





"""simple docstring"""



import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput






class 	__snake_case      (	nn.Module  ):





	"""simple docstring"""


	def __init__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase=1.0						):



					'''simple docstring'''




					super().__init__()

					__A					:		Optional[Any]						=  cut_size
					__A					:		Optional[int]						=  cut_power







	def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase						):



					'''simple docstring'''




					__A					:		Any						=  pixel_values.shape[2:4]
					__A					:		Union[str, Any]						=  min(__lowerCamelCase      ,		__lowerCamelCase						)
					__A					:		Any						=  min(__lowerCamelCase      ,		__lowerCamelCase      ,		self.cut_size						)
					__A					:		int						=  []
					for _ in range(__lowerCamelCase						):
									__A					:		List[Any]						=  int(torch.rand([]						) ** self.cut_power * (max_size - min_size) + min_size						)
									__A					:		str						=  torch.randint(0      ,		sideX - size + 1      ,		()						)
									__A					:		Union[str, Any]						=  torch.randint(0      ,		sideY - size + 1      ,		()						)
									__A					:		int						=  pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
									cutouts.append(F.adaptive_avg_poolad(__lowerCamelCase      ,		self.cut_size						)						)
					return torch.cat(__lowerCamelCase						)







def 	__lowercase  (     snake_case_						:						List[Any]       ,snake_case_						:						Tuple		)     ->Tuple:



				'''simple docstring'''







				__A					:		Tuple						=  F.normalize(snake_case_       ,dim=-1		)
				__A					:		Dict						=  F.normalize(snake_case_       ,dim=-1		)
				return (x - y).norm(dim=-1		).div(2		).arcsin().pow(2		).mul(2		)







def 	__lowercase  (     snake_case_						:						Optional[Any]       ,snake_case_						:						Optional[Any]		)     ->Union[str, Any]:



				'''simple docstring'''







				for param in model.parameters():
								__A					:		Tuple						=  value






class 	__snake_case      (	SCREAMING_SNAKE_CASE__  ):





	"""simple docstring"""


	def __init__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		):



					'''simple docstring'''




					super().__init__()
					self.register_modules(
					    vae=__lowerCamelCase      ,		text_encoder=__lowerCamelCase      ,		clip_model=__lowerCamelCase      ,		tokenizer=__lowerCamelCase      ,		unet=__lowerCamelCase      ,		scheduler=__lowerCamelCase      ,		feature_extractor=__lowerCamelCase      ,		)

					__A					:		Union[str, Any]						=  transforms.Normalize(mean=feature_extractor.image_mean      ,		std=feature_extractor.image_std						)
					__A					:		Dict						=  (
					    feature_extractor.size
					    if isinstance(feature_extractor.size      ,		__lowerCamelCase						)
					    else feature_extractor.size['''shortest_edge''']
					)
					__A					:		Optional[int]						=  MakeCutouts(self.cut_out_size						)

					set_requires_grad(self.text_encoder      ,		__lowerCamelCase						)
					set_requires_grad(self.clip_model      ,		__lowerCamelCase						)

	def       UpperCamelCase__(					self      ,		__lowerCamelCase = "auto"						):



					'''simple docstring'''




					if slice_size == "auto":
									# half the attention head size is usually a good trade-off between
									# speed and memory
									__A					:		Any						=  self.unet.config.attention_head_dim // 2
					self.unet.set_attention_slice(__lowerCamelCase						)

	def       UpperCamelCase__(					self						):



					'''simple docstring'''




					self.enable_attention_slicing(__lowerCamelCase						)

	def       UpperCamelCase__(					self						):



					'''simple docstring'''




					set_requires_grad(self.vae      ,		__lowerCamelCase						)

	def       UpperCamelCase__(					self						):



					'''simple docstring'''




					set_requires_grad(self.vae      ,		__lowerCamelCase						)

	def       UpperCamelCase__(					self						):



					'''simple docstring'''




					set_requires_grad(self.unet      ,		__lowerCamelCase						)

	def       UpperCamelCase__(					self						):



					'''simple docstring'''




					set_requires_grad(self.unet      ,		__lowerCamelCase						)

	@torch.enable_grad()
	def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase=True      ,		):



					'''simple docstring'''




					__A					:		Any						=  latents.detach().requires_grad_()

					__A					:		str						=  self.scheduler.scale_model_input(__lowerCamelCase      ,		__lowerCamelCase						)

					# predict the noise residual
					__A					:		List[str]						=  self.unet(__lowerCamelCase      ,		__lowerCamelCase      ,		encoder_hidden_states=__lowerCamelCase						).sample

					if isinstance(self.scheduler      ,		(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)						):
									__A					:		Dict						=  self.scheduler.alphas_cumprod[timestep]
									__A					:		List[Any]						=  1 - alpha_prod_t
									# compute predicted original sample from predicted noise also called
									# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
									__A					:		Optional[int]						=  (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

									__A					:		List[str]						=  torch.sqrt(__lowerCamelCase						)
									__A					:		Optional[Any]						=  pred_original_sample * (fac) + latents * (1 - fac)
					elif isinstance(self.scheduler      ,		__lowerCamelCase						):
									__A					:		Union[str, Any]						=  self.scheduler.sigmas[index]
									__A					:		List[str]						=  latents - sigma * noise_pred
					else:
									raise ValueError(F"""scheduler type {type(self.scheduler						)} not supported"""						)

					__A					:		str						=  1 / self.vae.config.scaling_factor * sample
					__A					:		Optional[int]						=  self.vae.decode(__lowerCamelCase						).sample
					__A					:		Tuple						=  (image / 2 + 0.5).clamp(0      ,		1						)

					if use_cutouts:
									__A					:		List[str]						=  self.make_cutouts(__lowerCamelCase      ,		__lowerCamelCase						)
					else:
									__A					:		int						=  transforms.Resize(self.cut_out_size						)(__lowerCamelCase						)
					__A					:		Optional[int]						=  self.normalize(__lowerCamelCase						).to(latents.dtype						)

					__A					:		Optional[int]						=  self.clip_model.get_image_features(__lowerCamelCase						)
					__A					:		List[str]						=  image_embeddings_clip / image_embeddings_clip.norm(p=2      ,		dim=-1      ,		keepdim=__lowerCamelCase						)

					if use_cutouts:
									__A					:		Tuple						=  spherical_dist_loss(__lowerCamelCase      ,		__lowerCamelCase						)
									__A					:		Optional[Any]						=  dists.view([num_cutouts, sample.shape[0], -1]						)
									__A					:		Union[str, Any]						=  dists.sum(2						).mean(0						).sum() * clip_guidance_scale
					else:
									__A					:		Dict						=  spherical_dist_loss(__lowerCamelCase      ,		__lowerCamelCase						).mean() * clip_guidance_scale

					__A					:		int						=  -torch.autograd.grad(__lowerCamelCase      ,		__lowerCamelCase						)[0]

					if isinstance(self.scheduler      ,		__lowerCamelCase						):
									__A					:		int						=  latents.detach() + grads * (sigma**2)
									__A					:		str						=  noise_pred_original
					else:
									__A					:		Optional[Any]						=  noise_pred_original - torch.sqrt(__lowerCamelCase						) * grads
					return noise_pred, latents







	@torch.no_grad()
	def __call__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase = 512      ,		__lowerCamelCase = 512      ,		__lowerCamelCase = 50      ,		__lowerCamelCase = 7.5      ,		__lowerCamelCase = 1      ,		__lowerCamelCase = 0.0      ,		__lowerCamelCase = 100      ,		__lowerCamelCase = None      ,		__lowerCamelCase = 4      ,		__lowerCamelCase = True      ,		__lowerCamelCase = None      ,		__lowerCamelCase = None      ,		__lowerCamelCase = "pil"      ,		__lowerCamelCase = True      ,		):



					'''simple docstring'''




					if isinstance(__lowerCamelCase      ,		__lowerCamelCase						):
									__A					:		List[str]						=  1
					elif isinstance(__lowerCamelCase      ,		__lowerCamelCase						):
									__A					:		List[Any]						=  len(__lowerCamelCase						)
					else:
									raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(__lowerCamelCase						)}"""						)

					if height % 8 != 0 or width % 8 != 0:
									raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""						)

					# get prompt text embeddings
					__A					:		List[Any]						=  self.tokenizer(
					    __lowerCamelCase      ,		padding='''max_length'''      ,		max_length=self.tokenizer.model_max_length      ,		truncation=__lowerCamelCase      ,		return_tensors='''pt'''      ,		)
					__A					:		Tuple						=  self.text_encoder(text_input.input_ids.to(self.device						)						)[0]
					# duplicate text embeddings for each generation per prompt
					__A					:		Tuple						=  text_embeddings.repeat_interleave(__lowerCamelCase      ,		dim=0						)

					if clip_guidance_scale > 0:
									if clip_prompt is not None:
													__A					:		Optional[int]						=  self.tokenizer(
													    __lowerCamelCase      ,		padding='''max_length'''      ,		max_length=self.tokenizer.model_max_length      ,		truncation=__lowerCamelCase      ,		return_tensors='''pt'''      ,		).input_ids.to(self.device						)
									else:
													__A					:		int						=  text_input.input_ids.to(self.device						)
									__A					:		Tuple						=  self.clip_model.get_text_features(__lowerCamelCase						)
									__A					:		Dict						=  text_embeddings_clip / text_embeddings_clip.norm(p=2      ,		dim=-1      ,		keepdim=__lowerCamelCase						)
									# duplicate text embeddings clip for each generation per prompt
									__A					:		Dict						=  text_embeddings_clip.repeat_interleave(__lowerCamelCase      ,		dim=0						)

					# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
					# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
					# corresponds to doing no classifier free guidance.
					__A					:		str						=  guidance_scale > 1.0
					# get unconditional embeddings for classifier free guidance
					if do_classifier_free_guidance:
									__A					:		Optional[Any]						=  text_input.input_ids.shape[-1]
									__A					:		List[Any]						=  self.tokenizer(['''''']      ,		padding='''max_length'''      ,		max_length=__lowerCamelCase      ,		return_tensors='''pt'''						)
									__A					:		str						=  self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]
									# duplicate unconditional embeddings for each generation per prompt
									__A					:		int						=  uncond_embeddings.repeat_interleave(__lowerCamelCase      ,		dim=0						)

									# For classifier free guidance, we need to do two forward passes.
									# Here we concatenate the unconditional and text embeddings into a single batch
									# to avoid doing two forward passes
									__A					:		List[str]						=  torch.cat([uncond_embeddings, text_embeddings]						)

					# get the initial random noise unless the user supplied it

					# Unlike in other pipelines, latents need to be generated in the target device
					# for 1-to-1 results reproducibility with the CompVis implementation.
					# However this currently doesn't work in `mps`.
					__A					:		str						=  (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
					__A					:		List[str]						=  text_embeddings.dtype
					if latents is None:
									if self.device.type == "mps":
													# randn does not work reproducibly on mps
													__A					:		Dict						=  torch.randn(__lowerCamelCase      ,		generator=__lowerCamelCase      ,		device='''cpu'''      ,		dtype=__lowerCamelCase						).to(
													    self.device						)
									else:
													__A					:		str						=  torch.randn(__lowerCamelCase      ,		generator=__lowerCamelCase      ,		device=self.device      ,		dtype=__lowerCamelCase						)
					else:
									if latents.shape != latents_shape:
													raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""						)
									__A					:		Any						=  latents.to(self.device						)

					# set timesteps
					__A					:		Any						=  '''offset''' in set(inspect.signature(self.scheduler.set_timesteps						).parameters.keys()						)
					__A					:		Optional[Any]						=  {}
					if accepts_offset:
									__A					:		int						=  1

					self.scheduler.set_timesteps(__lowerCamelCase      ,		**__lowerCamelCase						)

					# Some schedulers like PNDM have timesteps as arrays
					# It's more optimized to move all timesteps to correct device beforehand
					__A					:		int						=  self.scheduler.timesteps.to(self.device						)

					# scale the initial noise by the standard deviation required by the scheduler
					__A					:		Union[str, Any]						=  latents * self.scheduler.init_noise_sigma

					# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
					# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
					# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
					# and should be between [0, 1]
					__A					:		Optional[int]						=  '''eta''' in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
					__A					:		Union[str, Any]						=  {}
					if accepts_eta:
									__A					:		str						=  eta

					# check if the scheduler accepts generator
					__A					:		Optional[Any]						=  '''generator''' in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
					if accepts_generator:
									__A					:		Dict						=  generator

					for i, t in enumerate(self.progress_bar(__lowerCamelCase						)						):
									# expand the latents if we are doing classifier free guidance
									__A					:		int						=  torch.cat([latents] * 2						) if do_classifier_free_guidance else latents
									__A					:		Union[str, Any]						=  self.scheduler.scale_model_input(__lowerCamelCase      ,		__lowerCamelCase						)

									# predict the noise residual
									__A					:		List[Any]						=  self.unet(__lowerCamelCase      ,		__lowerCamelCase      ,		encoder_hidden_states=__lowerCamelCase						).sample

									# perform classifier free guidance
									if do_classifier_free_guidance:
													__A					:		Dict						=  noise_pred.chunk(2						)
													__A					:		Optional[int]						=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

									# perform clip guidance
									if clip_guidance_scale > 0:
													__A					:		str						=  (
													    text_embeddings.chunk(2						)[1] if do_classifier_free_guidance else text_embeddings
													)
													__A					:		Tuple						=  self.cond_fn(
													    __lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		)

									# compute the previous noisy sample x_t -> x_t-1
									__A					:		str						=  self.scheduler.step(__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		**__lowerCamelCase						).prev_sample

					# scale and decode the image latents with vae
					__A					:		List[str]						=  1 / self.vae.config.scaling_factor * latents
					__A					:		List[str]						=  self.vae.decode(__lowerCamelCase						).sample

					__A					:		List[Any]						=  (image / 2 + 0.5).clamp(0      ,		1						)
					__A					:		Any						=  image.cpu().permute(0      ,		2      ,		3      ,		1						).numpy()

					if output_type == "pil":
									__A					:		str						=  self.numpy_to_pil(__lowerCamelCase						)

					if not return_dict:
									return (image, None)

					return StableDiffusionPipelineOutput(images=__lowerCamelCase      ,		nsfw_content_detected=__lowerCamelCase						)











"""simple docstring"""



def 	__lowercase  (     snake_case_						:						Union[str, Any]		)     ->Optional[int]:



    '''simple docstring'''







    __A					:		Dict						=  [701, 301, 132, 57, 23, 10, 4, 1]
    for gap in gaps:
        for i in range(snake_case_       ,len(snake_case_		)		):
            __A					:		Tuple						=  collection[i]
            __A					:		Union[str, Any]						=  i
            while j >= gap and collection[j - gap] > insert_value:
                __A					:		Optional[int]						=  collection[j - gap]
                j -= gap
            if j != i:
                __A					:		Optional[int]						=  insert_value
    return collection


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       a_				=    input("""Enter numbers separated by a comma:\n""").strip()
       a_				=    [int(item) for item in user_input.split(""",""")]
       print(shell_sort(unsorted))




