

"""simple docstring"""


from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


__A						       =      logging.get_logger(__name__)  # pylint: disable=invalid-name

__A						       =      "\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE=8			)     ->     Optional[int]:
		__lowerCAmelCase:	Optional[Any]   				=      height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		__lowerCAmelCase:	Union[str, Any]   				=      width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor





class    snake_case     (    _lowercase  ):

				def __init__(     self							:    Dict					,  UpperCamelCase__							:    UNetaDConditionModel					,  UpperCamelCase__							:    DDPMScheduler					,  UpperCamelCase__							:    VQModel					,  )->       Union[str, Any]:



						'''simple docstring'''


						super().__init__()

						self.register_modules(
						    unet=__UpperCamelCase					,  scheduler=__UpperCamelCase					,  movq=__UpperCamelCase					,  )
						__lowerCAmelCase:	List[Any]   				=      2 ** (len(self.movq.config.block_out_channels) - 1)

				def 							lowercase_					(     self							:    Optional[Any]					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    Dict					,  UpperCamelCase__							:    Optional[int]					,  UpperCamelCase__							:    List[Any])->       Optional[int]:



						'''simple docstring'''


						if latents is None:
								__lowerCAmelCase:	Dict   				=      randn_tensor(__UpperCamelCase					,  generator=__UpperCamelCase					,  device=__UpperCamelCase					,  dtype=__UpperCamelCase)
						else:
								if latents.shape != shape:
										raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
								__lowerCAmelCase:	List[str]   				=      latents.to(__UpperCamelCase)

						__lowerCAmelCase:	int   				=      latents * scheduler.init_noise_sigma
						return latents

				def 							lowercase_					(     self							:    List[Any]					,  UpperCamelCase__							:    Any=0)->       List[Any]:



						'''simple docstring'''


						if is_accelerate_available():
								from accelerate import cpu_offload
						else:
								raise ImportError("Please install accelerate via `pip install accelerate`")

						__lowerCAmelCase:	List[Any]   				=      torch.device(f"cuda:{gpu_id}")

						__lowerCAmelCase:	int   				=      [
						    self.unet,
						    self.movq,
						]
						for cpu_offloaded_model in models:
								if cpu_offloaded_model is not None:
										cpu_offload(__UpperCamelCase					,  __UpperCamelCase)

				def 							lowercase_					(     self							:    Dict					,  UpperCamelCase__							:    Optional[Any]=0)->       Optional[int]:



						'''simple docstring'''


						if is_accelerate_available() and is_accelerate_version(">="					,  "0.17.0.dev0"):
								from accelerate import cpu_offload_with_hook
						else:
								raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.")

						__lowerCAmelCase:	Union[str, Any]   				=      torch.device(f"cuda:{gpu_id}")

						if self.device.type != "cpu":
								self.to("cpu"					,  silence_dtype_warnings=__UpperCamelCase)
								torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

						__lowerCAmelCase:	str   				=      None
						for cpu_offloaded_model in [self.unet, self.movq]:
								__lowerCAmelCase				,			__lowerCAmelCase:	str   				=      cpu_offload_with_hook(__UpperCamelCase					,  __UpperCamelCase					,  prev_module_hook=__UpperCamelCase)

						# We'll offload the last model manually.
						__lowerCAmelCase:	Optional[int]   				=      hook

				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def 							lowercase_					(     self							:    Any)->       Tuple:



						'''simple docstring'''


						if not hasattr(self.unet					,  "_hf_hook"):
								return self.device
						for module in self.unet.modules():
								if (
								    hasattr(__UpperCamelCase					,  "_hf_hook")
								    and hasattr(module._hf_hook					,  "execution_device")
								    and module._hf_hook.execution_device is not None
								):
										return torch.device(module._hf_hook.execution_device)
						return self.device




				@torch.no_grad()
				@replace_example_docstring(__UpperCamelCase)
				def __call__(     self							:    Tuple					,  UpperCamelCase__							:    Union[torch.FloatTensor, List[torch.FloatTensor]]					,  UpperCamelCase__							:    Union[torch.FloatTensor, List[torch.FloatTensor]]					,  UpperCamelCase__							:    torch.FloatTensor					,  UpperCamelCase__							:    int = 5_1_2					,  UpperCamelCase__							:    int = 5_1_2					,  UpperCamelCase__							:    int = 1_0_0					,  UpperCamelCase__							:    float = 4.0					,  UpperCamelCase__							:    int = 1					,  UpperCamelCase__							:    Optional[Union[torch.Generator, List[torch.Generator]]] = None					,  UpperCamelCase__							:    Optional[torch.FloatTensor] = None					,  UpperCamelCase__							:    Optional[str] = "pil"					,  UpperCamelCase__							:    bool = True					,  )->       Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      self._execution_device

						__lowerCAmelCase:	str   				=      guidance_scale > 1.0

						if isinstance(__UpperCamelCase					,  __UpperCamelCase):
								__lowerCAmelCase:	List[Any]   				=      torch.cat(__UpperCamelCase					,  dim=0)
						if isinstance(__UpperCamelCase					,  __UpperCamelCase):
								__lowerCAmelCase:	Any   				=      torch.cat(__UpperCamelCase					,  dim=0)
						if isinstance(__UpperCamelCase					,  __UpperCamelCase):
								__lowerCAmelCase:	Optional[Any]   				=      torch.cat(__UpperCamelCase					,  dim=0)

						__lowerCAmelCase:	Dict   				=      image_embeds.shape[0] * num_images_per_prompt

						if do_classifier_free_guidance:
								__lowerCAmelCase:	str   				=      image_embeds.repeat_interleave(__UpperCamelCase					,  dim=0)
								__lowerCAmelCase:	Dict   				=      negative_image_embeds.repeat_interleave(__UpperCamelCase					,  dim=0)
								__lowerCAmelCase:	Optional[Any]   				=      hint.repeat_interleave(__UpperCamelCase					,  dim=0)

						__lowerCAmelCase:	Optional[Any]   				=      torch.cat([negative_image_embeds, image_embeds]					,  dim=0).to(dtype=self.unet.dtype					,  device=__UpperCamelCase)
						__lowerCAmelCase:	Union[str, Any]   				=      torch.cat([hint, hint]					,  dim=0).to(dtype=self.unet.dtype					,  device=__UpperCamelCase)
						self.scheduler.set_timesteps(__UpperCamelCase					,  device=__UpperCamelCase)
						__lowerCAmelCase:	List[str]   				=      self.scheduler.timesteps

						__lowerCAmelCase:	Tuple   				=      self.movq.config.latent_channels

						__lowerCAmelCase				,			__lowerCAmelCase:	List[Any]   				=      downscale_height_and_width(__UpperCamelCase					,  __UpperCamelCase					,  self.movq_scale_factor)

						# create initial latent
						__lowerCAmelCase:	Dict   				=      self.prepare_latents(
						    (batch_size, num_channels_latents, height, width)					,  image_embeds.dtype					,  __UpperCamelCase					,  __UpperCamelCase					,  __UpperCamelCase					,  self.scheduler					,  )

						for i, t in enumerate(self.progress_bar(__UpperCamelCase)):
								# expand the latents if we are doing classifier free guidance
								__lowerCAmelCase:	List[Any]   				=      torch.cat([latents] * 2) if do_classifier_free_guidance else latents

								__lowerCAmelCase:	int   				=      {"image_embeds": image_embeds, "hint": hint}
								__lowerCAmelCase:	List[str]   				=      self.unet(
								    sample=__UpperCamelCase					,  timestep=__UpperCamelCase					,  encoder_hidden_states=__UpperCamelCase					,  added_cond_kwargs=__UpperCamelCase					,  return_dict=__UpperCamelCase					,  )[0]

								if do_classifier_free_guidance:
										__lowerCAmelCase				,			__lowerCAmelCase:	Optional[int]   				=      noise_pred.split(latents.shape[1]					,  dim=1)
										__lowerCAmelCase				,			__lowerCAmelCase:	List[str]   				=      noise_pred.chunk(2)
										__lowerCAmelCase				,			__lowerCAmelCase:	Any   				=      variance_pred.chunk(2)
										__lowerCAmelCase:	Optional[int]   				=      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
										__lowerCAmelCase:	Optional[int]   				=      torch.cat([noise_pred, variance_pred_text]					,  dim=1)

								if not (
								    hasattr(self.scheduler.config					,  "variance_type")
								    and self.scheduler.config.variance_type in ["learned", "learned_range"]
								):
										__lowerCAmelCase				,			__lowerCAmelCase:	str   				=      noise_pred.split(latents.shape[1]					,  dim=1)

								# compute the previous noisy sample x_t -> x_t-1
								__lowerCAmelCase:	Any   				=      self.scheduler.step(
								    __UpperCamelCase					,  __UpperCamelCase					,  __UpperCamelCase					,  generator=__UpperCamelCase					,  )[0]
						# post-processing
						__lowerCAmelCase:	Union[str, Any]   				=      self.movq.decode(__UpperCamelCase					,  force_not_quantize=__UpperCamelCase)["sample"]

						if output_type not in ["pt", "np", "pil"]:
								raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}")

						if output_type in ["np", "pil"]:
								__lowerCAmelCase:	Union[str, Any]   				=      image * 0.5 + 0.5
								__lowerCAmelCase:	Optional[Any]   				=      image.clamp(0					,  1)
								__lowerCAmelCase:	Dict   				=      image.cpu().permute(0					,  2					,  3					,  1).float().numpy()

						if output_type == "pil":
								__lowerCAmelCase:	List[Any]   				=      self.numpy_to_pil(__UpperCamelCase)

						if not return_dict:
								return (image,)

						return ImagePipelineOutput(images=__UpperCamelCase)





"""simple docstring"""


from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     np.ndarray:
		__lowerCAmelCase:	List[Any]   				=      cva.getAffineTransform(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)
		return cva.warpAffine(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			(rows, cols)			)


if __name__ == "__main__":
			# read original image
			__A						       =      cva.imread(
			    str(Path(__file__).resolve().parent.parent / "image_data" / "lena.jpg")
			)
			# turn image in gray scale value
			__A						       =      cva.cvtColor(image, cva.COLOR_BGR2GRAY)
			# get image shape
			__A       ,    __A						       =      gray_img.shape

			# set different points to rotate image
			__A						       =      np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
			__A						       =      np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
			__A						       =      np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
			__A						       =      np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

			# add all rotated images in a list
			__A						       =      [
			    gray_img,
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			]

			# plot different image rotations
			__A						       =      plt.figure(1)
			__A						       =      ["Original", "Rotation 1", "Rotation 2", "Rotation 3"]
			for i, image in enumerate(images):
						plt.subplot(2, 2, i + 1), plt.imshow(image, "gray")
						plt.title(titles[i])
						plt.axis("off")
						plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
			plt.show()


