code: 
  
  
  
  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
  
  
  __lowerCamelCase  				=					logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  __lowerCamelCase  				=					'\n    Examples:\n        ```py\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif\n\n        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n\n        >>> repo = "openai/shap-e"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 15.0\n        >>> prompt = "a shark"\n\n        >>> images = pipe(\n        ...     prompt,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], "shark_3d.gif")\n        ```\n'
  
  
  
  @dataclass
  class 		_snake_case      (			_UpperCAmelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			4_2
  
  
  
  
  
  
  
  class 		_snake_case      (			_UpperCAmelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   def __init__(						self	,    a	,    a	,    a	,    a	,    a	,    )	->							List[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     super().__init__()
  
     self.register_modules(
         prior=a	,    text_encoder=a	,    tokenizer=a	,    scheduler=a	,    renderer=a	,    )
   def lowercase_		(						self	,    a	,    a	,    a	,    a	,    a	,    a					)	->							Optional[int]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if latents is None:
       _A           =    randn_tensor(a	,    generator=a	,    device=a	,    dtype=a					)
     else:
       if latents.shape != shape:
         raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''					)
       _A           =    latents.to(a					)
  
     _A           =    latents * scheduler.init_noise_sigma
     return latents
   def lowercase_		(						self	,    a=0					)	->							Tuple:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if is_accelerate_available():
       from accelerate import cpu_offload
     else:
       raise ImportError('''Please install accelerate via `pip install accelerate`'''					)
  
     _A           =    torch.device(f'''cuda:{gpu_id}'''					)
  
     _A           =    [self.text_encoder, self.prior]
     for cpu_offloaded_model in models:
       if cpu_offloaded_model is not None:
         cpu_offload(a	,    a					)
   def lowercase_		(						self	,    a=0					)	->							Any:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     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.'''					)
  
     _A           =    torch.device(f'''cuda:{gpu_id}'''					)
  
     if self.device.type != "cpu":
       self.to('''cpu'''	,    silence_dtype_warnings=a					)
       torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
  
     _A           =    None
     for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
       _A    ,   _A           =    cpu_offload_with_hook(a	,    a	,    prev_module_hook=a					)
  
     if self.safety_checker is not None:
       _A    ,   _A           =    cpu_offload_with_hook(self.safety_checker	,    a	,    prev_module_hook=a					)
  
     # We'll offload the last model manually.
     _A           =    hook
   @property
   def lowercase_		(						self					)	->							List[str]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     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(a	,    '''_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 lowercase_		(						self	,    a	,    a	,    a	,    a	,    )	->							Any:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     len(a					) if isinstance(a	,    a					) else 1
  
     # YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
     _A           =    0
     # get prompt text embeddings
     _A           =    self.tokenizer(
         a	,    padding='''max_length'''	,    max_length=self.tokenizer.model_max_length	,    truncation=a	,    return_tensors='''pt'''	,    )
     _A           =    text_inputs.input_ids
     _A           =    self.tokenizer(a	,    padding='''longest'''	,    return_tensors='''pt'''					).input_ids
  
     if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(a	,    a					):
       _A           =    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}'''					)
  
     _A           =    self.text_encoder(text_input_ids.to(a					)					)
     _A           =    text_encoder_output.text_embeds
  
     _A           =    prompt_embeds.repeat_interleave(a	,    dim=0					)
     # in Shap-E it normalize the prompt_embeds and then later rescale it
     _A           =    prompt_embeds / torch.linalg.norm(a	,    dim=-1	,    keepdim=a					)
  
     if do_classifier_free_guidance:
       _A           =    torch.zeros_like(a					)
  
       # 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           =    torch.cat([negative_prompt_embeds, prompt_embeds]					)
  
     # Rescale the features to have unit variance
     _A           =    math.sqrt(prompt_embeds.shape[1]					) * prompt_embeds
  
     return prompt_embeds
  
  
  
   @torch.no_grad()
   @replace_example_docstring(a					)
   def __call__(						self	,    a	,    a = 1	,    a = 2_5	,    a = None	,    a = None	,    a = 4.0	,    a = 6_4	,    a = "pil"	,    a = True	,    )	->							str:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if isinstance(a	,    a					):
       _A           =    1
     elif isinstance(a	,    a					):
       _A           =    len(a					)
     else:
       raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(a					)}'''					)
  
     _A           =    self._execution_device
  
     _A           =    batch_size * num_images_per_prompt
  
     _A           =    guidance_scale > 1.0
     _A           =    self._encode_prompt(a	,    a	,    a	,    a					)
  
     # prior
  
     self.scheduler.set_timesteps(a	,    device=a					)
     _A           =    self.scheduler.timesteps
  
     _A           =    self.prior.config.num_embeddings
     _A           =    self.prior.config.embedding_dim
  
     _A           =    self.prepare_latents(
         (batch_size, num_embeddings * embedding_dim)	,    prompt_embeds.dtype	,    a	,    a	,    a	,    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
     _A           =    latents.reshape(latents.shape[0]	,    a	,    a					)
  
     for i, t in enumerate(self.progress_bar(a					)					):
       # expand the latents if we are doing classifier free guidance
       _A           =    torch.cat([latents] * 2					) if do_classifier_free_guidance else latents
       _A           =    self.scheduler.scale_model_input(a	,    a					)
  
       _A           =    self.prior(
           a	,    timestep=a	,    proj_embedding=a	,    ).predicted_image_embedding
  
       # remove the variance
       _A    ,   _A           =    noise_pred.split(
           scaled_model_input.shape[2]	,    dim=2					)  # batch_size, num_embeddings, embedding_dim
  
       if do_classifier_free_guidance is not None:
         _A    ,   _A           =    noise_pred.chunk(2					)
         _A           =    noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
  
       _A           =    self.scheduler.step(
           a	,    timestep=a	,    sample=a	,    ).prev_sample
  
     if output_type == "latent":
       return ShapEPipelineOutput(images=a					)
  
     _A           =    []
     for i, latent in enumerate(a					):
       _A           =    self.renderer.decode(
           latent[None, :]	,    a	,    size=a	,    ray_batch_size=4_0_9_6	,    n_coarse_samples=6_4	,    n_fine_samples=1_2_8	,    )
       images.append(a					)
  
     _A           =    torch.stack(a					)
  
     if output_type not in ["np", "pil"]:
       raise ValueError(f'''Only the output types `pil` and `np` are supported not output_type={output_type}'''					)
  
     _A           =    images.cpu().numpy()
  
     if output_type == "pil":
       _A           =    [self.numpy_to_pil(a					) 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=a					)
style_context: 
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case = 1_000							)   -> str:
    if (not isinstance(__snake_case						,				__snake_case							)) or (digit_position <= 0):
      raise ValueError('''Digit position must be a positive integer'''							)
    elif (not isinstance(__snake_case						,				__snake_case							)) or (precision < 0):
      raise ValueError('''Precision must be a nonnegative integer'''							)
  
    # compute an approximation of (16 ** (n - 1)) * pi whose fractional part is mostly
    # accurate
    _A           =    (
        4 * _subsum(__snake_case						,				1						,				__snake_case							)
        - 2 * _subsum(__snake_case						,				4						,				__snake_case							)
        - _subsum(__snake_case						,				5						,				__snake_case							)
        - _subsum(__snake_case						,				6						,				__snake_case							)
    )
  
    # return the first hex digit of the fractional part of the result
    return hex(int((sum_result % 1) * 16							)							)[2:]
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case							)   -> float:
    _A           =    0.0
    for sum_index in range(digit_pos_to_extract + precision							):
      _A           =    8 * sum_index + denominator_addend
      if sum_index < digit_pos_to_extract:
        # if the exponential term is an integer and we mod it by the denominator
        # before dividing, only the integer part of the sum will change;
        # the fractional part will not
        _A           =    pow(
            16						,				digit_pos_to_extract - 1 - sum_index						,				__snake_case							)
      else:
        _A           =    pow(16						,				digit_pos_to_extract - 1 - sum_index							)
      total += exponential_term / denominator
    return total
  
  
  if __name__ == "__main__":
      import doctest
  
      doctest.testmod()