



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





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




	def 	UpperCamelCase_			(self						):
						"""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=0.0_0085						,			beta_end=0.012						,			beta_schedule="scaled_linear"						,			clip_sample=__lowerCAmelCase						,			set_alpha_to_one=__lowerCAmelCase						,			)
						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=1000						,			)
						a   	=  CLIPTextModel(__lowerCAmelCase						)
						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						,			lowerCamelCase_						,			lowerCamelCase_=0						):
						"""simple docstring"""

						if str(__lowerCAmelCase						).startswith("mps"						):
											a   	=  torch.manual_seed(__lowerCAmelCase						)
						else:
											a   	=  torch.Generator(device=__lowerCAmelCase						).manual_seed(__lowerCAmelCase						)

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

						a   	=  Image.fromarray(np.uinta(__lowerCAmelCase						)						).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						):
						"""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						):
						"""simple docstring"""

						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						self._test_inference_batch_single_identical(expected_max_diff=2E-3						)




class       _lowercase				(  UpperCAmelCase__      ):





	"""simple docstring"""
	__A		      =				StableDiffusionControlNetInpaintPipeline
	__A		      =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	__A		      =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	__A		      =				frozenset([]      )




	def 	UpperCamelCase_			(self						):
						"""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=0.0_0085						,			beta_end=0.012						,			beta_schedule="scaled_linear"						,			clip_sample=__lowerCAmelCase						,			set_alpha_to_one=__lowerCAmelCase						,			)
						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=1000						,			)
						a   	=  CLIPTextModel(__lowerCAmelCase						)
						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       _lowercase				(  UpperCAmelCase__, UpperCAmelCase__, unittest.TestCase      ):





	"""simple docstring"""
	__A		      =				StableDiffusionControlNetInpaintPipeline
	__A		      =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
	__A		      =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS




	def 	UpperCamelCase_			(self						):
						"""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(lowerCamelCase_						):
											if isinstance(__lowerCAmelCase						,			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(__lowerCAmelCase						)

						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(__lowerCAmelCase						)

						torch.manual_seed(0						)
						a   	=  DDIMScheduler(
						    beta_start=0.0_0085						,			beta_end=0.012						,			beta_schedule="scaled_linear"						,			clip_sample=__lowerCAmelCase						,			set_alpha_to_one=__lowerCAmelCase						,			)
						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=1000						,			)
						a   	=  CLIPTextModel(__lowerCAmelCase						)
						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						,			lowerCamelCase_						,			lowerCamelCase_=0						):
						"""simple docstring"""

						if str(__lowerCAmelCase						).startswith("mps"						):
											a   	=  torch.manual_seed(__lowerCAmelCase						)
						else:
											a   	=  torch.Generator(device=__lowerCAmelCase						).manual_seed(__lowerCAmelCase						)

						a   	=  2

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

						a   	=  Image.fromarray(np.uinta(__lowerCAmelCase						)						).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						):
						"""simple docstring"""

						a   	=  self.get_dummy_components()
						a   	=  self.pipeline_class(**__lowerCAmelCase						)
						pipe.to(__lowerCAmelCase						)

						a   	=  10.0
						a   	=  4

						a   	=  self.get_dummy_inputs(__lowerCAmelCase						)
						a   	=  steps
						a   	=  scale
						a   	=  pipe(**__lowerCAmelCase						)[0]

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

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

						a   	=  self.get_dummy_inputs(__lowerCAmelCase						)
						a   	=  steps
						a   	=  scale
						a   	=  pipe(**__lowerCAmelCase						,			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						):
						"""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						):
						"""simple docstring"""

						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						self._test_inference_batch_single_identical(expected_max_diff=2E-3						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

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




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





	"""simple docstring"""




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

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




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

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

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

						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(
						    __lowerCAmelCase						,			image=__lowerCAmelCase						,			mask_image=__lowerCAmelCase						,			control_image=__lowerCAmelCase						,			generator=__lowerCAmelCase						,			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 	UpperCamelCase_			(self						):
						"""simple docstring"""

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

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

						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(lowerCamelCase_						,			lowerCamelCase_						):
											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(__lowerCAmelCase						,			0						).transpose(0						,			3						,			1						,			2						)
											a   	=  torch.from_numpy(__lowerCAmelCase						)
											return image

						a   	=  make_inpaint_condition(__lowerCAmelCase						,			__lowerCAmelCase						)

						a   	=  pipe(
						    __lowerCAmelCase						,			image=__lowerCAmelCase						,			mask_image=__lowerCAmelCase						,			control_image=__lowerCAmelCase						,			guidance_scale=9.0						,			eta=1.0						,			generator=__lowerCAmelCase						,			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





from __future__ import annotations


def 						UpperCAmelCase__   (   UpperCAmelCase__			:float | int			,       UpperCAmelCase__			:float | int			,       UpperCAmelCase__			:float | int			,       ):






 '''simple docstring'''

 if not all((nth_term, start_term_a, common_ratio_r)       ):
  return []
 a							=		[]
 a							=		1
 a							=		common_ratio_r
 for _ in range(int(UpperCAmelCase__       )       ):
  if not series:
   series.append(UpperCAmelCase__       )
  else:
   power += 1
   series.append(float(start_term_a * multiple       )       )
   a							=		pow(float(UpperCAmelCase__       )			,       UpperCAmelCase__       )
 return series


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     A_		:  Optional[Any]										=  float(input('''Enter the last number (n term) of the Geometric Series'''))
     A_		:  Optional[Any]										=  float(input('''Enter the starting term (a) of the Geometric Series'''))
     A_		:  Optional[int]										=  float(
         input('''Enter the common ratio between two terms (r) of the Geometric Series''')
     )
     print('''Formula of Geometric Series => a + ar + ar^2 ... +ar^n''')
     print(geometric_series(nth_term, start_term_a, common_ratio_r))

