


'''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


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







class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     def __init__(   self			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     lowerCamelCase			,     ):


          '''simple docstring'''





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


     def lowerCAmelCase__  (   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	:       List[str]      =							self.unet.config.attention_head_dim // 2
          self.unet.set_attention_slice(lowerCamelCase		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          self.enable_attention_slicing(lowerCamelCase		)


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


          '''simple docstring'''





          if isinstance(lowerCamelCase			,     lowerCamelCase		):
               __A	:       Optional[Any]      =							1
          elif isinstance(lowerCamelCase			,     lowerCamelCase		):
               __A	:       int      =							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}."		)

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

          # get prompt text embeddings
          __A	:       Optional[Any]      =							self.tokenizer(
              lowerCamelCase			,     padding="max_length"			,     max_length=self.tokenizer.model_max_length			,     return_tensors="pt"			,     )
          __A	:       Tuple      =							text_inputs.input_ids

          if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
               __A	:       str      =							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}"		)
               __A	:       Dict      =							text_input_ids[:, : self.tokenizer.model_max_length]

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

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

               __A	:       Tuple      =							text_input_ids.shape[-1]
               __A	:       Optional[int]      =							self.tokenizer(
                   lowerCamelCase			,     padding="max_length"			,     max_length=lowerCamelCase			,     truncation=lowerCamelCase			,     return_tensors="pt"			,     )
               __A	:       Optional[int]      =							self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]

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

          # set timesteps
          self.scheduler.set_timesteps(lowerCamelCase		)

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

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

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

               # predict the noise residual
               __A	:       Dict      =							self.unet(lowerCamelCase			,     lowerCamelCase			,     encoder_hidden_states=lowerCamelCase		).sample

               # perform guidance
               if do_classifier_free_guidance:
                    __A  ,__A	:       Any      =							noise_pred.chunk(2		)
                    __A	:       List[str]      =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

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

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

          __A	:       Optional[Any]      =							1 / 0.1_8215 * latents
          __A	:       Optional[int]      =							self.vae.decode(lowerCamelCase		).sample

          __A	:       Union[str, 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
          __A	:       int      =							image.cpu().permute(0			,     2			,     3			,     1		).float().numpy()

          if self.safety_checker is not None:
               __A	:       Optional[int]      =							self.feature_extractor(self.numpy_to_pil(lowerCamelCase		)			,     return_tensors="pt"		).to(
                   self.device		)
               __A  ,__A	:       int      =							self.safety_checker(
                   images=lowerCamelCase			,     clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype		)		)
          else:
               __A	:       Any      =							None

          if output_type == "pil":
               __A	:       Optional[Any]      =							self.numpy_to_pil(lowerCamelCase		)

          if not return_dict:
               return (image, has_nsfw_concept)

          return StableDiffusionPipelineOutput(images=lowerCamelCase			,     nsfw_content_detected=lowerCamelCase		)





'''simple docstring'''


def 		_lowercase (SCREAMING_SNAKE_CASE							):




     '''simple docstring'''





     __A	:       List[str]      =							1
     __A	:       Union[str, Any]      =							2
     while i * i <= n:
          __A	:       List[Any]      =							0
          while n % i == 0:
               n //= i
               multiplicity += 1
          n_divisors *= multiplicity + 1
          i += 1
     if n > 1:
          n_divisors *= 2
     return n_divisors


def 		_lowercase ():




     '''simple docstring'''





     __A	:       Optional[Any]      =							1
     __A	:       Dict      =							1

     while True:
          i += 1
          t_num += i

          if count_divisors(SCREAMING_SNAKE_CASE							) > 500:
               break

     return t_num


if __name__ == "__main__":
      print(solution())

