
'''simple docstring'''





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


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






class 							UpperCAmelCase_							(    A				):

 '''simple docstring'''







 def __init__(					self		:  Dict			,				snake_case__		:  AutoencoderKL			,				snake_case__		:  CLIPTextModel			,				snake_case__		:  CLIPTokenizer			,				snake_case__		:  UNetaDConditionModel			,				snake_case__		:  Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]			,				snake_case__		:  StableDiffusionSafetyChecker			,				snake_case__		:  CLIPImageProcessor			,				):



   '''simple docstring'''




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







 def 							UpperCamelCase  (					self		:  List[str]			,				snake_case__		:  Optional[Union[str, int]] = "auto" ):



   '''simple docstring'''




   if slice_size == "auto":
     # half the attention head size is usually a good trade-off between
     # speed and memory
     UpperCAmelCase__      :					Optional[int]				       =							self.unet.config.attention_head_dim // 2
   self.unet.set_attention_slice(snake_case__ )







 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   self.enable_attention_slicing(snake_case__ )







 @torch.no_grad()
 def __call__(					self		:  List[str]			,				snake_case__		:  Union[str, List[str]]			,				snake_case__		:  int = 5_12			,				snake_case__		:  int = 5_12			,				snake_case__		:  int = 50			,				snake_case__		:  float = 7.5			,				snake_case__		:  Optional[Union[str, List[str]]] = None			,				snake_case__		:  Optional[int] = 1			,				snake_case__		:  float = 0.0			,				snake_case__		:  Optional[torch.Generator] = None			,				snake_case__		:  Optional[torch.FloatTensor] = None			,				snake_case__		:  Optional[str] = "pil"			,				snake_case__		:  bool = True			,				snake_case__		:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None			,				snake_case__		:  int = 1			,				snake_case__		:  Optional[torch.FloatTensor] = None			,				**snake_case__		:  Optional[int]			,				):



   '''simple docstring'''




   if isinstance(snake_case__			,				snake_case__ ):
     UpperCAmelCase__      :					Optional[int]				       =							1
   elif isinstance(snake_case__			,				snake_case__ ):
     UpperCAmelCase__      :					str				       =							len(snake_case__ )
   else:
     raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(snake_case__ )}""" )

   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(snake_case__			,				snake_case__ ) or callback_steps <= 0)
   ):
     raise ValueError(
         F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
         F""" {type(snake_case__ )}.""" )

   # get prompt text embeddings
   UpperCAmelCase__      :					List[str]				       =							self.tokenizer(
       snake_case__			,				padding="max_length"			,				max_length=self.tokenizer.model_max_length			,				return_tensors="pt"			,				)
   UpperCAmelCase__      :					Any				       =							text_inputs.input_ids

   if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
     UpperCAmelCase__      :					List[Any]				       =							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}""" )
     UpperCAmelCase__      :					Tuple				       =							text_input_ids[:, : self.tokenizer.model_max_length]

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

   # duplicate text embeddings for each generation per prompt, using mps friendly method
   UpperCAmelCase__       ,			UpperCAmelCase__       ,			UpperCAmelCase__      :					List[str]				       =							text_embeddings.shape
   UpperCAmelCase__      :					Union[str, Any]				       =							text_embeddings.repeat(1			,				snake_case__			,				1 )
   UpperCAmelCase__      :					Tuple				       =							text_embeddings.view(bs_embed * num_images_per_prompt			,				snake_case__			,				-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.
   UpperCAmelCase__      :					List[Any]				       =							guidance_scale > 1.0
   # get unconditional embeddings for classifier free guidance
   if do_classifier_free_guidance:
     UpperCAmelCase__      :					List[str]
     if negative_prompt is None:
       UpperCAmelCase__      :					Dict				       =							[""]
     elif type(snake_case__ ) is not type(snake_case__ ):
       raise TypeError(
           F"""`negative_prompt` should be the same type to `prompt`, but got {type(snake_case__ )} !="""
           F""" {type(snake_case__ )}.""" )
     elif isinstance(snake_case__			,				snake_case__ ):
       UpperCAmelCase__      :					Union[str, Any]				       =							[negative_prompt]
     elif batch_size != len(snake_case__ ):
       raise ValueError(
           F"""`negative_prompt`: {negative_prompt} has batch size {len(snake_case__ )}, but `prompt`:"""
           F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
           " the batch size of `prompt`." )
     else:
       UpperCAmelCase__      :					Optional[int]				       =							negative_prompt

     UpperCAmelCase__      :					List[Any]				       =							text_input_ids.shape[-1]
     UpperCAmelCase__      :					List[str]				       =							self.tokenizer(
         snake_case__			,				padding="max_length"			,				max_length=snake_case__			,				truncation=snake_case__			,				return_tensors="pt"			,				)
     UpperCAmelCase__      :					Tuple				       =							self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]

     # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
     UpperCAmelCase__      :					Union[str, Any]				       =							uncond_embeddings.shape[1]
     UpperCAmelCase__      :					Optional[Any]				       =							uncond_embeddings.repeat(snake_case__			,				snake_case__			,				1 )
     UpperCAmelCase__      :					Optional[int]				       =							uncond_embeddings.view(batch_size * num_images_per_prompt			,				snake_case__			,				-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
     UpperCAmelCase__      :					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`.
   UpperCAmelCase__      :					List[str]				       =							(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
   UpperCAmelCase__      :					Dict				       =							(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
   UpperCAmelCase__      :					Optional[Any]				       =							text_embeddings.dtype
   if latents is None:
     if self.device.type == "mps":
       # randn does not exist on mps
       UpperCAmelCase__      :					Dict				       =							torch.randn(
           snake_case__			,				generator=snake_case__			,				device="cpu"			,				dtype=snake_case__ ).to(self.device )
       UpperCAmelCase__      :					str				       =							torch.randn(snake_case__			,				generator=snake_case__			,				device="cpu"			,				dtype=snake_case__ ).to(
           self.device )
     else:
       UpperCAmelCase__      :					List[Any]				       =							torch.randn(
           snake_case__			,				generator=snake_case__			,				device=self.device			,				dtype=snake_case__ )
       UpperCAmelCase__      :					List[Any]				       =							torch.randn(snake_case__			,				generator=snake_case__			,				device=self.device			,				dtype=snake_case__ )
   else:
     if latents_reference.shape != latents_shape:
       raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
     UpperCAmelCase__      :					Optional[Any]				       =							latents_reference.to(self.device )
     UpperCAmelCase__      :					str				       =							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
   UpperCAmelCase__      :					Any				       =							(latents_shape[3] - latents_shape_reference[3]) // 2
   UpperCAmelCase__      :					int				       =							(latents_shape[2] - latents_shape_reference[2]) // 2
   UpperCAmelCase__      :					Any				       =							latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
   UpperCAmelCase__      :					Dict				       =							latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
   UpperCAmelCase__      :					List[Any]				       =							0 if dx < 0 else dx
   UpperCAmelCase__      :					List[Any]				       =							0 if dy < 0 else dy
   UpperCAmelCase__      :					Optional[Any]				       =							max(-dx			,				0 )
   UpperCAmelCase__      :					List[str]				       =							max(-dy			,				0 )
   # import pdb
   # pdb.set_trace()
   UpperCAmelCase__      :					List[Any]				       =							latents_reference[:, :, dy : dy + h, dx : dx + w]

   # set timesteps
   self.scheduler.set_timesteps(snake_case__ )

   # Some schedulers like PNDM have timesteps as arrays
   # It's more optimized to move all timesteps to correct device beforehand
   UpperCAmelCase__      :					List[Any]				       =							self.scheduler.timesteps.to(self.device )

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

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

     # predict the noise residual
     UpperCAmelCase__      :					str				       =							self.unet(snake_case__			,				snake_case__			,				encoder_hidden_states=snake_case__ ).sample

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

     # compute the previous noisy sample x_t -> x_t-1
     UpperCAmelCase__      :					Union[str, Any]				       =							self.scheduler.step(snake_case__			,				snake_case__			,				snake_case__			,				**snake_case__ ).prev_sample

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

   UpperCAmelCase__      :					List[str]				       =							1 / 0.18215 * latents
   UpperCAmelCase__      :					Any				       =							self.vae.decode(snake_case__ ).sample

   UpperCAmelCase__      :					List[Any]				       =							(image / 2 + 0.5).clamp(0			,				1 )

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

   if self.safety_checker is not None:
     UpperCAmelCase__      :					str				       =							self.feature_extractor(self.numpy_to_pil(snake_case__ )			,				return_tensors="pt" ).to(
         self.device )
     UpperCAmelCase__       ,			UpperCAmelCase__      :					Union[str, Any]				       =							self.safety_checker(
         images=snake_case__			,				clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype ) )
   else:
     UpperCAmelCase__      :					List[Any]				       =							None

   if output_type == "pil":
     UpperCAmelCase__      :					str				       =							self.numpy_to_pil(snake_case__ )

   if not return_dict:
     return (image, has_nsfw_concept)

   return StableDiffusionPipelineOutput(images=snake_case__			,				nsfw_content_detected=snake_case__ )





'''simple docstring'''





def snake_case_     (					lowercase__		):
  return sum(i for i in range(1   ,       number // 2 + 1		) if number % i == 0		) == number


if __name__ == "__main__":
      print("""Program to check whether a number is a Perfect number or not...""")
      SCREAMING_SNAKE_CASE     =       int(input("""Enter number: """).strip())
      print(F'{number} is {"" if perfect(number) else "not "}a Perfect Number.')



