





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,
)


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

__lowerCAmelCase :str     =    '\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    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase=8				):
					_snake_case       :					Union[str, Any]				       =   height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					_snake_case       :					Dict				       =   width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor







def    A				(							UpperCAmelCase  ,  UpperCAmelCase=512  ,  UpperCAmelCase=512				):
					_snake_case       :					int				       =   pil_image.resize((w, h)  ,  resample=Image.BICUBIC  ,  reducing_gap=1				)
					_snake_case       :					List[Any]				       =   np.array(pil_image.convert("RGB"				)				)
					_snake_case       :					Dict				       =   arr.astype(np.floataa				) / 1_2_7.5 - 1
					_snake_case       :					Optional[Any]				       =   np.transpose(UpperCAmelCase  ,  [2, 0, 1]				)
					_snake_case       :					Optional[Any]				       =   torch.from_numpy(UpperCAmelCase				).unsqueeze(0				)
					return image






class        _a(    __A    ):







		def __init__(    self ,						__snake_case ,						__snake_case ,						__snake_case ,						)      ->      str:


							'''simple docstring'''

							super().__init__()

							self.register_modules(
							    unet=__snake_case ,						scheduler=__snake_case ,						movq=__snake_case ,						)
							_snake_case       :					Optional[int]				       =   2 ** (len(self.movq.config.block_out_channels				) - 1)







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case				)      ->      Optional[Any]:


							'''simple docstring'''

							_snake_case       :					Dict				       =   min(int(num_inference_steps * strength				) ,						__snake_case				)

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

							return timesteps, num_inference_steps - t_start







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case=None				)      ->      Optional[Any]:


							'''simple docstring'''

							if not isinstance(__snake_case ,						(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(__snake_case				)}"""				)

							_snake_case       :					Optional[Any]				       =   image.to(device=__snake_case ,						dtype=__snake_case				)

							_snake_case       :					str				       =   batch_size * num_images_per_prompt

							if image.shape[1] == 4:
												_snake_case       :					Optional[Any]				       =   image

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

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

												_snake_case       :					Tuple				       =   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       :					Dict				       =   randn_tensor(__snake_case ,						generator=__snake_case ,						device=__snake_case ,						dtype=__snake_case				)

							# get latents
							_snake_case       :					List[str]				       =   self.scheduler.add_noise(__snake_case ,						__snake_case ,						__snake_case				)

							_snake_case       :					Any				       =   init_latents

							return latents







		def        lowercase       (    self ,						__snake_case=0				)      ->      str:


							'''simple docstring'''

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

							_snake_case       :					Dict				       =   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(__snake_case ,						__snake_case				)







		def        lowercase       (    self ,						__snake_case=0				)      ->      str:


							'''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."				)

							_snake_case       :					str				       =   torch.device(f"""cuda:{gpu_id}"""				)

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

							_snake_case       :					List[str]				       =   None
							for cpu_offloaded_model in [self.unet, self.movq]:
												_snake_case       , _snake_case       :					Any				       =   cpu_offload_with_hook(__snake_case ,						__snake_case ,						prev_module_hook=__snake_case				)

							# We'll offload the last model manually.
							_snake_case       :					int				       =   hook







		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def        lowercase       (    self				)      ->      Union[str, Any]:


							'''simple docstring'''

							if not hasattr(self.unet ,						"_hf_hook"				):
												return self.device
							for module in self.unet.modules():
												if (
												    hasattr(__snake_case ,						"_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(__snake_case				)
		def __call__(    self ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case = 5_1_2 ,						__snake_case = 5_1_2 ,						__snake_case = 1_0_0 ,						__snake_case = 4.0 ,						__snake_case = 0.3 ,						__snake_case = 1 ,						__snake_case = None ,						__snake_case = "pil" ,						__snake_case = True ,						)      ->      str:


							'''simple docstring'''

							_snake_case       :					Dict				       =   self._execution_device

							_snake_case       :					str				       =   guidance_scale > 1.0

							if isinstance(__snake_case ,						__snake_case				):
												_snake_case       :					Optional[Any]				       =   torch.cat(__snake_case ,						dim=0				)
							_snake_case       :					int				       =   image_embeds.shape[0]
							if isinstance(__snake_case ,						__snake_case				):
												_snake_case       :					int				       =   torch.cat(__snake_case ,						dim=0				)

							if do_classifier_free_guidance:
												_snake_case       :					List[str]				       =   image_embeds.repeat_interleave(__snake_case ,						dim=0				)
												_snake_case       :					Union[str, Any]				       =   negative_image_embeds.repeat_interleave(__snake_case ,						dim=0				)

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

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

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

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

												_snake_case       :					List[Any]				       =   {"image_embeds": image_embeds}
												_snake_case       :					Dict				       =   self.unet(
												    sample=__snake_case ,						timestep=__snake_case ,						encoder_hidden_states=__snake_case ,						added_cond_kwargs=__snake_case ,						return_dict=__snake_case ,						)[0]

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

												# compute the previous noisy sample x_t -> x_t-1
												_snake_case       :					Optional[Any]				       =   self.scheduler.step(
												    __snake_case ,						__snake_case ,						__snake_case ,						generator=__snake_case ,						)[0]

							# post-processing
							_snake_case       :					List[str]				       =   self.movq.decode(__snake_case ,						force_not_quantize=__snake_case				)["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       :					Optional[int]				       =   image * 0.5 + 0.5
												_snake_case       :					Tuple				       =   image.clamp(0 ,						1				)
												_snake_case       :					Optional[Any]				       =   image.cpu().permute(0 ,						2 ,						3 ,						1				).float().numpy()

							if output_type == "pil":
												_snake_case       :					List[Any]				       =   self.numpy_to_pil(__snake_case				)

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=__snake_case				)






import sys
import turtle







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  ):
					my_pen.up()
					my_pen.goto(vertexa[0]  ,  vertexa[1]				)
					my_pen.down()
					my_pen.goto(vertexa[0]  ,  vertexa[1]				)
					my_pen.goto(vertexa[0]  ,  vertexa[1]				)
					my_pen.goto(vertexa[0]  ,  vertexa[1]				)

					if depth == 0:
										return

					triangle(UpperCAmelCase  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  depth - 1				)
					triangle(UpperCAmelCase  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  depth - 1				)
					triangle(UpperCAmelCase  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  depth - 1				)


if __name__ == "__main__":
							if len(sys.argv) != 2:
														raise ValueError(
														    'Correct format for using this script: '
														    'python fractals.py <int:depth_for_fractal>'
														)
							__lowerCAmelCase :Any     =    turtle.Turtle()
							my_pen.ht()
							my_pen.speed(5)
							my_pen.pencolor('red')

							__lowerCAmelCase :Optional[int]     =    [(-1_75, -1_25), (0, 1_75), (1_75, -1_25)]  # vertices of triangle
							triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))