



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





	"""simple docstring"""
	__A							=							StableDiffusionControlNetImgaImgPipeline
	__A							=							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
	__A							=							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	__A							=							IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"control_image"}	)
	__A							=							IMAGE_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=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




	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(control_image.shape		,       rng=random.Random(lowerCamelCase_		)		).to(lowerCamelCase_		)
						a					     =   image.cpu().permute(0		,       2		,       3		,       1		)[0]
						a					     =   Image.fromarray(np.uinta(lowerCamelCase_		)		).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		):
						"""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							(   lowerCAmelCase,      lowerCAmelCase,      unittest.TestCase	):





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




	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		)

						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(control_image[0].shape		,       rng=random.Random(lowerCamelCase_		)		).to(lowerCamelCase_		)
						a					     =   image.cpu().permute(0		,       2		,       3		,       1		)[0]
						a					     =   Image.fromarray(np.uinta(lowerCamelCase_		)		).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		):
						"""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					     =   StableDiffusionControlNetImgaImgPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"		,       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					     =   "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(
						    lowerCamelCase_		,       lowerCamelCase_		,       control_image=lowerCamelCase_		,       generator=lowerCamelCase_		,       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





import cmath
import math






def     a(      A       :    float ,     A       :    float ,     A       :    float ,     A       :    float       )		->       complex:
					"""simple docstring"""





					a					     =   math.radians(A       )
					a					     =   math.radians(A       )

					# Convert voltage and current to rectangular form
					a					     =   cmath.rect(A ,     A       )
					a					     =   cmath.rect(A ,     A       )

					# Calculate apparent power
					return voltage_rect * current_rect


if __name__ == "__main__":
				import doctest

				doctest.testmod()
