

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  lowerCamelCase__ (					_a							,			_a							,			_a=8):
	SCREAMING_SNAKE_CASE   :      int				     =       height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       width // scale_factor**2
	if width % scale_factor**2 != 0:
		new_width += 1
	return new_height * scale_factor, new_width * scale_factor






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			def __init__(			self							: Any      ,      a							: UNetaDConditionModel      ,      a							: DDPMScheduler      ,      a							: VQModel      ,      )							->       List[Any]:

				"""simple docstring"""

				super().__init__()

				self.register_modules(
				    unet=a      ,      scheduler=a      ,      movq=a      ,      )
				SCREAMING_SNAKE_CASE   :      List[str]				     =       2 ** (len(self.movq.config.block_out_channels	) - 1)
			def 							__UpperCamelCase (			self							: int      ,      a							: Any      ,      a							: Optional[Any]      ,      a							: int      ,      a							: List[str]      ,      a							: List[str]      ,      a							: str	)							->       Optional[int]:

				"""simple docstring"""

				if latents is None:
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       randn_tensor(a      ,      generator=a      ,      device=a      ,      dtype=a	)
				else:
					if latents.shape != shape:
						raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"	)
					SCREAMING_SNAKE_CASE   :      Dict				     =       latents.to(a	)

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       latents * scheduler.init_noise_sigma
				return latents
			def 							__UpperCamelCase (			self							: Optional[Any]      ,      a							: Union[str, 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`"	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       torch.device(F"cuda:{gpu_id}"	)

				SCREAMING_SNAKE_CASE   :      Any				     =       [
				    self.unet,
				    self.movq,
				]
				for cpu_offloaded_model in models:
					if cpu_offloaded_model is not None:
						cpu_offload(a      ,      a	)
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: Tuple=0	)							->       Union[str, Any]:

				"""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."	)

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

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

				SCREAMING_SNAKE_CASE   :      int				     =       None
				for cpu_offloaded_model in [self.unet, self.movq]:
					SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Tuple				     =       cpu_offload_with_hook(a      ,      a      ,      prev_module_hook=a	)

				# We'll offload the last model manually.
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       hook
			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def 							__UpperCamelCase (			self							: Dict	)							->       Union[str, Any]:

				"""simple docstring"""

				if not hasattr(self.unet      ,      "_hf_hook"	):
					return self.device
				for module in self.unet.modules():
					if (
					    hasattr(a      ,      "_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(a	)
			def __call__(			self							: Tuple      ,      a							: Union[torch.FloatTensor, List[torch.FloatTensor]]      ,      a							: Union[torch.FloatTensor, List[torch.FloatTensor]]      ,      a							: torch.FloatTensor      ,      a							: int = 512      ,      a							: int = 512      ,      a							: int = 100      ,      a							: float = 4.0      ,      a							: int = 1      ,      a							: Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,      a							: Optional[torch.FloatTensor] = None      ,      a							: Optional[str] = "pil"      ,      a							: bool = True      ,      )							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       self._execution_device

				SCREAMING_SNAKE_CASE   :      Dict				     =       guidance_scale > 1.0

				if isinstance(a      ,      a	):
					SCREAMING_SNAKE_CASE   :      int				     =       torch.cat(a      ,      dim=0	)
				if isinstance(a      ,      a	):
					SCREAMING_SNAKE_CASE   :      List[str]				     =       torch.cat(a      ,      dim=0	)
				if isinstance(a      ,      a	):
					SCREAMING_SNAKE_CASE   :      List[str]				     =       torch.cat(a      ,      dim=0	)

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

				if do_classifier_free_guidance:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       image_embeds.repeat_interleave(a      ,      dim=0	)
					SCREAMING_SNAKE_CASE   :      str				     =       negative_image_embeds.repeat_interleave(a      ,      dim=0	)
					SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       hint.repeat_interleave(a      ,      dim=0	)

				SCREAMING_SNAKE_CASE   :      str				     =       torch.cat([negative_image_embeds, image_embeds]      ,      dim=0	).to(dtype=self.unet.dtype      ,      device=a	)
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       torch.cat([hint, hint]      ,      dim=0	).to(dtype=self.unet.dtype      ,      device=a	)
				self.scheduler.set_timesteps(a      ,      device=a	)
				SCREAMING_SNAKE_CASE   :      Any				     =       self.scheduler.timesteps

				SCREAMING_SNAKE_CASE   :      int				     =       self.movq.config.latent_channels

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Dict				     =       downscale_height_and_width(a      ,      a      ,      self.movq_scale_factor	)

				# create initial latent
				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.prepare_latents(
				    (batch_size, num_channels_latents, height, width)      ,      image_embeds.dtype      ,      a      ,      a      ,      a      ,      self.scheduler      ,      )

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

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

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

					# compute the previous noisy sample x_t -> x_t-1
					SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.scheduler.step(
					    a      ,      a      ,      a      ,      generator=a      ,      )[0]
				# post-processing
				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.movq.decode(a      ,      force_not_quantize=a	)["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"]:
					SCREAMING_SNAKE_CASE   :      Tuple				     =       image * 0.5 + 0.5
					SCREAMING_SNAKE_CASE   :      List[str]				     =       image.clamp(0      ,      1	)
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       image.cpu().permute(0      ,      2      ,      3      ,      1	).float().numpy()

				if output_type == "pil":
					SCREAMING_SNAKE_CASE   :      str				     =       self.numpy_to_pil(a	)

				if not return_dict:
					return (image,)

				return ImagePipelineOutput(images=a	)


class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Optional[Any]      ,      a							: int	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [0] * size
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [0] * size
			@staticmethod
			def 							__UpperCamelCase (			a							: int	)							->       int:

				"""simple docstring"""

				return index | (index + 1)
			@staticmethod
			def 							__UpperCamelCase (			a							: int	)							->       int:

				"""simple docstring"""

				return (index & (index + 1)) - 1
			def 							__UpperCamelCase (			self							: Any      ,      a							: int      ,      a							: int	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       value
				while index < self.size:
					SCREAMING_SNAKE_CASE   :      Dict				     =       self.get_prev(a	) + 1
					if current_left_border == index:
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       value
					else:
						SCREAMING_SNAKE_CASE   :      Tuple				     =       max(a      ,      a      ,      a	)
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.get_next(a	)






			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: int      ,      a							: int	)							->       int:

				"""simple docstring"""

				right -= 1  # Because of right is exclusive
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       0
				while left <= right:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.get_prev(a	)
					if left <= current_left:
						SCREAMING_SNAKE_CASE   :      List[Any]				     =       max(a      ,      self.tree[right]	)
						SCREAMING_SNAKE_CASE   :      str				     =       current_left
					else:
						SCREAMING_SNAKE_CASE   :      List[str]				     =       max(a      ,      self.arr[right]	)
						right -= 1
				return result


if __name__ == "__main__":
			import doctest

			doctest.testmod()