


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

_lowerCamelCase     =  '\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 __lowercase     (      lowerCamelCase_  :      str						,					lowerCamelCase_  :      List[str]						,					lowerCamelCase_  :      Optional[int]=8  ):
				SCREAMING_SNAKE_CASE__     							=				height // scale_factor**2
				if height % scale_factor**2 != 0:
								new_height += 1
				SCREAMING_SNAKE_CASE__     							=				width // scale_factor**2
				if width % scale_factor**2 != 0:
								new_width += 1
				return new_height * scale_factor, new_width * scale_factor


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		):
								super().__init__()

								self.register_modules(
								    unet=UpperCAmelCase__       ,		scheduler=UpperCAmelCase__       ,		movq=UpperCAmelCase__       ,		)
								SCREAMING_SNAKE_CASE__     							=				2 ** (len(self.movq.config.block_out_channels     ) - 1)



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__     ):
								if latents is None:
												SCREAMING_SNAKE_CASE__     							=				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}'''     )
												SCREAMING_SNAKE_CASE__     							=				latents.to(UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				latents * scheduler.init_noise_sigma
								return latents



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__=0     ):
								if is_accelerate_available():
												from accelerate import cpu_offload
								else:
												raise ImportError("Please install accelerate via `pip install accelerate`"     )

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

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



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__=0     ):
								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__     							=				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)

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

								# We'll offload the last model manually.
								SCREAMING_SNAKE_CASE__     							=				hook



				@property
				# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
				def   lowerCAmelCase__		(						self     ):
								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       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = 512       ,		UpperCAmelCase__ = 512       ,		UpperCAmelCase__ = 100       ,		UpperCAmelCase__ = 4.0       ,		UpperCAmelCase__ = 1       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = "pil"       ,		UpperCAmelCase__ = True       ,		):
								SCREAMING_SNAKE_CASE__     							=				self._execution_device

								SCREAMING_SNAKE_CASE__     							=				guidance_scale > 1.0

								if isinstance(UpperCAmelCase__       ,		UpperCAmelCase__     ):
												SCREAMING_SNAKE_CASE__     							=				torch.cat(UpperCAmelCase__       ,		dim=0     )
								if isinstance(UpperCAmelCase__       ,		UpperCAmelCase__     ):
												SCREAMING_SNAKE_CASE__     							=				torch.cat(UpperCAmelCase__       ,		dim=0     )
								if isinstance(UpperCAmelCase__       ,		UpperCAmelCase__     ):
												SCREAMING_SNAKE_CASE__     							=				torch.cat(UpperCAmelCase__       ,		dim=0     )

								SCREAMING_SNAKE_CASE__     							=				image_embeds.shape[0] * num_images_per_prompt

								if do_classifier_free_guidance:
												SCREAMING_SNAKE_CASE__     							=				image_embeds.repeat_interleave(UpperCAmelCase__       ,		dim=0     )
												SCREAMING_SNAKE_CASE__     							=				negative_image_embeds.repeat_interleave(UpperCAmelCase__       ,		dim=0     )
												SCREAMING_SNAKE_CASE__     							=				hint.repeat_interleave(UpperCAmelCase__       ,		dim=0     )

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

								SCREAMING_SNAKE_CASE__     							=				self.movq.config.latent_channels

								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				downscale_height_and_width(UpperCAmelCase__       ,		UpperCAmelCase__       ,		self.movq_scale_factor     )

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

												SCREAMING_SNAKE_CASE__     							=				{"image_embeds": image_embeds, "hint": hint}
												SCREAMING_SNAKE_CASE__     							=				self.unet(
												    sample=UpperCAmelCase__       ,		timestep=UpperCAmelCase__       ,		encoder_hidden_states=UpperCAmelCase__       ,		added_cond_kwargs=UpperCAmelCase__       ,		return_dict=UpperCAmelCase__       ,		)[0]

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

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

								if output_type == "pil":
												SCREAMING_SNAKE_CASE__     							=				self.numpy_to_pil(UpperCAmelCase__     )

								if not return_dict:
												return (image,)

								return ImagePipelineOutput(images=UpperCAmelCase__     )




"""simple docstring"""






from __future__ import annotations


def __lowercase     (      lowerCamelCase_  :      list						,					lowerCamelCase_  :      int						,					lowerCamelCase_  :      int						,					lowerCamelCase_  :      int  ):
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				input_list[low:mid], input_list[mid : high + 1]
				while left and right:
								result.append((left if left[0] <= right[0] else right).pop(0  )  )
				SCREAMING_SNAKE_CASE__     							=				result + left + right
				return input_list


def __lowercase     (      lowerCamelCase_  :      list  ):
				if len(lowerCamelCase_  ) <= 1:
								return input_list
				SCREAMING_SNAKE_CASE__     							=				list(lowerCamelCase_  )

				# iteration for two-way merging
				SCREAMING_SNAKE_CASE__     							=				2
				while p <= len(lowerCamelCase_  ):
								# getting low, high and middle value for merge-sort of single list
								for i in range(0						,					len(lowerCamelCase_  )						,					lowerCamelCase_  ):
												SCREAMING_SNAKE_CASE__     							=				i
												SCREAMING_SNAKE_CASE__     							=				i + p - 1
												SCREAMING_SNAKE_CASE__     							=				(low + high + 1) // 2
												SCREAMING_SNAKE_CASE__     							=				merge(lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_  )
								# final merge of last two parts
								if p * 2 >= len(lowerCamelCase_  ):
												SCREAMING_SNAKE_CASE__     							=				i
												SCREAMING_SNAKE_CASE__     							=				merge(lowerCamelCase_						,					0						,					lowerCamelCase_						,					len(lowerCamelCase_  ) - 1  )
												break
								p *= 2

				return input_list


if __name__ == "__main__":
	_lowerCamelCase     =  input('Enter numbers separated by a comma:\n').strip()
	if user_input == "":
		_lowerCamelCase     =  []
	else:
		_lowerCamelCase     =  [int(item.strip()) for item in user_input.split(',')]
	print(iter_merge_sort(unsorted))
