
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,
)


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

_UpperCAmelCase :			int              =	"\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 				A	( lowercase   ,	lowercase   ,	lowercase=8			)       ->  Optional[int]:
		'''simple docstring'''

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


class     lowercase (		_SCREAMING_SNAKE_CASE       ):



		def __init__(			self    ,				A_    ,				A_    ,				A_    ,				)  -> Dict:
				"""simple docstring"""
				super().__init__()

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



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_    ,				A_	)  -> Optional[int]:
				"""simple docstring"""
				if latents is None:
						UpperCamelCase          =					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}'''	)
						UpperCamelCase          =					latents.to(A_	)

				UpperCamelCase          =					latents * scheduler.init_noise_sigma
				return latents



		def __UpperCamelCase			(			self    ,				A_=0	)  -> Tuple:
				"""simple docstring"""
				if is_accelerate_available():
						from accelerate import cpu_offload
				else:
						raise ImportError('Please install accelerate via `pip install accelerate`'	)

				UpperCamelCase          =					torch.device(F'''cuda:{gpu_id}'''	)

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



		def __UpperCamelCase			(			self    ,				A_=0	)  -> Dict:
				"""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.'	)

				UpperCamelCase          =					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)

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

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



		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def __UpperCamelCase			(			self	)  -> str:
				"""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    ,				)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					self._execution_device

				UpperCamelCase          =					guidance_scale > 1.0

				if isinstance(A_    ,				A_	):
						UpperCamelCase          =					torch.cat(A_    ,				dim=0	)
				if isinstance(A_    ,				A_	):
						UpperCamelCase          =					torch.cat(A_    ,				dim=0	)
				if isinstance(A_    ,				A_	):
						UpperCamelCase          =					torch.cat(A_    ,				dim=0	)

				UpperCamelCase          =					image_embeds.shape[0] * num_images_per_prompt

				if do_classifier_free_guidance:
						UpperCamelCase          =					image_embeds.repeat_interleave(A_    ,				dim=0	)
						UpperCamelCase          =					negative_image_embeds.repeat_interleave(A_    ,				dim=0	)
						UpperCamelCase          =					hint.repeat_interleave(A_    ,				dim=0	)

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

				UpperCamelCase          =					self.movq.config.latent_channels

				UpperCamelCase     ,  UpperCamelCase          =					downscale_height_and_width(A_    ,				A_    ,				self.movq_scale_factor	)

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

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

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

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

				if output_type == "pil":
						UpperCamelCase          =					self.numpy_to_pil(A_	)

				if not return_dict:
						return (image,)

				return ImagePipelineOutput(images=A_	)


from __future__ import annotations


def 				A	( lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)       ->  list:
		'''simple docstring'''

		UpperCamelCase          =					[]
		UpperCamelCase     ,  UpperCamelCase          =					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			)			)
		UpperCamelCase          =					result + left + right
		return input_list


def 				A	( lowercase			)       ->  list:
		'''simple docstring'''

		if len(lowercase			) <= 1:
				return input_list
		UpperCamelCase          =					list(lowercase			)

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

		return input_list


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