


def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    Any:




						'''simple docstring'''







						return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_=0			)		->    Optional[int]:




						'''simple docstring'''







						return sorted(lowercase_			,					key=lambda lowercase_			: x[column]			)




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_=float('''inf'''			)			)		->    Any:




						'''simple docstring'''







						for i in range(points_counts - 1			):
												for j in range(i + 1			,					lowercase_			):
																		__UpperCAmelCase :       List[Any]  						=  euclidean_distance_sqr(points[i]			,					points[j]			)
																		if current_dis < min_dis:
																								__UpperCAmelCase :       Any  						=  current_dis
						return min_dis




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_=float('''inf'''			)			)		->    Optional[int]:




						'''simple docstring'''







						for i in range(min(6			,					points_counts - 1			)			,					lowercase_			):
												for j in range(max(0			,					i - 6			)			,					lowercase_			):
																		__UpperCAmelCase :       str  						=  euclidean_distance_sqr(points[i]			,					points[j]			)
																		if current_dis < min_dis:
																								__UpperCAmelCase :       Dict  						=  current_dis
						return min_dis




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			)		->    str:




						'''simple docstring'''







						if points_counts <= 3:
												return dis_between_closest_pair(lowercase_			,					lowercase_			)

						# recursion
						__UpperCAmelCase :       int  						=  points_counts // 2
						__UpperCAmelCase :       Tuple  						=  closest_pair_of_points_sqr(
						    lowercase_			,					points_sorted_on_y[:mid]			,					lowercase_			)
						__UpperCAmelCase :       Optional[int]  						=  closest_pair_of_points_sqr(
						    lowercase_			,					points_sorted_on_y[mid:]			,					points_counts - mid			)
						__UpperCAmelCase :       int  						=  min(lowercase_			,					lowercase_			)
						__UpperCAmelCase :       str  						=  []
						for point in points_sorted_on_x:
												if abs(point[0] - points_sorted_on_x[mid][0]			) < closest_pair_dis:
																		cross_strip.append(lowercase_			)

						__UpperCAmelCase :       Dict  						=  dis_between_closest_in_strip(
						    lowercase_			,					len(lowercase_			)			,					lowercase_			)
						return min(lowercase_			,					lowercase_			)




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			)		->    Optional[int]:




						'''simple docstring'''







						__UpperCAmelCase :       str  						=  column_based_sort(lowercase_			,					column=0			)
						__UpperCAmelCase :       Any  						=  column_based_sort(lowercase_			,					column=1			)
						return (
						    closest_pair_of_points_sqr(
						        lowercase_			,					lowercase_			,					lowercase_			)
						) ** 0.5


if __name__ == "__main__":
				lowerCAmelCase							=						[(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
				print("""Distance:""", closest_pair_of_points(points, len(points)))





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							=						"""
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")


        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> 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\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""




def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_=8			)		->    List[Any]:




						'''simple docstring'''







						__UpperCAmelCase :       int  						=  height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						__UpperCAmelCase :       Union[str, Any]  						=  width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor





class        lowerCamelCase      (     _UpperCamelCase   ):


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

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


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__):
							if latents is None:
													__UpperCAmelCase :       Any  						=  randn_tensor(lowercase__							,    generator=lowercase__							,    device=lowercase__							,    dtype=lowercase__)
							else:
													if latents.shape != shape:
																			raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}")
													__UpperCAmelCase :       Union[str, Any]  						=  latents.to(lowercase__)

							__UpperCAmelCase :       Union[str, Any]  						=  latents * scheduler.init_noise_sigma
							return latents


	def    A(						self							,    lowercase__=0):
							if is_accelerate_available():
													from accelerate import cpu_offload
							else:
													raise ImportError('''Please install accelerate via `pip install accelerate`''')

							__UpperCAmelCase :       List[str]  						=  torch.device(F"cuda:{gpu_id}")

							__UpperCAmelCase :       List[Any]  						=  [
							    self.unet,
							    self.movq,
							]
							for cpu_offloaded_model in models:
													if cpu_offloaded_model is not None:
																			cpu_offload(lowercase__							,    lowercase__)


	def    A(						self							,    lowercase__=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.''')

							__UpperCAmelCase :       Optional[Any]  						=  torch.device(F"cuda:{gpu_id}")

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

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

							# We'll offload the last model manually.
							__UpperCAmelCase :       Any  						=  hook


	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def    A(						self):
							if not hasattr(self.unet							,    '''_hf_hook'''):
													return self.device
							for module in self.unet.modules():
													if (
													    hasattr(lowercase__							,    '''_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(lowercase__)
	def __call__(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__ = 5_1_2							,    lowercase__ = 5_1_2							,    lowercase__ = 1_0_0							,    lowercase__ = 4.0							,    lowercase__ = 1							,    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = "pil"							,    lowercase__ = True							,    ):
							__UpperCAmelCase :       str  						=  self._execution_device

							__UpperCAmelCase :       List[str]  						=  guidance_scale > 1.0

							if isinstance(lowercase__							,    lowercase__):
													__UpperCAmelCase :       Dict  						=  torch.cat(lowercase__							,    dim=0)
							if isinstance(lowercase__							,    lowercase__):
													__UpperCAmelCase :       Tuple  						=  torch.cat(lowercase__							,    dim=0)
							if isinstance(lowercase__							,    lowercase__):
													__UpperCAmelCase :       Any  						=  torch.cat(lowercase__							,    dim=0)

							__UpperCAmelCase :       Union[str, Any]  						=  image_embeds.shape[0] * num_images_per_prompt

							if do_classifier_free_guidance:
													__UpperCAmelCase :       Optional[int]  						=  image_embeds.repeat_interleave(lowercase__							,    dim=0)
													__UpperCAmelCase :       Dict  						=  negative_image_embeds.repeat_interleave(lowercase__							,    dim=0)
													__UpperCAmelCase :       List[Any]  						=  hint.repeat_interleave(lowercase__							,    dim=0)

							__UpperCAmelCase :       Tuple  						=  torch.cat([negative_image_embeds, image_embeds]							,    dim=0).to(dtype=self.unet.dtype							,    device=lowercase__)
							__UpperCAmelCase :       List[Any]  						=  torch.cat([hint, hint]							,    dim=0).to(dtype=self.unet.dtype							,    device=lowercase__)
							self.scheduler.set_timesteps(lowercase__							,    device=lowercase__)
							__UpperCAmelCase :       List[Any]  						=  self.scheduler.timesteps

							__UpperCAmelCase :       Any  						=  self.movq.config.latent_channels

							__UpperCAmelCase						,			__UpperCAmelCase :       List[str]  						=  downscale_height_and_width(lowercase__							,    lowercase__							,    self.movq_scale_factor)

							# create initial latent
							__UpperCAmelCase :       Union[str, Any]  						=  self.prepare_latents(
							    (batch_size, num_channels_latents, height, width)							,    image_embeds.dtype							,    lowercase__							,    lowercase__							,    lowercase__							,    self.scheduler							,    )

							for i, t in enumerate(self.progress_bar(lowercase__)):
													# expand the latents if we are doing classifier free guidance
													__UpperCAmelCase :       List[Any]  						=  torch.cat([latents] * 2) if do_classifier_free_guidance else latents

													__UpperCAmelCase :       Union[str, Any]  						=  {'''image_embeds''': image_embeds, '''hint''': hint}
													__UpperCAmelCase :       Any  						=  self.unet(
													    sample=lowercase__							,    timestep=lowercase__							,    encoder_hidden_states=lowercase__							,    added_cond_kwargs=lowercase__							,    return_dict=lowercase__							,    )[0]

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

													# compute the previous noisy sample x_t -> x_t-1
													__UpperCAmelCase :       Tuple  						=  self.scheduler.step(
													    lowercase__							,    lowercase__							,    lowercase__							,    generator=lowercase__							,    )[0]
							# post-processing
							__UpperCAmelCase :       str  						=  self.movq.decode(lowercase__							,    force_not_quantize=lowercase__)['''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 :       Dict  						=  image * 0.5 + 0.5
													__UpperCAmelCase :       Union[str, Any]  						=  image.clamp(0							,    1)
													__UpperCAmelCase :       List[str]  						=  image.cpu().permute(0							,    2							,    3							,    1).float().numpy()

							if output_type == "pil":
													__UpperCAmelCase :       List[str]  						=  self.numpy_to_pil(lowercase__)

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=lowercase__)

