

# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

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,
    StableDiffusionControlNetImgaImgPipeline,
    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 (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()

class     a__  (    snake_case							,		snake_case							,		snake_case							,		unittest.TestCase	):
			"""simple docstring"""







			__lowerCamelCase       				=				StableDiffusionControlNetImgaImgPipeline
			__lowerCamelCase       				=				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
			__lowerCamelCase       				=				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			__lowerCamelCase       				=				IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'}	)
			__lowerCamelCase       				=				IMAGE_TO_IMAGE_IMAGE_PARAMS


			def    UpperCamelCase						(			self				)     ->  Dict:
							'''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=lowercase					,					set_alpha_to_one=lowercase					,					)
							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=1000					,					)
							A__						=	CLIPTextModel(lowercase				)
							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					,					lowercase					,					lowercase=0				)     ->  Any:
							'''simple docstring'''






							if str(lowercase				).startswith("mps"				):
											A__						=	torch.manual_seed(lowercase				)
							else:
											A__						=	torch.Generator(device=lowercase				).manual_seed(lowercase				)

							A__						=	2
							A__						=	randn_tensor(
							    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					,					generator=lowercase					,					device=torch.device(lowercase				)					,					)
							A__						=	floats_tensor(control_image.shape					,					rng=random.Random(lowercase				)				).to(lowercase				)
							A__						=	image.cpu().permute(0					,					2					,					3					,					1				)[0]
							A__						=	Image.fromarray(np.uinta(lowercase				)				).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,
							    "control_image": control_image,
							}

							return inputs


			def    UpperCamelCase						(			self				)     ->  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    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''simple docstring'''






							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3				)


			def    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






							self._test_inference_batch_single_identical(expected_max_diff=2e-3				)

class     a__  (    snake_case							,		snake_case							,		unittest.TestCase	):
			"""simple docstring"""







			__lowerCamelCase       				=				StableDiffusionControlNetImgaImgPipeline
			__lowerCamelCase       				=				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
			__lowerCamelCase       				=				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			__lowerCamelCase       				=				frozenset([]	)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess


			def    UpperCamelCase						(			self				)     ->  List[Any]:
							'''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				)

							def init_weights(lowercase				):
											if isinstance(lowercase					,					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(lowercase				)

							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(lowercase				)

							torch.manual_seed(0				)
							A__						=	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				)
							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=1000					,					)
							A__						=	CLIPTextModel(lowercase				)
							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					,					lowercase					,					lowercase=0				)     ->  List[str]:
							'''simple docstring'''






							if str(lowercase				).startswith("mps"				):
											A__						=	torch.manual_seed(lowercase				)
							else:
											A__						=	torch.Generator(device=lowercase				).manual_seed(lowercase				)

							A__						=	2

							A__						=	[
							    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				)					,					),
							]

							A__						=	floats_tensor(control_image[0].shape					,					rng=random.Random(lowercase				)				).to(lowercase				)
							A__						=	image.cpu().permute(0					,					2					,					3					,					1				)[0]
							A__						=	Image.fromarray(np.uinta(lowercase				)				).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,
							    "control_image": control_image,
							}

							return inputs


			def    UpperCamelCase						(			self				)     ->  Dict:
							'''simple docstring'''






							A__						=	self.get_dummy_components()
							A__						=	self.pipeline_class(**lowercase				)
							pipe.to(lowercase				)

							A__						=	10.0
							A__						=	4

							A__						=	self.get_dummy_inputs(lowercase				)
							A__						=	steps
							A__						=	scale
							A__						=	pipe(**lowercase				)[0]

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

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

							A__						=	self.get_dummy_inputs(lowercase				)
							A__						=	steps
							A__						=	scale
							A__						=	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    UpperCamelCase						(			self				)     ->  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    UpperCamelCase						(			self				)     ->  Dict:
							'''simple docstring'''






							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3				)


			def    UpperCamelCase						(			self				)     ->  str:
							'''simple docstring'''






							self._test_inference_batch_single_identical(expected_max_diff=2e-3				)


			def    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






							A__						=	self.get_dummy_components()
							A__						=	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     a__  (    unittest.TestCase	):
			"""simple docstring"""







			def    UpperCamelCase						(			self				)     ->  str:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






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

							A__						=	StableDiffusionControlNetImgaImgPipeline.from_pretrained(
							    "runwayml/stable-diffusion-v1-5"					,					safety_checker=lowercase					,					controlnet=lowercase				)
							pipe.enable_model_cpu_offload()
							pipe.set_progress_bar_config(disable=lowercase				)

							A__						=	torch.Generator(device="cpu"				).manual_seed(0				)
							A__						=	"evil space-punk bird"
							A__						=	load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"				).resize((512, 512)				)
							A__						=	load_image(
							    "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"				).resize((512, 512)				)

							A__						=	pipe(
							    lowercase					,					lowercase					,					control_image=lowercase					,					generator=lowercase					,					output_type="np"					,					num_inference_steps=50					,					strength=0.6					,					)

							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/img2img.npy"				)

							assert np.abs(expected_image - image				).max() < 9e-2







def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       float	,							lowercase	:       list[float]     ):


      '''simple docstring'''



      if discount_rate < 0:
            raise ValueError('Discount rate cannot be negative'     )
      if not cash_flows:
            raise ValueError('Cash flows list cannot be empty'     )
      lowerCamelCase_		     =    sum(
          cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(lowercase     )     )
      return round(lowercase	,							ndigits=2     )


if __name__ == "__main__":
 import doctest

 doctest.testmod()



