  )
							else:
										lowerCAmelCase__ : str   =						torch.Generator(device=a  ).manual_seed(a  )

							lowerCAmelCase__ : str   =						2

							lowerCAmelCase__ : List[Any]   =						[
							    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__ : Any   =						floats_tensor((1, 3, 32, 32)       ,	rng=random.Random(a  )  ).to(a  )
							lowerCAmelCase__ : List[Any]   =						init_image.cpu().permute(0       ,	2       ,	3       ,	1  )[0]

							lowerCAmelCase__ : str   =						Image.fromarray(np.uinta(a  )  ).convert('RGB'  ).resize((64, 64)  )
							lowerCAmelCase__ : List[Any]   =						Image.fromarray(np.uinta(init_image + 4  )  ).convert('RGB'  ).resize((64, 64)  )

							lowerCAmelCase__ : List[Any]   =						{
							    '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   : Tuple  ):






							'''simple docstring'''







							lowerCAmelCase__ : Union[str, Any]   =						self.get_dummy_components()
							lowerCAmelCase__ : Any   =						self.pipeline_class(**a  )
							pipe.to(a  )

							lowerCAmelCase__ : int   =						10.0
							lowerCAmelCase__ : str   =						4

							lowerCAmelCase__ : Optional[int]   =						self.get_dummy_inputs(a  )
							lowerCAmelCase__ : List[str]   =						steps
							lowerCAmelCase__ : int   =						scale
							lowerCAmelCase__ : int   =						pipe(**a  )[0]

							lowerCAmelCase__ : Any   =						self.get_dummy_inputs(a  )
							lowerCAmelCase__ : str   =						steps
							lowerCAmelCase__ : Tuple   =						scale
							lowerCAmelCase__ : List[Any]   =						pipe(**a       ,	control_guidance_start=0.1       ,	control_guidance_end=0.2  )[0]

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

							lowerCAmelCase__ : Any   =						self.get_dummy_inputs(a  )
							lowerCAmelCase__ : str   =						steps
							lowerCAmelCase__ : int   =						scale
							lowerCAmelCase__ : List[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 				_lowerCamelCase						(		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 				_lowerCamelCase						(		self   : int  ):






							'''simple docstring'''







							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3  )






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






							'''simple docstring'''







							self._test_inference_batch_single_identical(expected_max_diff=2E-3  )






				def 				_lowerCamelCase						(		self   : Optional[int]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Union[str, 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 	A__				(	unittest.TestCase      ):






				def 				_lowerCamelCase						(		self   : Optional[int]  ):






							'''simple docstring'''







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






				def 				_lowerCamelCase						(		self   : Tuple  ):






							'''simple docstring'''







							lowerCAmelCase__ : Dict   =						ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'  )

							lowerCAmelCase__ : str   =						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__ : Optional[int]   =						torch.Generator(device='cpu'  ).manual_seed(0  )
							lowerCAmelCase__ : Tuple   =						load_image(
							    'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'  ).resize((512, 512)  )

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

							lowerCAmelCase__ : Dict   =						'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__ : List[Any]   =						pipe(
							    a       ,	image=a       ,	mask_image=a       ,	control_image=a       ,	generator=a       ,	output_type='np'       ,	num_inference_steps=3       ,	)

							lowerCAmelCase__ : str   =						output.images[0]

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

							lowerCAmelCase__ : Optional[int]   =						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   : str  ):






							'''simple docstring'''







							lowerCAmelCase__ : Dict   =						ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'  )

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

							lowerCAmelCase__ : str   =						torch.Generator(device='cpu'  ).manual_seed(33  )

							lowerCAmelCase__ : Optional[Any]   =						load_image(
							    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'  )
							lowerCAmelCase__ : Dict   =						init_image.resize((512, 512)  )

							lowerCAmelCase__ : List[Any]   =						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__ : Union[str, Any]   =						'a handsome man with ray-ban sunglasses'

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

							lowerCAmelCase__ : List[str]   =						make_inpaint_condition(a       ,	a  )

							lowerCAmelCase__ : List[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'       ,	)
							lowerCAmelCase__ : Optional[int]   =						output.images[0]

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

							lowerCAmelCase__ : Optional[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 __future__ import annotations

import sys
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    if n < 0:
        raise ValueError("""Negative arguments are not supported"""		)
    return _fib(UpperCAmelCase_		)[0]
def 							__lowerCAmelCase			(UpperCAmelCase_		):
    if n == 0:  # (F(0), F(1))
        return (0, 1)

    # F(2n) = F(n)[2F(n+1) − F(n)]
    # F(2n+1) = F(n+1)^2+F(n)^2
    a__			,		a__  			=							_fib(n // 2		)
    a__  			=							a * (b * 2 - a)
    a__  			=							a * a + b * b
    return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_    :       Dict       =							int(sys.argv[1])
 print(F"fibonacci({n}) is {fibonacci(n)}")






