



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,
)


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

_lowerCAmelCase	:   Union[str, Any]    =   "\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     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__=8					)	->     Any:



				"""simple docstring"""





				lowerCAmelCase__		  =  height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				lowerCAmelCase__		  =  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   ( SCREAMING_SNAKE_CASE ):
		def __init__(							self    ,a_    ,a_    ,a_    ,):







						"""simple docstring"""
						super().__init__()

						self.register_modules(
						    unet=a_    ,scheduler=a_    ,movq=a_    ,)
						lowerCAmelCase__		  =  2 ** (len(self.movq.config.block_out_channels						) - 1)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_    ,a_    ,a_    ,a_    ,a_    ,a_						):







						"""simple docstring"""
						if latents is None:
										lowerCAmelCase__		  =  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}'						)
										lowerCAmelCase__		  =  latents.to(a_						)

						lowerCAmelCase__		  =  latents * scheduler.init_noise_sigma
						return latents
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_=0						):







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

						lowerCAmelCase__		  =  torch.device(f'cuda:{gpu_id}'						)

						lowerCAmelCase__		  =  [
						    self.unet,
						    self.movq,
						]
						for cpu_offloaded_model in models:
										if cpu_offloaded_model is not None:
														cpu_offload(a_    ,a_						)
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_=0						):







						"""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__		  =  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)

						lowerCAmelCase__		  =  None
						for cpu_offloaded_model in [self.unet, self.movq]:
										lowerCAmelCase__  ,   lowerCAmelCase__		  =  cpu_offload_with_hook(a_    ,a_    ,prev_module_hook=a_						)

						# We'll offload the last model manually.
						lowerCAmelCase__		  =  hook
		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""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    ,a_    ,a_    ,a_    ,a_ = 512    ,a_ = 512    ,a_ = 100    ,a_ = 4.0    ,a_ = 1    ,a_ = None    ,a_ = None    ,a_ = "pil"    ,a_ = True    ,):







						"""simple docstring"""
						lowerCAmelCase__		  =  self._execution_device

						lowerCAmelCase__		  =  guidance_scale > 1.0

						if isinstance(a_    ,a_						):
										lowerCAmelCase__		  =  torch.cat(a_    ,dim=0						)
						if isinstance(a_    ,a_						):
										lowerCAmelCase__		  =  torch.cat(a_    ,dim=0						)
						if isinstance(a_    ,a_						):
										lowerCAmelCase__		  =  torch.cat(a_    ,dim=0						)

						lowerCAmelCase__		  =  image_embeds.shape[0] * num_images_per_prompt

						if do_classifier_free_guidance:
										lowerCAmelCase__		  =  image_embeds.repeat_interleave(a_    ,dim=0						)
										lowerCAmelCase__		  =  negative_image_embeds.repeat_interleave(a_    ,dim=0						)
										lowerCAmelCase__		  =  hint.repeat_interleave(a_    ,dim=0						)

						lowerCAmelCase__		  =  torch.cat([negative_image_embeds, image_embeds]    ,dim=0						).to(dtype=self.unet.dtype    ,device=a_						)
						lowerCAmelCase__		  =  torch.cat([hint, hint]    ,dim=0						).to(dtype=self.unet.dtype    ,device=a_						)
						self.scheduler.set_timesteps(a_    ,device=a_						)
						lowerCAmelCase__		  =  self.scheduler.timesteps

						lowerCAmelCase__		  =  self.movq.config.latent_channels

						lowerCAmelCase__  ,   lowerCAmelCase__		  =  downscale_height_and_width(a_    ,a_    ,self.movq_scale_factor						)

						# create initial latent
						lowerCAmelCase__		  =  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
										lowerCAmelCase__		  =  torch.cat([latents] * 2						) if do_classifier_free_guidance else latents

										lowerCAmelCase__		  =  {'image_embeds': image_embeds, 'hint': hint}
										lowerCAmelCase__		  =  self.unet(
										    sample=a_    ,timestep=a_    ,encoder_hidden_states=a_    ,added_cond_kwargs=a_    ,return_dict=a_    ,)[0]

										if do_classifier_free_guidance:
														lowerCAmelCase__  ,   lowerCAmelCase__		  =  noise_pred.split(latents.shape[1]    ,dim=1						)
														lowerCAmelCase__  ,   lowerCAmelCase__		  =  noise_pred.chunk(2						)
														lowerCAmelCase__  ,   lowerCAmelCase__		  =  variance_pred.chunk(2						)
														lowerCAmelCase__		  =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
														lowerCAmelCase__		  =  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__		  =  noise_pred.split(latents.shape[1]    ,dim=1						)

										# compute the previous noisy sample x_t -> x_t-1
										lowerCAmelCase__		  =  self.scheduler.step(
										    a_    ,a_    ,a_    ,generator=a_    ,)[0]
						# post-processing
						lowerCAmelCase__		  =  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"]:
										lowerCAmelCase__		  =  image * 0.5 + 0.5
										lowerCAmelCase__		  =  image.clamp(0    ,1						)
										lowerCAmelCase__		  =  image.cpu().permute(0    ,2    ,3    ,1						).float().numpy()

						if output_type == "pil":
										lowerCAmelCase__		  =  self.numpy_to_pil(a_						)

						if not return_dict:
										return (image,)

						return ImagePipelineOutput(images=a_						)





from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

_lowerCAmelCase	:   List[str]    =   TypeVar("T")


class 	__snake_case   ( Generic[T] ):
		def __init__(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  data
						lowerCAmelCase__		  =  None



		def __str__(							self						):







						"""simple docstring"""
						return f'{self.data}'



class 	__snake_case   ( Generic[T] ):
		def __init__(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  None
		def __iter__(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  self.top
						while node:
										yield node.data
										lowerCAmelCase__		  =  node.next
		def __str__(							self						):







						"""simple docstring"""
						return "->".join([str(a_						) for item in self]						)
		def __len__(							self						):







						"""simple docstring"""
						return len(tuple(iter(self						)						)						)
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						return self.top is None
		def 						SCREAMING_SNAKE_CASE_					(							self    ,a_						):







						"""simple docstring"""
						lowerCAmelCase__		  =  Node(a_						)
						if not self.is_empty():
										lowerCAmelCase__		  =  self.top
						lowerCAmelCase__		  =  node
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						if self.is_empty():
										raise IndexError('pop from empty stack'						)
						assert isinstance(self.top    ,a_						)
						lowerCAmelCase__		  =  self.top
						lowerCAmelCase__		  =  self.top.next
						return pop_node.data
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						if self.is_empty():
										raise IndexError('peek from empty stack'						)

						assert self.top is not None
						return self.top.data



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  None


if __name__ == "__main__":
	from doctest import testmod

	testmod()
