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
  
  
  UpperCamelCase__				       =			logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  UpperCamelCase__				       =			'\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 						UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
       UpperCAmelCase_         =  42
  
  
  
  
  class 						UpperCAmelCase__   (      A_		):
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       str	,      UpperCamelCase       :       PriorTransformer	,      UpperCamelCase       :       CLIPTextModelWithProjection	,      UpperCamelCase       :       CLIPTokenizer	,      UpperCamelCase       :       HeunDiscreteScheduler	,      UpperCamelCase       :       ShapERenderer	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              super().__init__()
  
              self.register_modules(
                  prior=UpperCamelCase	,      text_encoder=UpperCamelCase	,      tokenizer=UpperCamelCase	,      scheduler=UpperCamelCase	,      renderer=UpperCamelCase	,      )
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Union[str, Any]	,      UpperCamelCase       :       Optional[Any]	,      UpperCamelCase       :       str	,      UpperCamelCase       :       Optional[Any]	,      UpperCamelCase       :       Union[str, Any]	,      UpperCamelCase       :       Any	,      UpperCamelCase       :       Dict		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              if latents is None:
                     _lowercase					:				int           =			randn_tensor(UpperCamelCase	,      generator=UpperCamelCase	,      device=UpperCamelCase	,      dtype=UpperCamelCase		)
              else:
                     if latents.shape != shape:
                            raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'		)
                     _lowercase					:				Optional[int]           =			latents.to(UpperCamelCase		)
  
              _lowercase					:				str           =			latents * scheduler.init_noise_sigma
              return latents
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]	,      UpperCamelCase       :       List[str]=0		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              if is_accelerate_available():
                     from accelerate import cpu_offload
              else:
                     raise ImportError('''Please install accelerate via `pip install accelerate`'''		)
  
              _lowercase					:				Union[str, Any]           =			torch.device(F'cuda:{gpu_id}'		)
  
              _lowercase					:				Tuple           =			[self.text_encoder, self.prior]
              for cpu_offloaded_model in models:
                     if cpu_offloaded_model is not None:
                            cpu_offload(UpperCamelCase	,      UpperCamelCase		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]	,      UpperCamelCase       :       Tuple=0		):
  
  
  
  
  
              """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.'''		)
  
              _lowercase					:				List[Any]           =			torch.device(F'cuda:{gpu_id}'		)
  
              if self.device.type != "cpu":
                     self.to('''cpu'''	,      silence_dtype_warnings=UpperCamelCase		)
                     torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
  
              _lowercase					:				Any           =			None
              for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
                     _lowercase       ,	_lowercase					:				Tuple           =			cpu_offload_with_hook(UpperCamelCase	,      UpperCamelCase	,      prev_module_hook=UpperCamelCase		)
  
              if self.safety_checker is not None:
                     _lowercase       ,	_lowercase					:				Dict           =			cpu_offload_with_hook(self.safety_checker	,      UpperCamelCase	,      prev_module_hook=UpperCamelCase		)
  
              # We'll offload the last model manually.
              _lowercase					:				str           =			hook
  
  
  
  
       @property
       def   lowerCAmelCase_   ( self       :       Dict		):
  
  
  
  
  
              """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(UpperCamelCase	,      '''_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   lowerCAmelCase_   ( self       :       Union[str, Any]	,      UpperCamelCase       :       Dict	,      UpperCamelCase       :       Union[str, Any]	,      UpperCamelCase       :       Dict	,      UpperCamelCase       :       Tuple	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              len(UpperCamelCase		) if isinstance(UpperCamelCase	,      UpperCamelCase		) else 1
  
              # YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
              _lowercase					:				Dict           =			0
              # get prompt text embeddings
              _lowercase					:				Optional[int]           =			self.tokenizer(
                  UpperCamelCase	,      padding='''max_length'''	,      max_length=self.tokenizer.model_max_length	,      truncation=UpperCamelCase	,      return_tensors='''pt'''	,      )
              _lowercase					:				int           =			text_inputs.input_ids
              _lowercase					:				Union[str, Any]           =			self.tokenizer(UpperCamelCase	,      padding='''longest'''	,      return_tensors='''pt'''		).input_ids
  
              if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(UpperCamelCase	,      UpperCamelCase		):
                     _lowercase					:				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}'		)
  
              _lowercase					:				str           =			self.text_encoder(text_input_ids.to(UpperCamelCase		)		)
              _lowercase					:				str           =			text_encoder_output.text_embeds
  
              _lowercase					:				Dict           =			prompt_embeds.repeat_interleave(UpperCamelCase	,      dim=0		)
              # in Shap-E it normalize the prompt_embeds and then later rescale it
              _lowercase					:				int           =			prompt_embeds / torch.linalg.norm(UpperCamelCase	,      dim=-1	,      keepdim=UpperCamelCase		)
  
              if do_classifier_free_guidance:
                     _lowercase					:				Union[str, Any]           =			torch.zeros_like(UpperCamelCase		)
  
                     # 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
                     _lowercase					:				int           =			torch.cat([negative_prompt_embeds, prompt_embeds]		)
  
              # Rescale the features to have unit variance
              _lowercase					:				List[str]           =			math.sqrt(prompt_embeds.shape[1]		) * prompt_embeds
  
              return prompt_embeds
  
  
  
  
       @torch.no_grad()
       @replace_example_docstring(UpperCamelCase		)
       def __call__( self       :       str	,      UpperCamelCase       :       str	,      UpperCamelCase       :       int = 1	,      UpperCamelCase       :       int = 25	,      UpperCamelCase       :       Optional[Union[torch.Generator, List[torch.Generator]]] = None	,      UpperCamelCase       :       Optional[torch.FloatTensor] = None	,      UpperCamelCase       :       float = 4.0	,      UpperCamelCase       :       int = 64	,      UpperCamelCase       :       Optional[str] = "pil"	,      UpperCamelCase       :       bool = True	,      ):
  
  
  
  
  
              """simple docstring"""
  
  
  
              if isinstance(UpperCamelCase	,      UpperCamelCase		):
                     _lowercase					:				Optional[Any]           =			1
              elif isinstance(UpperCamelCase	,      UpperCamelCase		):
                     _lowercase					:				Any           =			len(UpperCamelCase		)
              else:
                     raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(UpperCamelCase		)}'		)
  
              _lowercase					:				Optional[Any]           =			self._execution_device
  
              _lowercase					:				List[Any]           =			batch_size * num_images_per_prompt
  
              _lowercase					:				List[str]           =			guidance_scale > 1.0
              _lowercase					:				List[str]           =			self._encode_prompt(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase		)
  
              # prior
  
              self.scheduler.set_timesteps(UpperCamelCase	,      device=UpperCamelCase		)
              _lowercase					:				Any           =			self.scheduler.timesteps
  
              _lowercase					:				int           =			self.prior.config.num_embeddings
              _lowercase					:				Any           =			self.prior.config.embedding_dim
  
              _lowercase					:				int           =			self.prepare_latents(
                  (batch_size, num_embeddings * embedding_dim)	,      prompt_embeds.dtype	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      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
              _lowercase					:				Optional[Any]           =			latents.reshape(latents.shape[0]	,      UpperCamelCase	,      UpperCamelCase		)
  
              for i, t in enumerate(self.progress_bar(UpperCamelCase		)		):
                     # expand the latents if we are doing classifier free guidance
                     _lowercase					:				Optional[int]           =			torch.cat([latents] * 2		) if do_classifier_free_guidance else latents
                     _lowercase					:				Any           =			self.scheduler.scale_model_input(UpperCamelCase	,      UpperCamelCase		)
  
                     _lowercase					:				Any           =			self.prior(
                         UpperCamelCase	,      timestep=UpperCamelCase	,      proj_embedding=UpperCamelCase	,      ).predicted_image_embedding
  
                     # remove the variance
                     _lowercase       ,	_lowercase					:				List[str]           =			noise_pred.split(
                         scaled_model_input.shape[2]	,      dim=2		)  # batch_size, num_embeddings, embedding_dim
  
                     if do_classifier_free_guidance is not None:
                            _lowercase       ,	_lowercase					:				Union[str, Any]           =			noise_pred.chunk(2		)
                            _lowercase					:				List[str]           =			noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
  
                     _lowercase					:				Union[str, Any]           =			self.scheduler.step(
                         UpperCamelCase	,      timestep=UpperCamelCase	,      sample=UpperCamelCase	,      ).prev_sample
  
              if output_type == "latent":
                     return ShapEPipelineOutput(images=UpperCamelCase		)
  
              _lowercase					:				Optional[int]           =			[]
              for i, latent in enumerate(UpperCamelCase		):
                     _lowercase					:				str           =			self.renderer.decode(
                         latent[None, :]	,      UpperCamelCase	,      size=UpperCamelCase	,      ray_batch_size=40_96	,      n_coarse_samples=64	,      n_fine_samples=1_28	,      )
                     images.append(UpperCamelCase		)
  
              _lowercase					:				Any           =			torch.stack(UpperCamelCase		)
  
              if output_type not in ["np", "pil"]:
                     raise ValueError(F'Only the output types `pil` and `np` are supported not output_type={output_type}'		)
  
              _lowercase					:				Tuple           =			images.cpu().numpy()
  
              if output_type == "pil":
                     _lowercase					:				List[str]           =			[self.numpy_to_pil(UpperCamelCase		) 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=UpperCamelCase		)
style_context: 
  
  
  import os
  from pathlib import Path
  from typing import Dict, List
  
  import fire
  import torch
  
  from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
  from transformers.utils.logging import get_logger
  
  
  UpperCamelCase__				       =			get_logger(__name__)
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_			)						->							List[str]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         if text.startswith(UpperCAmelCase_			):
                return text[len(UpperCAmelCase_			) :]
         return text  # or whatever
  
  def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Optional[int]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         return {remove_prefix(UpperCAmelCase_    ,     '''model.'''			): v for k, v in sd.items()}
  
  def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Any:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Optional[int]           =			{}
         for k in state_dicts[0].keys():
                _lowercase					:				Tuple           =			[sd[k] for sd in state_dicts]
                _lowercase					:				Union[str, Any]           =			sum(UpperCAmelCase_			) / len(UpperCAmelCase_			)
                assert isinstance(UpperCAmelCase_    ,     torch.Tensor			)
                _lowercase					:				Union[str, Any]           =			new_t
         return new_sd
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)						->							None:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				List[str]           =			AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase_			)
         if os.path.isfile(UpperCAmelCase_			):
                _lowercase					:				Optional[Any]           =			[pl_ckpt_path]
         else:
                assert os.path.isdir(UpperCAmelCase_			)
                _lowercase					:				List[str]           =			list(Path(UpperCAmelCase_			).glob('''*.ckpt'''			)			)
                assert ckpt_files, F'could not find any ckpt files inside the {pl_ckpt_path} directory'
  
         if len(UpperCAmelCase_			) > 1:
                logger.info(F'averaging the weights of {ckpt_files}'			)
  
         _lowercase					:				List[Any]           =			[sanitize(torch.load(UpperCAmelCase_    ,     map_location='''cpu'''			)['''state_dict''']			) for x in ckpt_files]
         _lowercase					:				Optional[Any]           =			average_state_dicts(UpperCAmelCase_			)
  
         _lowercase       ,	_lowercase					:				str           =			hf_model.load_state_dict(UpperCAmelCase_    ,     strict=UpperCAmelCase_			)
         assert not missing, F'missing keys: {missing}'
         hf_model.save_pretrained(UpperCAmelCase_			)
         try:
                _lowercase					:				Union[str, Any]           =			AutoTokenizer.from_pretrained(UpperCAmelCase_			)
                tok.save_pretrained(UpperCAmelCase_			)
         except Exception:
                pass
                # dont copy tokenizer if cant
  
  
  if __name__ == "__main__":
     fire.Fire(convert_pl_to_hf)