
from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch




@dataclass
class        _SCREAMING_SNAKE_CASE	:
	lowerCAmelCase__   =					42  # [batch_size x 3]
	lowerCAmelCase__   =					42  # [batch_size x 3]
	lowerCAmelCase__   =					42  # [batch_size x 3]
	lowerCAmelCase__   =					42  # [batch_size x 3]
	lowerCAmelCase__   =					42
	lowerCAmelCase__   =					42
	lowerCAmelCase__   =					42
	lowerCAmelCase__   =					42
	lowerCAmelCase__   =					42



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[int]:
					assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
					assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
					assert len(self.x.shape					) == len(self.y.shape					) == len(self.z.shape					) == len(self.origin.shape					) == 2



	def        SCREAMING_SNAKE_CASE_(       self					)							->			str:
					return torch.from_numpy(np.array([self.width, self.height]		,    dtype=np.floataa					)					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					return torch.from_numpy(np.array([self.x_fov, self.y_fov]		,    dtype=np.floataa					)					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			torch.Tensor:
					lowerCamelCase_         =     torch.arange(self.height * self.width					)
					lowerCamelCase_         =     torch.stack(
					    [
					        pixel_indices % self.width,
					        torch.div(lowercase		,    self.width		,    rounding_mode="trunc"					),
					    ]		,    axis=1		,    )
					return coords



	@property
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Dict:
					lowerCamelCase_      ,       *lowerCamelCase_         =     self.shape
					lowerCamelCase_         =     int(np.prod(lowercase					)					)

					lowerCamelCase_         =     self.get_image_coords()
					lowerCamelCase_         =     torch.broadcast_to(coords.unsqueeze(0					)		,    [batch_size * inner_batch_size, *coords.shape]					)
					lowerCamelCase_         =     self.get_camera_rays(lowercase					)

					lowerCamelCase_         =     rays.view(lowercase		,    inner_batch_size * self.height * self.width		,    2		,    3					)

					return rays



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase					)							->			torch.Tensor:
					lowerCamelCase_      ,       *lowerCamelCase_      ,       lowerCamelCase_         =     coords.shape
					assert n_coords == 2
					assert batch_size == self.origin.shape[0]

					lowerCamelCase_         =     coords.view(lowercase		,    -1		,    2					)

					lowerCamelCase_         =     self.resolution()
					lowerCamelCase_         =     self.fov()

					lowerCamelCase_         =     (flat.float() / (res - 1)) * 2 - 1
					lowerCamelCase_         =     fracs * torch.tan(fov / 2					)

					lowerCamelCase_         =     fracs.view(lowercase		,    -1		,    2					)
					lowerCamelCase_         =     (
					    self.z.view(lowercase		,    1		,    3					)
					    + self.x.view(lowercase		,    1		,    3					) * fracs[:, :, :1]
					    + self.y.view(lowercase		,    1		,    3					) * fracs[:, :, 1:]
					)
					lowerCamelCase_         =     directions / directions.norm(dim=-1		,    keepdim=lowercase					)
					lowerCamelCase_         =     torch.stack(
					    [
					        torch.broadcast_to(self.origin.view(lowercase		,    1		,    3					)		,    [batch_size, directions.shape[1], 3]					),
					        directions,
					    ]		,    dim=2		,    )
					return rays.view(lowercase		,    *lowercase		,    2		,    3					)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase					)							->			"DifferentiableProjectiveCamera":
					assert width * self.height == height * self.width, "The aspect ratio should not change."
					return DifferentiableProjectiveCamera(
					    origin=self.origin		,    x=self.x		,    y=self.y		,    z=self.z		,    width=lowercase		,    height=lowercase		,    x_fov=self.x_fov		,    y_fov=self.y_fov		,    )





def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     []
				lowerCamelCase_         =     []
				lowerCamelCase_         =     []
				lowerCamelCase_         =     []
				for theta in np.linspace(0      ,					2 * np.pi      ,					num=2_0       ):
								lowerCamelCase_         =     np.array([np.sin(lowerCamelCase__       ), np.cos(lowerCamelCase__       ), -0.5]       )
								z /= np.sqrt(np.sum(z**2       )       )
								lowerCamelCase_         =     -z * 4
								lowerCamelCase_         =     np.array([np.cos(lowerCamelCase__       ), -np.sin(lowerCamelCase__       ), 0.0]       )
								lowerCamelCase_         =     np.cross(lowerCamelCase__      ,					lowerCamelCase__       )
								origins.append(lowerCamelCase__       )
								xs.append(lowerCamelCase__       )
								ys.append(lowerCamelCase__       )
								zs.append(lowerCamelCase__       )
				return DifferentiableProjectiveCamera(
				    origin=torch.from_numpy(np.stack(lowerCamelCase__      ,					axis=0       )       ).float()      ,					x=torch.from_numpy(np.stack(lowerCamelCase__      ,					axis=0       )       ).float()      ,					y=torch.from_numpy(np.stack(lowerCamelCase__      ,					axis=0       )       ).float()      ,					z=torch.from_numpy(np.stack(lowerCamelCase__      ,					axis=0       )       ).float()      ,					width=lowerCamelCase__      ,					height=lowerCamelCase__      ,					x_fov=0.7      ,					y_fov=0.7      ,					shape=(1, len(lowerCamelCase__       ))      ,					)




# 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        _SCREAMING_SNAKE_CASE	( snake_case_     ,			snake_case_     ,			snake_case_     ,			unittest.TestCase		):
	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        SCREAMING_SNAKE_CASE_(       self					)							->			int:
					torch.manual_seed(0					)
					lowerCamelCase_         =     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_         =     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_         =     DDIMScheduler(
					    beta_start=0.0_0_0_8_5		,    beta_end=0.0_1_2		,    beta_schedule="scaled_linear"		,    clip_sample=lowercase		,    set_alpha_to_one=lowercase		,    )
					torch.manual_seed(0					)
					lowerCamelCase_         =     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_         =     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		,    )
					lowerCamelCase_         =     CLIPTextModel(lowercase					)
					lowerCamelCase_         =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)

					lowerCamelCase_         =     {
					    "unet": unet,
					    "controlnet": controlnet,
					    "scheduler": scheduler,
					    "vae": vae,
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "safety_checker": None,
					    "feature_extractor": None,
					}
					return components



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase=0					)							->			int:
					if str(lowercase					).startswith("mps"					):
									lowerCamelCase_         =     torch.manual_seed(lowercase					)
					else:
									lowerCamelCase_         =     torch.Generator(device=lowercase					).manual_seed(lowercase					)

					lowerCamelCase_         =     2
					lowerCamelCase_         =     randn_tensor(
					    (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)		,    generator=lowercase		,    device=torch.device(lowercase					)		,    )
					lowerCamelCase_         =     floats_tensor(control_image.shape		,    rng=random.Random(lowercase					)					).to(lowercase					)
					lowerCamelCase_         =     image.cpu().permute(0		,    2		,    3		,    1					)[0]
					lowerCamelCase_         =     Image.fromarray(np.uinta(lowercase					)					).convert("RGB"					).resize((64, 64)					)
					lowerCamelCase_         =     {
					    "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        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					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        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Any:
					self._test_inference_batch_single_identical(expected_max_diff=2e-3					)




class        _SCREAMING_SNAKE_CASE	( snake_case_     ,			snake_case_     ,			unittest.TestCase		):
	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        SCREAMING_SNAKE_CASE_(       self					)							->			Dict:
					torch.manual_seed(0					)
					lowerCamelCase_         =     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					)

					lowerCamelCase_         =     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					)
					lowerCamelCase_         =     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					)
					lowerCamelCase_         =     DDIMScheduler(
					    beta_start=0.0_0_0_8_5		,    beta_end=0.0_1_2		,    beta_schedule="scaled_linear"		,    clip_sample=lowercase		,    set_alpha_to_one=lowercase		,    )
					torch.manual_seed(0					)
					lowerCamelCase_         =     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_         =     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		,    )
					lowerCamelCase_         =     CLIPTextModel(lowercase					)
					lowerCamelCase_         =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)

					lowerCamelCase_         =     MultiControlNetModel([controlneta, controlneta]					)

					lowerCamelCase_         =     {
					    "unet": unet,
					    "controlnet": controlnet,
					    "scheduler": scheduler,
					    "vae": vae,
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "safety_checker": None,
					    "feature_extractor": None,
					}
					return components



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase=0					)							->			List[Any]:
					if str(lowercase					).startswith("mps"					):
									lowerCamelCase_         =     torch.manual_seed(lowercase					)
					else:
									lowerCamelCase_         =     torch.Generator(device=lowercase					).manual_seed(lowercase					)

					lowerCamelCase_         =     2

					lowerCamelCase_         =     [
					    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					)		,    ),
					]

					lowerCamelCase_         =     floats_tensor(control_image[0].shape		,    rng=random.Random(lowercase					)					).to(lowercase					)
					lowerCamelCase_         =     image.cpu().permute(0		,    2		,    3		,    1					)[0]
					lowerCamelCase_         =     Image.fromarray(np.uinta(lowercase					)					).convert("RGB"					).resize((64, 64)					)
					lowerCamelCase_         =     {
					    "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        SCREAMING_SNAKE_CASE_(       self					)							->			List[Any]:
					lowerCamelCase_         =     self.get_dummy_components()
					lowerCamelCase_         =     self.pipeline_class(**lowercase					)
					pipe.to(lowercase					)

					lowerCamelCase_         =     1_0.0
					lowerCamelCase_         =     4

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

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

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

					lowerCamelCase_         =     self.get_dummy_inputs(lowercase					)
					lowerCamelCase_         =     steps
					lowerCamelCase_         =     scale
					lowerCamelCase_         =     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        SCREAMING_SNAKE_CASE_(       self					)							->			Dict:
					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        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[int]:
					self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Union[str, Any]:
					self._test_inference_batch_single_identical(expected_max_diff=2e-3					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			int:
					lowerCamelCase_         =     self.get_dummy_components()
					lowerCamelCase_         =     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        _SCREAMING_SNAKE_CASE	( unittest.TestCase		):



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[Any]:
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[int]:
					lowerCamelCase_         =     ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny"					)

					lowerCamelCase_         =     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					)

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

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

					lowerCamelCase_         =     output.images[0]

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

					lowerCamelCase_         =     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


