code: 
  
  
  
  
  
  
  from logging import getLogger
  from typing import Any, Callable, List, Optional, Union
  
  import numpy as np
  import PIL
  import torch
  
  from ...schedulers import DDPMScheduler
  from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
  from ..pipeline_utils import ImagePipelineOutput
  from . import StableDiffusionUpscalePipeline
  
  
  __A  							=  getLogger(__name__)
  
  
  __A  							=  4
  __A  							=  7
  
  __A  							=  {
      "float16": torch.floataa,
      "float32": torch.floataa,
  }
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	List[Any]		)  ->							List[Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         if isinstance(A_     ,			torch.Tensor		):
                return image
         elif isinstance(A_     ,			PIL.Image.Image		):
                __UpperCAmelCase    							=[image]
  
         if isinstance(image[0]     ,			PIL.Image.Image		):
                __UpperCAmelCase							,   __UpperCAmelCase    							=image[0].size
                __UpperCAmelCase							,   __UpperCAmelCase    							=(x - x % 64 for x in (w, h))  # resize to integer multiple of 32
  
                __UpperCAmelCase    							=[np.array(i.resize((w, h)		)		)[None, :] for i in image]
                __UpperCAmelCase    							=np.concatenate(A_     ,			axis=0		)
                __UpperCAmelCase    							=np.array(A_		).astype(np.floataa		) / 2_5_5.0
                __UpperCAmelCase    							=image.transpose(0     ,			3     ,			1     ,			2		)
                __UpperCAmelCase    							=2.0 * image - 1.0
                __UpperCAmelCase    							=torch.from_numpy(A_		)
         elif isinstance(image[0]     ,			torch.Tensor		):
                __UpperCAmelCase    							=torch.cat(A_     ,			dim=0		)
  
         return image
  
  
  
  
  
  class 							_A (    UpperCamelCase							):
      """simple docstring"""
  
      def __init__(							self			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    OnnxRuntimeModel   ,    __SCREAMING_SNAKE_CASE			:    OnnxRuntimeModel   ,    __SCREAMING_SNAKE_CASE			:    Any   ,    __SCREAMING_SNAKE_CASE			:    OnnxRuntimeModel   ,    __SCREAMING_SNAKE_CASE			:    DDPMScheduler   ,    __SCREAMING_SNAKE_CASE			:    Any   ,    __SCREAMING_SNAKE_CASE			:    int = 350   ,    )			->       Any:
             super().__init__(
                 vae=__SCREAMING_SNAKE_CASE   ,    text_encoder=__SCREAMING_SNAKE_CASE   ,    tokenizer=__SCREAMING_SNAKE_CASE   ,    unet=__SCREAMING_SNAKE_CASE   ,    low_res_scheduler=__SCREAMING_SNAKE_CASE   ,    scheduler=__SCREAMING_SNAKE_CASE   ,    safety_checker=__SCREAMING_SNAKE_CASE   ,    feature_extractor=__SCREAMING_SNAKE_CASE   ,    watermarker=__SCREAMING_SNAKE_CASE   ,    max_noise_level=__SCREAMING_SNAKE_CASE   ,    )
  
  
  
      def __call__(							self			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, List[str]]   ,    __SCREAMING_SNAKE_CASE			:    Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]]   ,    __SCREAMING_SNAKE_CASE			:    int = 75   ,    __SCREAMING_SNAKE_CASE			:    float = 9.0   ,    __SCREAMING_SNAKE_CASE			:    int = 20   ,    __SCREAMING_SNAKE_CASE			:    Optional[Union[str, List[str]]] = None   ,    __SCREAMING_SNAKE_CASE			:    Optional[int] = 1   ,    __SCREAMING_SNAKE_CASE			:    float = 0.0   ,    __SCREAMING_SNAKE_CASE			:    Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,    __SCREAMING_SNAKE_CASE			:    Optional[torch.FloatTensor] = None   ,    __SCREAMING_SNAKE_CASE			:    Optional[np.ndarray] = None   ,    __SCREAMING_SNAKE_CASE			:    Optional[np.ndarray] = None   ,    __SCREAMING_SNAKE_CASE			:    Optional[str] = "pil"   ,    __SCREAMING_SNAKE_CASE			:    bool = True   ,    __SCREAMING_SNAKE_CASE			:    Optional[Callable[[int, int, torch.FloatTensor], None]] = None   ,    __SCREAMING_SNAKE_CASE			:    Optional[int] = 1   ,    )			->       Dict:
             self.check_inputs(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			)
  
             # 2. Define call parameters
             if prompt is not None and isinstance(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			):
                    __UpperCAmelCase    							=1
             elif prompt is not None and isinstance(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			):
                    __UpperCAmelCase    							=len(__SCREAMING_SNAKE_CASE			)
             else:
                    __UpperCAmelCase    							=prompt_embeds.shape[0]
  
             __UpperCAmelCase    							=self._execution_device
             # 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    							=guidance_scale > 1.0
  
             # 3. Encode input prompt
             __UpperCAmelCase    							=self._encode_prompt(
                 __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    prompt_embeds=__SCREAMING_SNAKE_CASE   ,    negative_prompt_embeds=__SCREAMING_SNAKE_CASE   ,    )
  
             __UpperCAmelCase    							=ORT_TO_PT_TYPE[str(text_embeddings.dtype			)]
  
             # 4. Preprocess image
             __UpperCAmelCase    							=preprocess(__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=image.cpu()
  
             # 5. set timesteps
             self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE   ,    device=__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=self.scheduler.timesteps
  
             # 5. Add noise to image
             __UpperCAmelCase    							=torch.tensor([noise_level]   ,    dtype=torch.long   ,    device=__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=torch.randn(image.shape   ,    generator=__SCREAMING_SNAKE_CASE   ,    device=__SCREAMING_SNAKE_CASE   ,    dtype=__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=self.low_res_scheduler.add_noise(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			)
  
             __UpperCAmelCase    							=2 if do_classifier_free_guidance else 1
             __UpperCAmelCase    							=np.concatenate([image] * batch_multiplier * num_images_per_prompt			)
             __UpperCAmelCase    							=np.concatenate([noise_level] * image.shape[0]			)
  
             # 6. Prepare latent variables
             __UpperCAmelCase							,   __UpperCAmelCase    							=image.shape[2:]
             __UpperCAmelCase    							=self.prepare_latents(
                 batch_size * num_images_per_prompt   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    )
  
             # 7. Check that sizes of image and latents match
             __UpperCAmelCase    							=image.shape[1]
             if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
                    raise ValueError(
                        """Incorrect configuration settings! The config of `pipeline.unet` expects"""
                        f''' {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +'''
                        f''' `num_channels_image`: {num_channels_image} '''
                        f''' = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of'''
                        """ `pipeline.unet` or your `image` input."""			)
  
             # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
             __UpperCAmelCase    							=self.prepare_extra_step_kwargs(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			)
  
             __UpperCAmelCase    							=next(
                 (input.type for input in self.unet.model.get_inputs() if input.name == """timestep""")   ,    """tensor(float)"""			)
             __UpperCAmelCase    							=ORT_TO_NP_TYPE[timestep_dtype]
  
             # 9. Denoising loop
             __UpperCAmelCase    							=len(__SCREAMING_SNAKE_CASE			) - num_inference_steps * self.scheduler.order
             with self.progress_bar(total=__SCREAMING_SNAKE_CASE			) as progress_bar:
                    for i, t in enumerate(__SCREAMING_SNAKE_CASE			):
                           # expand the latents if we are doing classifier free guidance
                           __UpperCAmelCase    							=np.concatenate([latents] * 2			) if do_classifier_free_guidance else latents
  
                           # concat latents, mask, masked_image_latents in the channel dimension
                           __UpperCAmelCase    							=self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			)
                           __UpperCAmelCase    							=np.concatenate([latent_model_input, image]   ,    axis=1			)
  
                           # timestep to tensor
                           __UpperCAmelCase    							=np.array([t]   ,    dtype=__SCREAMING_SNAKE_CASE			)
  
                           # predict the noise residual
                           __UpperCAmelCase    							=self.unet(
                               sample=__SCREAMING_SNAKE_CASE   ,    timestep=__SCREAMING_SNAKE_CASE   ,    encoder_hidden_states=__SCREAMING_SNAKE_CASE   ,    class_labels=noise_level.astype(np.intaa			)   ,    )[0]
  
                           # perform guidance
                           if do_classifier_free_guidance:
                                  __UpperCAmelCase							,   __UpperCAmelCase    							=np.split(__SCREAMING_SNAKE_CASE   ,    2			)
                           __UpperCAmelCase    							=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
  
                           # compute the previous noisy sample x_t -> x_t-1
                           __UpperCAmelCase    							=self.scheduler.step(
                               torch.from_numpy(__SCREAMING_SNAKE_CASE			)   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    **__SCREAMING_SNAKE_CASE			).prev_sample
  
                           # call the callback, if provided
                           if i == len(__SCREAMING_SNAKE_CASE			) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                                  progress_bar.update()
                                  if callback is not None and i % callback_steps == 0:
                                         callback(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			)
  
          # 10. Post-processing
             __UpperCAmelCase    							=self.decode_latents(latents.float()			)
  
             # 11. Convert to PIL
             if output_type == "pil":
                    __UpperCAmelCase    							=self.numpy_to_pil(__SCREAMING_SNAKE_CASE			)
  
             if not return_dict:
                    return (image,)
  
             return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    str			)			->       str:
             __UpperCAmelCase    							=1 / 0.08_333 * latents
             __UpperCAmelCase    							=self.vae(latent_sample=__SCREAMING_SNAKE_CASE			)[0]
             __UpperCAmelCase    							=np.clip(image / 2 + 0.5   ,    0   ,    1			)
             __UpperCAmelCase    							=image.transpose((0, 2, 3, 1)			)
             return image
  
  
  
      def _a       (							self			:    Any   ,    __SCREAMING_SNAKE_CASE			:    Union[str, List[str]]   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    bool   ,    __SCREAMING_SNAKE_CASE			:    Optional[str]   ,    __SCREAMING_SNAKE_CASE			:    Optional[torch.FloatTensor] = None   ,    __SCREAMING_SNAKE_CASE			:    Optional[torch.FloatTensor] = None   ,    )			->       Optional[int]:
             if prompt is not None and isinstance(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			):
                    __UpperCAmelCase    							=1
             elif prompt is not None and isinstance(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			):
                    __UpperCAmelCase    							=len(__SCREAMING_SNAKE_CASE			)
             else:
                    __UpperCAmelCase    							=prompt_embeds.shape[0]
  
             if prompt_embeds is None:
                    __UpperCAmelCase    							=self.tokenizer(
                        __SCREAMING_SNAKE_CASE   ,    padding="""max_length"""   ,    max_length=self.tokenizer.model_max_length   ,    truncation=__SCREAMING_SNAKE_CASE   ,    return_tensors="""pt"""   ,    )
                    __UpperCAmelCase    							=text_inputs.input_ids
                    __UpperCAmelCase    							=self.tokenizer(__SCREAMING_SNAKE_CASE   ,    padding="""longest"""   ,    return_tensors="""pt"""			).input_ids
  
                    if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                        __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			):
                           __UpperCAmelCase    							=self.tokenizer.batch_decode(
                               untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]			)
                           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}'''			)
  
                    # no positional arguments to text_encoder
                    __UpperCAmelCase    							=self.text_encoder(
                        input_ids=text_input_ids.int().to(__SCREAMING_SNAKE_CASE			)   ,    )
                    __UpperCAmelCase    							=prompt_embeds[0]
  
             __UpperCAmelCase							,   __UpperCAmelCase							,   __UpperCAmelCase    							=prompt_embeds.shape
             # duplicate text embeddings for each generation per prompt, using mps friendly method
             __UpperCAmelCase    							=prompt_embeds.repeat(1   ,    __SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=prompt_embeds.reshape(bs_embed * num_images_per_prompt   ,    __SCREAMING_SNAKE_CASE   ,    -1			)
  
             # get unconditional embeddings for classifier free guidance
             if do_classifier_free_guidance and negative_prompt_embeds is None:
                    __UpperCAmelCase    							=42
                    if negative_prompt is None:
                           __UpperCAmelCase    							=[""""""] * batch_size
                    elif type(__SCREAMING_SNAKE_CASE			) is not type(__SCREAMING_SNAKE_CASE			):
                           raise TypeError(
                               f'''`negative_prompt` should be the same type to `prompt`, but got {type(__SCREAMING_SNAKE_CASE			)} !='''
                               f''' {type(__SCREAMING_SNAKE_CASE			)}.'''			)
                    elif isinstance(__SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE			):
                           __UpperCAmelCase    							=[negative_prompt]
                    elif batch_size != len(__SCREAMING_SNAKE_CASE			):
                           raise ValueError(
                               f'''`negative_prompt`: {negative_prompt} has batch size {len(__SCREAMING_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    							=negative_prompt
  
                    __UpperCAmelCase    							=text_input_ids.shape[-1]
                    __UpperCAmelCase    							=self.tokenizer(
                        __SCREAMING_SNAKE_CASE   ,    padding="""max_length"""   ,    max_length=__SCREAMING_SNAKE_CASE   ,    truncation=__SCREAMING_SNAKE_CASE   ,    return_tensors="""pt"""   ,    )
  
                    # if hasattr(uncond_input, "attention_mask"):
                    #     attention_mask = uncond_input.attention_mask.to(device)
                    # else:
                    #     attention_mask = None
  
                    __UpperCAmelCase    							=self.text_encoder(
                        input_ids=uncond_input.input_ids.int().to(__SCREAMING_SNAKE_CASE			)   ,    )
                    __UpperCAmelCase    							=uncond_embeddings[0]
  
             if do_classifier_free_guidance:
                    __UpperCAmelCase    							=uncond_embeddings.shape[1]
                    # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                    __UpperCAmelCase    							=uncond_embeddings.repeat(1   ,    __SCREAMING_SNAKE_CASE			)
                    __UpperCAmelCase    							=uncond_embeddings.reshape(batch_size * num_images_per_prompt   ,    __SCREAMING_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    							=np.concatenate([uncond_embeddings, prompt_embeds]			)
  
             return prompt_embeds
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from collections import defaultdict
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	int = 5		)  ->							int:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=defaultdict(A_		)
         __UpperCAmelCase    							=0
  
         while True:
                __UpperCAmelCase    							=get_digits(A_		)
                freqs[digits].append(A_		)
  
                if len(freqs[digits]		) == max_base:
                       __UpperCAmelCase    							=freqs[digits][0] ** 3
                       return base
  
                num += 1
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	int		)  ->							str:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return "".join(sorted(str(num**3		)		)		)
  
  
  if __name__ == "__main__":
     print(F"""{solution() = }""")
  
  
  
  
  