
def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					if not isinstance(lowerCAmelCase__ ,			lowerCAmelCase__		):
										raise ValueError('''Input must be an integer'''		)
					if input_num <= 0:
										raise ValueError('''Input must be positive'''		)
					return sum(
					    divisor for divisor in range(1 ,			input_num // 2 + 1		) if input_num % divisor == 0		)


if __name__ == "__main__":
							import doctest

							doctest.testmod()






import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


lowercase__		:Optional[int]   =    logging.get_logger(__name__)  # pylint: disable=invalid-name







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):




							def __init__(				self   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__   ,):
												super().__init__()
												self.register_modules(
												    vae=A__   ,text_encoder=A__   ,tokenizer=A__   ,unet=A__   ,scheduler=A__   ,safety_checker=A__   ,feature_extractor=A__   ,)




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




							def  A__      (				self):
												self.enable_attention_slicing(A__)





							@torch.no_grad()
							def __call__(				self   ,A__   ,A__ = 5_1_2   ,A__ = 5_1_2   ,A__ = 5_0   ,A__ = 7.5   ,A__ = None   ,A__ = 1   ,A__ = 0.0   ,A__ = None   ,A__ = None   ,A__ = "pil"   ,A__ = True   ,A__ = None   ,A__ = 1   ,A__ = None   ,**A__   ,):

												if isinstance(A__   ,A__):
																	lowercase							=						1
												elif isinstance(A__   ,A__):
																	lowercase							=						len(A__)
												else:
																	raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(A__)}')

												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 (callback_steps is None) or (
												    callback_steps is not None and (not isinstance(A__   ,A__) or callback_steps <= 0)
												):
																	raise ValueError(
																	    f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
																	    f' {type(A__)}.')

												# get prompt text embeddings
												lowercase							=						self.tokenizer(
												    A__   ,padding='''max_length'''   ,max_length=self.tokenizer.model_max_length   ,return_tensors='''pt'''   ,)
												lowercase							=						text_inputs.input_ids

												if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																	lowercase							=						self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
																	logger.warning(
																	    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
																	    f' {self.tokenizer.model_max_length} tokens: {removed_text}')
																	lowercase							=						text_input_ids[:, : self.tokenizer.model_max_length]

												if text_embeddings is None:
																	lowercase							=						self.text_encoder(text_input_ids.to(self.device))[0]

												# duplicate text embeddings for each generation per prompt, using mps friendly method
												lowercase	,						lowercase	,						lowercase							=						text_embeddings.shape
												lowercase							=						text_embeddings.repeat(1   ,A__   ,1)
												lowercase							=						text_embeddings.view(bs_embed * num_images_per_prompt   ,A__   ,-1)

												# 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.
												lowercase							=						guidance_scale > 1.0
												# get unconditional embeddings for classifier free guidance
												if do_classifier_free_guidance:
																	lowercase							=						42
																	if negative_prompt is None:
																						lowercase							=						['''''']
																	elif type(A__) is not type(A__):
																						raise TypeError(
																						    f'`negative_prompt` should be the same type to `prompt`, but got {type(A__)} !='
																						    f' {type(A__)}.')
																	elif isinstance(A__   ,A__):
																						lowercase							=						[negative_prompt]
																	elif batch_size != len(A__):
																						raise ValueError(
																						    f'`negative_prompt`: {negative_prompt} has batch size {len(A__)}, but `prompt`:'
																						    f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
																						    ''' the batch size of `prompt`.''')
																	else:
																						lowercase							=						negative_prompt

																	lowercase							=						text_input_ids.shape[-1]
																	lowercase							=						self.tokenizer(
																	    A__   ,padding='''max_length'''   ,max_length=A__   ,truncation=A__   ,return_tensors='''pt'''   ,)
																	lowercase							=						self.text_encoder(uncond_input.input_ids.to(self.device))[0]

																	# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																	lowercase							=						uncond_embeddings.shape[1]
																	lowercase							=						uncond_embeddings.repeat(A__   ,A__   ,1)
																	lowercase							=						uncond_embeddings.view(batch_size * num_images_per_prompt   ,A__   ,-1)

																	# 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
																	lowercase							=						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`.
												lowercase							=						(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
												lowercase							=						(batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
												lowercase							=						text_embeddings.dtype
												if latents is None:
																	if self.device.type == "mps":
																						# randn does not exist on mps
																						lowercase							=						torch.randn(
																						    A__   ,generator=A__   ,device='''cpu'''   ,dtype=A__).to(self.device)
																						lowercase							=						torch.randn(A__   ,generator=A__   ,device='''cpu'''   ,dtype=A__).to(
																						    self.device)
																	else:
																						lowercase							=						torch.randn(
																						    A__   ,generator=A__   ,device=self.device   ,dtype=A__)
																						lowercase							=						torch.randn(A__   ,generator=A__   ,device=self.device   ,dtype=A__)
												else:
																	if latents_reference.shape != latents_shape:
																						raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}')
																	lowercase							=						latents_reference.to(self.device)
																	lowercase							=						latents.to(self.device)

												# This is the key part of the pipeline where we
												# try to ensure that the generated images w/ the same seed
												# but different sizes actually result in similar images
												lowercase							=						(latents_shape[3] - latents_shape_reference[3]) // 2
												lowercase							=						(latents_shape[2] - latents_shape_reference[2]) // 2
												lowercase							=						latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
												lowercase							=						latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
												lowercase							=						0 if dx < 0 else dx
												lowercase							=						0 if dy < 0 else dy
												lowercase							=						max(-dx   ,0)
												lowercase							=						max(-dy   ,0)
												# import pdb
												# pdb.set_trace()
												lowercase							=						latents_reference[:, :, dy : dy + h, dx : dx + w]

												# set timesteps
												self.scheduler.set_timesteps(A__)

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

												# scale the initial noise by the standard deviation required by the scheduler
												lowercase							=						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]
												lowercase							=						'''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())
												lowercase							=						{}
												if accepts_eta:
																	lowercase							=						eta

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

																	# predict the noise residual
																	lowercase							=						self.unet(A__   ,A__   ,encoder_hidden_states=A__).sample

																	# perform guidance
																	if do_classifier_free_guidance:
																						lowercase	,						lowercase							=						noise_pred.chunk(2)
																						lowercase							=						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																	# compute the previous noisy sample x_t -> x_t-1
																	lowercase							=						self.scheduler.step(A__   ,A__   ,A__   ,**A__).prev_sample

																	# call the callback, if provided
																	if callback is not None and i % callback_steps == 0:
																						callback(A__   ,A__   ,A__)

												lowercase							=						1 / 0.18215 * latents
												lowercase							=						self.vae.decode(A__).sample

												lowercase							=						(image / 2 + 0.5).clamp(0   ,1)

												# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
												lowercase							=						image.cpu().permute(0   ,2   ,3   ,1).float().numpy()

												if self.safety_checker is not None:
																	lowercase							=						self.feature_extractor(self.numpy_to_pil(A__)   ,return_tensors='''pt''').to(
																	    self.device)
																	lowercase	,						lowercase							=						self.safety_checker(
																	    images=A__   ,clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype))
												else:
																	lowercase							=						None

												if output_type == "pil":
																	lowercase							=						self.numpy_to_pil(A__)

												if not return_dict:
																	return (image, has_nsfw_concept)

												return StableDiffusionPipelineOutput(images=A__   ,nsfw_content_detected=A__)




