






from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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,
)


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

__UpperCAmelCase							:   Optional[Any]							=  "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"A red cartoon frog, 4k\"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to(\"cuda\")\n\n        >>> init_image = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/frog.png\"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save(\"red_frog.png\")\n        ```\n"





def 					lowercase_ (    __snake_case     :							Union[str, Any]	,	__snake_case     :							Union[str, Any]	,	__snake_case     :							Tuple=8      )     -> List[str]:





				'''simple docstring'''


				snake_case__      :int         =   height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				snake_case__      :Any         =   width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor





def 					lowercase_ (    __snake_case     :							Any	,	__snake_case     :							Dict=5_12	,	__snake_case     :							str=5_12      )     -> Tuple:





				'''simple docstring'''


				snake_case__      :List[str]         =   pil_image.resize((w, h)	,	resample=Image.BICUBIC	,	reducing_gap=1      )
				snake_case__      :Any         =   np.array(pil_image.convert("RGB"      )      )
				snake_case__      :int         =   arr.astype(np.floataa      ) / 1_2_7.5 - 1
				snake_case__      :List[Any]         =   np.transpose(UpperCAmelCase__	,	[2, 0, 1]      )
				snake_case__      :Any         =   torch.from_numpy(UpperCAmelCase__      ).unsqueeze(0      )
				return image


class 					_snake_case						(			_a ):






	def __init__(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,)						->    Tuple:
					super().__init__()

					self.register_modules(
					    unet=_A					,scheduler=_A					,movq=_A					,)
					snake_case__      :Any         =   2 ** (len(self.movq.config.block_out_channels       ) - 1)






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )						->    List[str]:
					# get the original timestep using init_timestep
					snake_case__      :Optional[int]         =   min(int(num_inference_steps * strength       )					,_A       )

					snake_case__      :Tuple         =   max(num_inference_steps - init_timestep					,0       )
					snake_case__      :Dict         =   self.scheduler.timesteps[t_start:]

					return timesteps, num_inference_steps - t_start






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase=None       )						->    List[str]:
					if not isinstance(_A					,(torch.Tensor, PIL.Image.Image, list)       ):
									raise ValueError(
									    f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_A       )}'       )

					snake_case__      :Any         =   image.to(device=_A					,dtype=_A       )

					snake_case__      :Dict         =   batch_size * num_images_per_prompt

					if image.shape[1] == 4:
									snake_case__      :str         =   image

					else:
									if isinstance(_A					,_A       ) and len(_A       ) != batch_size:
													raise ValueError(
													    f'You have passed a list of generators of length {len(_A       )}, but requested an effective batch'
													    f' size of {batch_size}. Make sure the batch size matches the length of the generators.'       )

									elif isinstance(_A					,_A       ):
													snake_case__      :int         =   [
													    self.movq.encode(image[i : i + 1]       ).latent_dist.sample(generator[i]       ) for i in range(_A       )
													]
													snake_case__      :int         =   torch.cat(_A					,dim=0       )
									else:
													snake_case__      :Tuple         =   self.movq.encode(_A       ).latent_dist.sample(_A       )

									snake_case__      :int         =   self.movq.config.scaling_factor * init_latents

					snake_case__      :List[str]         =   torch.cat([init_latents]					,dim=0       )

					snake_case__      :str         =   init_latents.shape
					snake_case__      :Any         =   randn_tensor(_A					,generator=_A					,device=_A					,dtype=_A       )

					# get latents
					snake_case__      :str         =   self.scheduler.add_noise(_A					,_A					,_A       )

					snake_case__      :Any         =   init_latents

					return latents






	def    lowerCAmelCase_							(		self					,UpperCamelCase=0       )						->    str:
					if is_accelerate_available():
									from accelerate import cpu_offload
					else:
									raise ImportError("Please install accelerate via `pip install accelerate`"       )

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

					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    lowerCAmelCase_							(		self					,UpperCamelCase=0       )						->    Union[str, Any]:
					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."       )

					snake_case__      :Optional[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)

					snake_case__      :Dict         =   None
					for cpu_offloaded_model in [self.unet, self.movq]:
									snake_case__    ,						snake_case__      :Tuple         =   cpu_offload_with_hook(_A					,_A					,prev_module_hook=_A       )

					# We'll offload the last model manually.
					snake_case__      :str         =   hook






	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def    lowerCAmelCase_							(		self       )						->    int:
					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					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = 512					,UpperCamelCase = 512					,UpperCamelCase = 100					,UpperCamelCase = 4.0					,UpperCamelCase = 0.3					,UpperCamelCase = 1					,UpperCamelCase = None					,UpperCamelCase = "pil"					,UpperCamelCase = True					,)						->    Any:
					snake_case__      :Dict         =   self._execution_device

					snake_case__      :Any         =   guidance_scale > 1.0

					if isinstance(_A					,_A       ):
									snake_case__      :List[str]         =   torch.cat(_A					,dim=0       )
					snake_case__      :Dict         =   image_embeds.shape[0]
					if isinstance(_A					,_A       ):
									snake_case__      :List[str]         =   torch.cat(_A					,dim=0       )

					if do_classifier_free_guidance:
									snake_case__      :Optional[Any]         =   image_embeds.repeat_interleave(_A					,dim=0       )
									snake_case__      :List[Any]         =   negative_image_embeds.repeat_interleave(_A					,dim=0       )

					snake_case__      :Union[str, Any]         =   torch.cat([negative_image_embeds, image_embeds]					,dim=0       ).to(dtype=self.unet.dtype					,device=_A       )

					if not isinstance(_A					,_A       ):
									snake_case__      :Tuple         =   [image]
					if not all(isinstance(_A					,(PIL.Image.Image, torch.Tensor)       ) for i in image       ):
									raise ValueError(
									    f'Input is in incorrect format: {[type(_A       ) for i in image]}. Currently, we only support  PIL image and pytorch tensor'       )

					snake_case__      :str         =   torch.cat([prepare_image(_A					,_A					,_A       ) for i in image]					,dim=0       )
					snake_case__      :str         =   image.to(dtype=image_embeds.dtype					,device=_A       )

					snake_case__      :Optional[int]         =   self.movq.encode(_A       )["latents"]
					snake_case__      :int         =   latents.repeat_interleave(_A					,dim=0       )
					self.scheduler.set_timesteps(_A					,device=_A       )
					snake_case__    ,						snake_case__      :Optional[Any]         =   self.get_timesteps(_A					,_A					,_A       )
					snake_case__      :Union[str, Any]         =   timesteps[:1].repeat(batch_size * num_images_per_prompt       )
					snake_case__    ,						snake_case__      :List[str]         =   downscale_height_and_width(_A					,_A					,self.movq_scale_factor       )
					snake_case__      :Any         =   self.prepare_latents(
					    _A					,_A					,_A					,_A					,image_embeds.dtype					,_A					,_A       )
					for i, t in enumerate(self.progress_bar(_A       )       ):
									# expand the latents if we are doing classifier free guidance
									snake_case__      :Dict         =   torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

									snake_case__      :Any         =   {"image_embeds": image_embeds}
									snake_case__      :List[str]         =   self.unet(
									    sample=_A					,timestep=_A					,encoder_hidden_states=_A					,added_cond_kwargs=_A					,return_dict=_A					,)[0]

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

									# compute the previous noisy sample x_t -> x_t-1
									snake_case__      :Tuple         =   self.scheduler.step(
									    _A					,_A					,_A					,generator=_A					,)[0]

					# post-processing
					snake_case__      :List[str]         =   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"]:
									snake_case__      :str         =   image * 0.5 + 0.5
									snake_case__      :Union[str, Any]         =   image.clamp(0					,1       )
									snake_case__      :Optional[Any]         =   image.cpu().permute(0					,2					,3					,1       ).float().numpy()

					if output_type == "pil":
									snake_case__      :Optional[Any]         =   self.numpy_to_pil(_A       )

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=_A       )







from __future__ import annotations


class 					_snake_case						:






	def __init__(		self					,UpperCamelCase       )						->    None:
					snake_case__      :Union[str, Any]         =   data
					snake_case__      :Node | None         =   None
					snake_case__      :Node | None         =   None





def 					lowercase_ (    __snake_case     :							Node | None      )     -> None:  # In Order traversal of the tree





				'''simple docstring'''


				if tree:
								display(tree.left      )
								print(tree.data      )
								display(tree.right      )





def 					lowercase_ (    __snake_case     :							Node | None      )     -> int:





				'''simple docstring'''


				return 1 + max(depth_of_tree(tree.left      )	,	depth_of_tree(tree.right      )      ) if tree else 0





def 					lowercase_ (    __snake_case     :							Node      )     -> bool:





				'''simple docstring'''


				if not tree:
								return True
				if tree.left and tree.right:
								return is_full_binary_tree(tree.left      ) and is_full_binary_tree(tree.right      )
				else:
								return not tree.left and not tree.right





def 					lowercase_ (    )     -> None:  # Main function for testing.





				'''simple docstring'''


				snake_case__      :Dict         =   Node(1      )
				snake_case__      :int         =   Node(2      )
				snake_case__      :Optional[Any]         =   Node(3      )
				snake_case__      :Tuple         =   Node(4      )
				snake_case__      :str         =   Node(5      )
				snake_case__      :Optional[Any]         =   Node(6      )
				snake_case__      :List[Any]         =   Node(7      )
				snake_case__      :List[str]         =   Node(8      )
				snake_case__      :Tuple         =   Node(9      )

				print(is_full_binary_tree(__snake_case      )      )
				print(depth_of_tree(__snake_case      )      )
				print("Tree is: "      )
				display(__snake_case      )


if __name__ == "__main__":
	main()