






"""simple docstring"""
import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

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

def 							snake_case (  A__      ,A__      ,A__		):
			if isinstance(A__      ,torch.Tensor		):
						return image
			elif isinstance(A__      ,PIL.Image.Image		):
						UpperCAmelCase_	:					int     	=   [image]

			if isinstance(image[0]      ,PIL.Image.Image		):
						UpperCAmelCase_	:					List[Any]     	=   [np.array(i.resize((w, h)      ,resample=PIL_INTERPOLATION["lanczos"]		)		)[None, :] for i in image]
						UpperCAmelCase_	:					Dict     	=   np.concatenate(A__      ,axis=0		)
						UpperCAmelCase_	:					List[Any]     	=   np.array(A__		).astype(np.floataa		) / 255.0
						UpperCAmelCase_	:					List[str]     	=   image.transpose(0      ,3      ,1      ,2		)
						UpperCAmelCase_	:					Any     	=   2.0 * image - 1.0
						UpperCAmelCase_	:					Optional[Any]     	=   torch.from_numpy(A__		)
			elif isinstance(image[0]      ,torch.Tensor		):
						UpperCAmelCase_	:					Tuple     	=   torch.cat(A__      ,dim=0		)
			return image

def 							snake_case (  A__      ,A__      ,A__      ,A__=0.9995		):
			if not isinstance(A__      ,np.ndarray		):
						UpperCAmelCase_	:					Any     	=   True
						UpperCAmelCase_	:					Optional[Any]     	=   va.device
						UpperCAmelCase_	:					Any     	=   va.cpu().numpy()
						UpperCAmelCase_	:					Union[str, Any]     	=   va.cpu().numpy()

			UpperCAmelCase_	:					List[str]     	=   np.sum(va * va / (np.linalg.norm(A__		) * np.linalg.norm(A__		))		)
			if np.abs(A__		) > DOT_THRESHOLD:
						UpperCAmelCase_	:					str     	=   (1 - t) * va + t * va
			else:
						UpperCAmelCase_	:					Union[str, Any]     	=   np.arccos(A__		)
						UpperCAmelCase_	:					Optional[Any]     	=   np.sin(A__		)
						UpperCAmelCase_	:					Optional[int]     	=   theta_a * t
						UpperCAmelCase_	:					Any     	=   np.sin(A__		)
						UpperCAmelCase_	:					int     	=   np.sin(theta_a - theta_t		) / sin_theta_a
						UpperCAmelCase_	:					int     	=   sin_theta_t / sin_theta_a
						UpperCAmelCase_	:					int     	=   sa * va + sa * va

			if inputs_are_torch:
						UpperCAmelCase_	:					List[Any]     	=   torch.from_numpy(A__		).to(A__		)

			return va

def 							snake_case (  A__      ,A__		):
			UpperCAmelCase_	:					Dict     	=   F.normalize(A__      ,dim=-1		)
			UpperCAmelCase_	:					str     	=   F.normalize(A__      ,dim=-1		)
			return (x - y).norm(dim=-1		).div(2		).arcsin().pow(2		).mul(2		)

def 							snake_case (  A__      ,A__		):
			for param in model.parameters():
						UpperCAmelCase_	:					List[str]     	=   value







class      UpperCamelCase_			(__A	):





	def __init__( self   :					Optional[int]      ,      lowerCAmelCase_   :					AutoencoderKL      ,      lowerCAmelCase_   :					CLIPTextModel      ,      lowerCAmelCase_   :					CLIPModel      ,      lowerCAmelCase_   :					CLIPTokenizer      ,      lowerCAmelCase_   :					UNetaDConditionModel      ,      lowerCAmelCase_   :					Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]      ,      lowerCAmelCase_   :					CLIPFeatureExtractor      ,      lowerCAmelCase_   :					Optional[int]=None      ,      lowerCAmelCase_   :					List[str]=None      ,      lowerCAmelCase_   :					Tuple=None      ,      )		->     Any:
				super().__init__()
				self.register_modules(
				    vae=lowerCAmelCase_      ,      text_encoder=lowerCAmelCase_      ,      clip_model=lowerCAmelCase_      ,      tokenizer=lowerCAmelCase_      ,      unet=lowerCAmelCase_      ,      scheduler=lowerCAmelCase_      ,      feature_extractor=lowerCAmelCase_      ,      coca_model=lowerCAmelCase_      ,      coca_tokenizer=lowerCAmelCase_      ,      coca_transform=lowerCAmelCase_      ,      )
				UpperCAmelCase_	:					str     	=   (
				    feature_extractor.size
				    if isinstance(feature_extractor.size      ,      lowerCAmelCase_		)
				    else feature_extractor.size["shortest_edge"]
				)
				UpperCAmelCase_	:					str     	=   transforms.Normalize(mean=feature_extractor.image_mean      ,      std=feature_extractor.image_std		)
				set_requires_grad(self.text_encoder      ,      lowerCAmelCase_		)
				set_requires_grad(self.clip_model      ,      lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str      ,      lowerCAmelCase_   :					Optional[Union[str, int]] = "auto"		)		->     Union[str, Any]:
				if slice_size == "auto":
							# half the attention head size is usually a good trade-off between
							# speed and memory
							UpperCAmelCase_	:					List[Any]     	=   self.unet.config.attention_head_dim // 2
				self.unet.set_attention_slice(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     int:
				self.enable_attention_slicing(lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     Optional[int]:
				set_requires_grad(self.vae      ,      lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str		)		->     int:
				set_requires_grad(self.vae      ,      lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     Optional[Any]:
				set_requires_grad(self.unet      ,      lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     Union[str, Any]:
				set_requires_grad(self.unet      ,      lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					List[Any]      ,      lowerCAmelCase_   :					Tuple      ,      lowerCAmelCase_   :					Tuple		)		->     List[str]:
				# get the original timestep using init_timestep
				UpperCAmelCase_	:					Optional[Any]     	=   min(int(num_inference_steps * strength		)      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					Union[str, Any]     	=   max(num_inference_steps - init_timestep      ,      0		)
				UpperCAmelCase_	:					Union[str, Any]     	=   self.scheduler.timesteps[t_start:]

				return timesteps, num_inference_steps - t_start





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict      ,      lowerCAmelCase_   :					List[Any]      ,      lowerCAmelCase_   :					List[Any]      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					Dict      ,      lowerCAmelCase_   :					List[Any]      ,      lowerCAmelCase_   :					Optional[int]=None		)		->     Tuple:
				if not isinstance(lowerCAmelCase_      ,      torch.Tensor		):
							raise ValueError(f"""`image` has to be of type `torch.Tensor` but is {type(lowerCAmelCase_		)}"""		)

				UpperCAmelCase_	:					Optional[Any]     	=   image.to(device=lowerCAmelCase_      ,      dtype=lowerCAmelCase_		)

				if isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		):
							UpperCAmelCase_	:					Dict     	=   [
							    self.vae.encode(image[i : i + 1]		).latent_dist.sample(generator[i]		) for i in range(lowerCAmelCase_		)
							]
							UpperCAmelCase_	:					str     	=   torch.cat(lowerCAmelCase_      ,      dim=0		)
				else:
							UpperCAmelCase_	:					Optional[int]     	=   self.vae.encode(lowerCAmelCase_		).latent_dist.sample(lowerCAmelCase_		)

				# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
				UpperCAmelCase_	:					Any     	=   0.1_8_2_1_5 * init_latents
				UpperCAmelCase_	:					List[str]     	=   init_latents.repeat_interleave(lowerCAmelCase_      ,      dim=0		)

				UpperCAmelCase_	:					Optional[Any]     	=   randn_tensor(init_latents.shape      ,      generator=lowerCAmelCase_      ,      device=lowerCAmelCase_      ,      dtype=lowerCAmelCase_		)

				# get latents
				UpperCAmelCase_	:					Dict     	=   self.scheduler.add_noise(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					Union[str, Any]     	=   init_latents

				return latents





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					List[str]		)		->     int:
				UpperCAmelCase_	:					Any     	=   self.coca_transform(lowerCAmelCase_		).unsqueeze(0		)
				with torch.no_grad(), torch.cuda.amp.autocast():
							UpperCAmelCase_	:					int     	=   self.coca_model.generate(transformed_image.to(device=self.device      ,      dtype=self.coca_model.dtype		)		)
				UpperCAmelCase_	:					List[Any]     	=   self.coca_tokenizer.decode(generated[0].cpu().numpy()		)
				return generated.split("<end_of_text>"		)[0].replace("<start_of_text>"      ,      ""		).rstrip(" .,"		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]      ,      lowerCAmelCase_   :					List[str]      ,      lowerCAmelCase_   :					Optional[Any]		)		->     List[Any]:
				UpperCAmelCase_	:					Optional[Any]     	=   self.feature_extractor.preprocess(lowerCAmelCase_		)
				UpperCAmelCase_	:					Optional[int]     	=   torch.from_numpy(clip_image_input["pixel_values"][0]		).unsqueeze(0		).to(self.device		).half()
				UpperCAmelCase_	:					List[Any]     	=   self.clip_model.get_image_features(lowerCAmelCase_		)
				UpperCAmelCase_	:					Union[str, Any]     	=   image_embeddings_clip / image_embeddings_clip.norm(p=2      ,      dim=-1      ,      keepdim=lowerCAmelCase_		)
				UpperCAmelCase_	:					str     	=   image_embeddings_clip.repeat_interleave(lowerCAmelCase_      ,      dim=0		)
				return image_embeddings_clip





	@torch.enable_grad()
	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]      ,      lowerCAmelCase_   :					Tuple      ,      lowerCAmelCase_   :					Tuple      ,      lowerCAmelCase_   :					Dict      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					Any      ,      lowerCAmelCase_   :					str      ,      )		->     List[str]:
				UpperCAmelCase_	:					str     	=   latents.detach().requires_grad_()

				UpperCAmelCase_	:					List[Any]     	=   self.scheduler.scale_model_input(lowerCAmelCase_      ,      lowerCAmelCase_		)

				# predict the noise residual
				UpperCAmelCase_	:					int     	=   self.unet(lowerCAmelCase_      ,      lowerCAmelCase_      ,      encoder_hidden_states=lowerCAmelCase_		).sample

				if isinstance(self.scheduler      ,      (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)		):
							UpperCAmelCase_	:					str     	=   self.scheduler.alphas_cumprod[timestep]
							UpperCAmelCase_	:					int     	=   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
							UpperCAmelCase_	:					List[Any]     	=   (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

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

				# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
				UpperCAmelCase_	:					List[Any]     	=   1 / 0.1_8_2_1_5 * sample
				UpperCAmelCase_	:					Dict     	=   self.vae.decode(lowerCAmelCase_		).sample
				UpperCAmelCase_	:					str     	=   (image / 2 + 0.5).clamp(0      ,      1		)

				UpperCAmelCase_	:					Any     	=   transforms.Resize(self.feature_extractor_size		)(lowerCAmelCase_		)
				UpperCAmelCase_	:					Any     	=   self.normalize(lowerCAmelCase_		).to(latents.dtype		)

				UpperCAmelCase_	:					Union[str, Any]     	=   self.clip_model.get_image_features(lowerCAmelCase_		)
				UpperCAmelCase_	:					Union[str, Any]     	=   image_embeddings_clip / image_embeddings_clip.norm(p=2      ,      dim=-1      ,      keepdim=lowerCAmelCase_		)

				UpperCAmelCase_	:					Dict     	=   spherical_dist_loss(lowerCAmelCase_      ,      lowerCAmelCase_		).mean() * clip_guidance_scale

				UpperCAmelCase_	:					str     	=   -torch.autograd.grad(lowerCAmelCase_      ,      lowerCAmelCase_		)[0]

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





	@torch.no_grad()
	def __call__( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					Union[torch.FloatTensor, PIL.Image.Image]      ,      lowerCAmelCase_   :					Union[torch.FloatTensor, PIL.Image.Image]      ,      lowerCAmelCase_   :					Optional[str] = None      ,      lowerCAmelCase_   :					Optional[str] = None      ,      lowerCAmelCase_   :					Optional[int] = 512      ,      lowerCAmelCase_   :					Optional[int] = 512      ,      lowerCAmelCase_   :					float = 0.6      ,      lowerCAmelCase_   :					Optional[int] = 50      ,      lowerCAmelCase_   :					Optional[float] = 7.5      ,      lowerCAmelCase_   :					Optional[int] = 1      ,      lowerCAmelCase_   :					float = 0.0      ,      lowerCAmelCase_   :					Optional[float] = 100      ,      lowerCAmelCase_   :					Optional[torch.Generator] = None      ,      lowerCAmelCase_   :					Optional[str] = "pil"      ,      lowerCAmelCase_   :					bool = True      ,      lowerCAmelCase_   :					float = 0.8      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					float = 0.1      ,      )		->     int:
				if isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		) and len(lowerCAmelCase_		) != batch_size:
							raise ValueError(f"""You have passed {batch_size} batch_size, but only {len(lowerCAmelCase_		)} generators."""		)

				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}."""		)

				if isinstance(lowerCAmelCase_      ,      torch.Generator		) and batch_size > 1:
							UpperCAmelCase_	:					Optional[Any]     	=   [generator] + [None] * (batch_size - 1)

				UpperCAmelCase_	:					Optional[int]     	=   [
				    ("model", self.coca_model is None),
				    ("tokenizer", self.coca_tokenizer is None),
				    ("transform", self.coca_transform is None),
				]
				UpperCAmelCase_	:					Dict     	=   [x[0] for x in coca_is_none if x[1]]
				UpperCAmelCase_	:					str     	=   ", ".join(lowerCAmelCase_		)
				# generate prompts with coca model if prompt is None
				if content_prompt is None:
							if len(lowerCAmelCase_		):
										raise ValueError(
										    f"""Content prompt is None and CoCa [{coca_is_none_str}] is None."""
										    f"""Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."""		)
							UpperCAmelCase_	:					List[str]     	=   self.get_image_description(lowerCAmelCase_		)
				if style_prompt is None:
							if len(lowerCAmelCase_		):
										raise ValueError(
										    f"""Style prompt is None and CoCa [{coca_is_none_str}] is None."""
										    f""" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."""		)
							UpperCAmelCase_	:					List[str]     	=   self.get_image_description(lowerCAmelCase_		)

				# get prompt text embeddings for content and style
				UpperCAmelCase_	:					str     	=   self.tokenizer(
				    lowerCAmelCase_      ,      padding="max_length"      ,      max_length=self.tokenizer.model_max_length      ,      truncation=lowerCAmelCase_      ,      return_tensors="pt"      ,      )
				UpperCAmelCase_	:					Optional[int]     	=   self.text_encoder(content_text_input.input_ids.to(self.device		)		)[0]

				UpperCAmelCase_	:					int     	=   self.tokenizer(
				    lowerCAmelCase_      ,      padding="max_length"      ,      max_length=self.tokenizer.model_max_length      ,      truncation=lowerCAmelCase_      ,      return_tensors="pt"      ,      )
				UpperCAmelCase_	:					List[Any]     	=   self.text_encoder(style_text_input.input_ids.to(self.device		)		)[0]

				UpperCAmelCase_	:					str     	=   slerp(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_		)

				# duplicate text embeddings for each generation per prompt
				UpperCAmelCase_	:					Dict     	=   text_embeddings.repeat_interleave(lowerCAmelCase_      ,      dim=0		)

				# set timesteps
				UpperCAmelCase_	:					List[str]     	=   "offset" in set(inspect.signature(self.scheduler.set_timesteps		).parameters.keys()		)
				UpperCAmelCase_	:					List[str]     	=   {}
				if accepts_offset:
							UpperCAmelCase_	:					Optional[Any]     	=   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
				self.scheduler.timesteps.to(self.device		)

				UpperCAmelCase_	,      UpperCAmelCase_	:					str     	=   self.get_timesteps(lowerCAmelCase_      ,      lowerCAmelCase_      ,      self.device		)
				UpperCAmelCase_	:					List[str]     	=   timesteps[:1].repeat(lowerCAmelCase_		)

				# Preprocess image
				UpperCAmelCase_	:					Optional[Any]     	=   preprocess(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					Any     	=   self.prepare_latents(
				    lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      text_embeddings.dtype      ,      self.device      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					Any     	=   preprocess(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					Dict     	=   self.prepare_latents(
				    lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      text_embeddings.dtype      ,      self.device      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					str     	=   slerp(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_		)

				if clip_guidance_scale > 0:
							UpperCAmelCase_	:					int     	=   self.get_clip_image_embeddings(lowerCAmelCase_      ,      lowerCAmelCase_		)
							UpperCAmelCase_	:					Any     	=   self.get_clip_image_embeddings(lowerCAmelCase_      ,      lowerCAmelCase_		)
							UpperCAmelCase_	:					Union[str, Any]     	=   slerp(
							    lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_		)

				# 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.
				UpperCAmelCase_	:					List[Any]     	=   guidance_scale > 1.0
				# get unconditional embeddings for classifier free guidance
				if do_classifier_free_guidance:
							UpperCAmelCase_	:					List[str]     	=   content_text_input.input_ids.shape[-1]
							UpperCAmelCase_	:					Tuple     	=   self.tokenizer([""]      ,      padding="max_length"      ,      max_length=lowerCAmelCase_      ,      return_tensors="pt"		)
							UpperCAmelCase_	:					List[str]     	=   self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]
							# duplicate unconditional embeddings for each generation per prompt
							UpperCAmelCase_	:					Optional[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
							UpperCAmelCase_	:					Optional[int]     	=   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`.
				UpperCAmelCase_	:					List[Any]     	=   (batch_size, self.unet.config.in_channels, height // 8, width // 8)
				UpperCAmelCase_	:					Union[str, Any]     	=   text_embeddings.dtype
				if latents is None:
							if self.device.type == "mps":
										# randn does not work reproducibly on mps
										UpperCAmelCase_	:					Tuple     	=   torch.randn(lowerCAmelCase_      ,      generator=lowerCAmelCase_      ,      device="cpu"      ,      dtype=lowerCAmelCase_		).to(
										    self.device		)
							else:
										UpperCAmelCase_	:					Optional[Any]     	=   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}"""		)
							UpperCAmelCase_	:					Any     	=   latents.to(self.device		)

				# scale the initial noise by the standard deviation required by the scheduler
				UpperCAmelCase_	:					List[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]
				UpperCAmelCase_	:					str     	=   "eta" in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
				UpperCAmelCase_	:					Optional[int]     	=   {}
				if accepts_eta:
							UpperCAmelCase_	:					Any     	=   eta

				# check if the scheduler accepts generator
				UpperCAmelCase_	:					Optional[int]     	=   "generator" in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
				if accepts_generator:
							UpperCAmelCase_	:					List[Any]     	=   generator

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

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

										# perform classifier free guidance
										if do_classifier_free_guidance:
													UpperCAmelCase_	,      UpperCAmelCase_	:					Tuple     	=   noise_pred.chunk(2		)
													UpperCAmelCase_	:					Dict     	=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

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

										# compute the previous noisy sample x_t -> x_t-1
										UpperCAmelCase_	:					int     	=   self.scheduler.step(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_      ,      **lowerCAmelCase_		).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
				UpperCAmelCase_	:					Tuple     	=   1 / 0.1_8_2_1_5 * latents
				UpperCAmelCase_	:					Any     	=   self.vae.decode(lowerCAmelCase_		).sample

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

				if output_type == "pil":
							UpperCAmelCase_	:					List[str]     	=   self.numpy_to_pil(lowerCAmelCase_		)

				if not return_dict:
							return (image, None)

				return StableDiffusionPipelineOutput(images=lowerCAmelCase_      ,      nsfw_content_detected=lowerCAmelCase_		)






def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      if len(SCREAMING_SNAKE_CASE   ) < 2:
            return collection

      def circle_sort_util(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ) -> bool:
            A_							       =		False

            if low == high:
                  return swapped

            A_							       =		low
            A_							       =		high

            while left < right:
                  if collection[left] > collection[right]:
                        A_       ,A_							       =		(
                            collection[right],
                            collection[left],
                        )
                        A_							       =		True

                  left += 1
                  right -= 1

            if left == right and collection[left] > collection[right + 1]:
                  A_       ,A_							       =		(
                      collection[right + 1],
                      collection[left],
                  )

                  A_							       =		True

            A_							       =		low + int((high - low) / 2   )
            A_							       =		circle_sort_util(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )
            A_							       =		circle_sort_util(SCREAMING_SNAKE_CASE		,				mid + 1		,				SCREAMING_SNAKE_CASE   )

            return swapped or left_swap or right_swap

      A_							       =		True

      while is_not_sorted is True:
            A_							       =		circle_sort_util(SCREAMING_SNAKE_CASE		,				0		,				len(SCREAMING_SNAKE_CASE   ) - 1   )

      return collection


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





