






import math
from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


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

__snake_case							 =  """
    Examples:
        ```py
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif

        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")

        >>> repo = \"openai/shap-e\"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 15.0
        >>> prompt = \"a shark\"

        >>> images = pipe(
        ...     prompt,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], \"shark_3d.gif\")
        ```
"""







@dataclass
class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      UpperCamelCase_    : Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]]







class       UpperCAmelCase_						( lowercase			):



      """simple docstring"""
      def __init__(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    )    ->				Union[str, Any]:
          super().__init__()

          self.register_modules(
              prior=SCREAMING_SNAKE_CASE_   ,    text_encoder=SCREAMING_SNAKE_CASE_   ,    tokenizer=SCREAMING_SNAKE_CASE_   ,    scheduler=SCREAMING_SNAKE_CASE_   ,    renderer=SCREAMING_SNAKE_CASE_   ,    )
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)    ->				Optional[Any]:
          if latents is None:
              UpperCamelCase	:Dict     							=       randn_tensor(SCREAMING_SNAKE_CASE_   ,    generator=SCREAMING_SNAKE_CASE_   ,    device=SCREAMING_SNAKE_CASE_   ,    dtype=SCREAMING_SNAKE_CASE_			)
          else:
              if latents.shape != shape:
                  raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''			)
              UpperCamelCase	:Dict     							=       latents.to(SCREAMING_SNAKE_CASE_			)

          UpperCamelCase	:List[Any]     							=       latents * scheduler.init_noise_sigma
          return latents
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_=0			)    ->				Tuple:
          if is_accelerate_available():
              from accelerate import cpu_offload
          else:
              raise ImportError('''Please install accelerate via `pip install accelerate`'''			)

          UpperCamelCase	:int     							=       torch.device(F'''cuda:{gpu_id}'''			)

          UpperCamelCase	:Optional[int]     							=       [self.text_encoder, self.prior]
          for cpu_offloaded_model in models:
              if cpu_offloaded_model is not None:
                  cpu_offload(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_=0			)    ->				Optional[int]:
          if is_accelerate_available() and is_accelerate_version('''>='''   ,    '''0.17.0.dev0'''			):
              from accelerate import cpu_offload_with_hook
          else:
              raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''			)

          UpperCamelCase	:int     							=       torch.device(F'''cuda:{gpu_id}'''			)

          if self.device.type != "cpu":
              self.to('''cpu'''   ,    silence_dtype_warnings=SCREAMING_SNAKE_CASE_			)
              torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

          UpperCamelCase	:List[str]     							=       None
          for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
              UpperCamelCase							,       UpperCamelCase	:List[Any]     							=       cpu_offload_with_hook(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    prev_module_hook=SCREAMING_SNAKE_CASE_			)

          if self.safety_checker is not None:
              UpperCamelCase							,       UpperCamelCase	:Union[str, Any]     							=       cpu_offload_with_hook(self.safety_checker   ,    SCREAMING_SNAKE_CASE_   ,    prev_module_hook=SCREAMING_SNAKE_CASE_			)

          # We'll offload the last model manually.
          UpperCamelCase	:Optional[int]     							=       hook
      @property
      def    UpperCAmelCase						(    self			)    ->				Optional[int]:
          if self.device != torch.device('''meta'''			) or not hasattr(self.text_encoder   ,    '''_hf_hook'''			):
              return self.device
          for module in self.text_encoder.modules():
              if (
                  hasattr(SCREAMING_SNAKE_CASE_   ,    '''_hf_hook'''			)
                  and hasattr(module._hf_hook   ,    '''execution_device'''			)
                  and module._hf_hook.execution_device is not None
              ):
                  return torch.device(module._hf_hook.execution_device			)
          return self.device
      def    UpperCAmelCase						(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    )    ->				Tuple:
          len(SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			) else 1

          # YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
          UpperCamelCase	:List[str]     							=       0
          # get prompt text embeddings
          UpperCamelCase	:Tuple     							=       self.tokenizer(
              SCREAMING_SNAKE_CASE_   ,    padding='''max_length'''   ,    max_length=self.tokenizer.model_max_length   ,    truncation=SCREAMING_SNAKE_CASE_   ,    return_tensors='''pt'''   ,    )
          UpperCamelCase	:Union[str, Any]     							=       text_inputs.input_ids
          UpperCamelCase	:Union[str, Any]     							=       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	:Union[str, Any]     							=       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}'''			)

          UpperCamelCase	:int     							=       self.text_encoder(text_input_ids.to(SCREAMING_SNAKE_CASE_			)			)
          UpperCamelCase	:Union[str, Any]     							=       text_encoder_output.text_embeds

          UpperCamelCase	:Union[str, Any]     							=       prompt_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_   ,    dim=0			)
          # in Shap-E it normalize the prompt_embeds and then later rescale it
          UpperCamelCase	:Dict     							=       prompt_embeds / torch.linalg.norm(SCREAMING_SNAKE_CASE_   ,    dim=-1   ,    keepdim=SCREAMING_SNAKE_CASE_			)

          if do_classifier_free_guidance:
              UpperCamelCase	:List[Any]     							=       torch.zeros_like(SCREAMING_SNAKE_CASE_			)

              # 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([negative_prompt_embeds, prompt_embeds]			)

          # Rescale the features to have unit variance
          UpperCamelCase	:List[str]     							=       math.sqrt(prompt_embeds.shape[1]			) * prompt_embeds

          return prompt_embeds




      @torch.no_grad()
      @replace_example_docstring(SCREAMING_SNAKE_CASE_			)
      def __call__(    self   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_ = 1   ,    SCREAMING_SNAKE_CASE_ = 25   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = None   ,    SCREAMING_SNAKE_CASE_ = 4.0   ,    SCREAMING_SNAKE_CASE_ = 64   ,    SCREAMING_SNAKE_CASE_ = "pil"   ,    SCREAMING_SNAKE_CASE_ = True   ,    )    ->				Optional[int]:

          if isinstance(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			):
              UpperCamelCase	:Dict     							=       1
          elif isinstance(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			):
              UpperCamelCase	:int     							=       len(SCREAMING_SNAKE_CASE_			)
          else:
              raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_			)}'''			)

          UpperCamelCase	:int     							=       self._execution_device

          UpperCamelCase	:Any     							=       batch_size * num_images_per_prompt

          UpperCamelCase	:List[str]     							=       guidance_scale > 1.0
          UpperCamelCase	:Optional[int]     							=       self._encode_prompt(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

          # prior

          self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_   ,    device=SCREAMING_SNAKE_CASE_			)
          UpperCamelCase	:int     							=       self.scheduler.timesteps

          UpperCamelCase	:int     							=       self.prior.config.num_embeddings
          UpperCamelCase	:Optional[int]     							=       self.prior.config.embedding_dim

          UpperCamelCase	:Optional[int]     							=       self.prepare_latents(
              (batch_size, num_embeddings * embedding_dim)   ,    prompt_embeds.dtype   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    self.scheduler   ,    )

          # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
          UpperCamelCase	:List[str]     							=       latents.reshape(latents.shape[0]   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_			)

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

              UpperCamelCase	:Tuple     							=       self.prior(
                  SCREAMING_SNAKE_CASE_   ,    timestep=SCREAMING_SNAKE_CASE_   ,    proj_embedding=SCREAMING_SNAKE_CASE_   ,    ).predicted_image_embedding

              # remove the variance
              UpperCamelCase							,       UpperCamelCase	:Any     							=       noise_pred.split(
                  scaled_model_input.shape[2]   ,    dim=2			)  # batch_size, num_embeddings, embedding_dim

              if do_classifier_free_guidance is not None:
                  UpperCamelCase							,       UpperCamelCase	:Optional[int]     							=       noise_pred.chunk(2			)
                  UpperCamelCase	:Union[str, Any]     							=       noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

              UpperCamelCase	:List[Any]     							=       self.scheduler.step(
                  SCREAMING_SNAKE_CASE_   ,    timestep=SCREAMING_SNAKE_CASE_   ,    sample=SCREAMING_SNAKE_CASE_   ,    ).prev_sample

          if output_type == "latent":
              return ShapEPipelineOutput(images=SCREAMING_SNAKE_CASE_			)

          UpperCamelCase	:int     							=       []
          for i, latent in enumerate(SCREAMING_SNAKE_CASE_			):
              UpperCamelCase	:int     							=       self.renderer.decode(
                  latent[None, :]   ,    SCREAMING_SNAKE_CASE_   ,    size=SCREAMING_SNAKE_CASE_   ,    ray_batch_size=4096   ,    n_coarse_samples=64   ,    n_fine_samples=128   ,    )
              images.append(SCREAMING_SNAKE_CASE_			)

          UpperCamelCase	:int     							=       torch.stack(SCREAMING_SNAKE_CASE_			)

          if output_type not in ["np", "pil"]:
              raise ValueError(F'''Only the output types `pil` and `np` are supported not output_type={output_type}'''			)

          UpperCamelCase	:Optional[Any]     							=       images.cpu().numpy()

          if output_type == "pil":
              UpperCamelCase	:Any     							=       [self.numpy_to_pil(SCREAMING_SNAKE_CASE_			) for image in images]

          # Offload last model to CPU
          if hasattr(self   ,    '''final_offload_hook'''			) and self.final_offload_hook is not None:
              self.final_offload_hook.offload()

          if not return_dict:
              return (images,)

          return ShapEPipelineOutput(images=SCREAMING_SNAKE_CASE_			)













def   _A       (    SCREAMING_SNAKE_CASE__  :     int = 998001	):

    # fetches the next number
    for number in range(n - 1					,  9999					,  -1	):
        UpperCamelCase	:Union[str, Any]     							=       str(SCREAMING_SNAKE_CASE__	)

        # checks whether 'str_number' is a palindrome.
        if str_number == str_number[::-1]:
            UpperCamelCase	:Dict     							=       999

            # if 'number' is a product of two 3-digit numbers
            # then number is the answer otherwise fetch next number.
            while divisor != 99:
                if (number % divisor == 0) and (len(str(number // divisor	)	) == 3.0):
                    return number
                divisor -= 1
    raise ValueError('''That number is larger than our acceptable range.'''	)


if __name__ == "__main__":
 print(f'''{solution() = }''')





