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
  
  
  A_:      int        =			logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  A_:      Optional[Any]        =			'\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 _lowercase					(       _UpperCAmelCase						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							lowerCAmelCase__       =					42
  
  
  
  
  
  
  class _lowercase					(       _UpperCAmelCase						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							def __init__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														super().__init__()
  
  														self.register_modules(
  														    prior=UpperCAmelCase     ,   text_encoder=UpperCAmelCase     ,   tokenizer=UpperCAmelCase     ,   scheduler=UpperCAmelCase     ,   renderer=UpperCAmelCase     ,   )
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if latents is None:
  																					_lowercase				=	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				=	latents.to(UpperCAmelCase  )
  
  														_lowercase				=	latents * scheduler.init_noise_sigma
  														return latents
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase=0  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if is_accelerate_available():
  																					from accelerate import cpu_offload
  														else:
  																					raise ImportError("""Please install accelerate via `pip install accelerate`"""  )
  
  														_lowercase				=	torch.device(F'''cuda:{gpu_id}'''  )
  
  														_lowercase				=	[self.text_encoder, self.prior]
  														for cpu_offloaded_model in models:
  																					if cpu_offloaded_model is not None:
  																												cpu_offload(UpperCAmelCase     ,   UpperCAmelCase  )
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase=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				=	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				=	None
  														for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
  																					_lowercase				,       _lowercase				=	cpu_offload_with_hook(UpperCAmelCase     ,   UpperCAmelCase     ,   prev_module_hook=UpperCAmelCase  )
  
  														if self.safety_checker is not None:
  																					_lowercase				,       _lowercase				=	cpu_offload_with_hook(self.safety_checker     ,   UpperCAmelCase     ,   prev_module_hook=UpperCAmelCase  )
  
  														# We'll offload the last model manually.
  														_lowercase				=	hook
  
  
  
  							@property
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''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     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   ):
  
  
  
  
  
  
  
  														'''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				=	0
  														# get prompt text embeddings
  														_lowercase				=	self.tokenizer(
  														    UpperCAmelCase     ,   padding="""max_length"""     ,   max_length=self.tokenizer.model_max_length     ,   truncation=UpperCAmelCase     ,   return_tensors="""pt"""     ,   )
  														_lowercase				=	text_inputs.input_ids
  														_lowercase				=	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				=	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				=	self.text_encoder(text_input_ids.to(UpperCAmelCase  )  )
  														_lowercase				=	text_encoder_output.text_embeds
  
  														_lowercase				=	prompt_embeds.repeat_interleave(UpperCAmelCase     ,   dim=0  )
  														# in Shap-E it normalize the prompt_embeds and then later rescale it
  														_lowercase				=	prompt_embeds / torch.linalg.norm(UpperCAmelCase     ,   dim=-1     ,   keepdim=UpperCAmelCase  )
  
  														if do_classifier_free_guidance:
  																					_lowercase				=	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				=	torch.cat([negative_prompt_embeds, prompt_embeds]  )
  
  														# Rescale the features to have unit variance
  														_lowercase				=	math.sqrt(prompt_embeds.shape[1]  ) * prompt_embeds
  
  														return prompt_embeds
  
  
  
  							@torch.no_grad()
  							@replace_example_docstring(UpperCAmelCase  )
  							def __call__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase = 1     ,   UpperCAmelCase = 25     ,   UpperCAmelCase = None     ,   UpperCAmelCase = None     ,   UpperCAmelCase = 4.0     ,   UpperCAmelCase = 64     ,   UpperCAmelCase = "pil"     ,   UpperCAmelCase = True     ,   ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if isinstance(UpperCAmelCase     ,   UpperCAmelCase  ):
  																					_lowercase				=	1
  														elif isinstance(UpperCAmelCase     ,   UpperCAmelCase  ):
  																					_lowercase				=	len(UpperCAmelCase  )
  														else:
  																					raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(UpperCAmelCase  )}'''  )
  
  														_lowercase				=	self._execution_device
  
  														_lowercase				=	batch_size * num_images_per_prompt
  
  														_lowercase				=	guidance_scale > 1.0
  														_lowercase				=	self._encode_prompt(UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase  )
  
  														# prior
  
  														self.scheduler.set_timesteps(UpperCAmelCase     ,   device=UpperCAmelCase  )
  														_lowercase				=	self.scheduler.timesteps
  
  														_lowercase				=	self.prior.config.num_embeddings
  														_lowercase				=	self.prior.config.embedding_dim
  
  														_lowercase				=	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				=	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				=	torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents
  																					_lowercase				=	self.scheduler.scale_model_input(UpperCAmelCase     ,   UpperCAmelCase  )
  
  																					_lowercase				=	self.prior(
  																					    UpperCAmelCase     ,   timestep=UpperCAmelCase     ,   proj_embedding=UpperCAmelCase     ,   ).predicted_image_embedding
  
  																					# remove the variance
  																					_lowercase				,       _lowercase				=	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				=	noise_pred.chunk(2  )
  																												_lowercase				=	noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
  
  																					_lowercase				=	self.scheduler.step(
  																					    UpperCAmelCase     ,   timestep=UpperCAmelCase     ,   sample=UpperCAmelCase     ,   ).prev_sample
  
  														if output_type == "latent":
  																					return ShapEPipelineOutput(images=UpperCAmelCase  )
  
  														_lowercase				=	[]
  														for i, latent in enumerate(UpperCAmelCase  ):
  																					_lowercase				=	self.renderer.decode(
  																					    latent[None, :]     ,   UpperCAmelCase     ,   size=UpperCAmelCase     ,   ray_batch_size=4096     ,   n_coarse_samples=64     ,   n_fine_samples=128     ,   )
  																					images.append(UpperCAmelCase  )
  
  														_lowercase				=	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				=	images.cpu().numpy()
  
  														if output_type == "pil":
  																					_lowercase				=	[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: from ctypes import c_float, sizeof
  from enum import Enum
  from typing import TYPE_CHECKING, Optional, Union
  
  
  if TYPE_CHECKING:
  				from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
  
  
  class _lowercase					(       _UpperCAmelCase						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							lowerCAmelCase__       =					c_float
  
  
  
  							@property
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return sizeof(self.value  )
  
  
  
  
  
  
  def 					__lowerCAmelCase			(  _A ,_A ,_A = 0					):
  
  
  
  							"""simple docstring"""
  
  
  							if dimension <= 0:
  														_lowercase				=	fixed_dimension
  
  							dimension -= num_token_to_add
  							return dimension
  
  
  def 					__lowerCAmelCase			(  _A ,_A					):
  
  
  
  							"""simple docstring"""
  
  
  							return num_parameters * dtype.size
  
  
  
  
  
  
  
  def 					__lowerCAmelCase			(  _A					):
  
  
  
  							"""simple docstring"""
  
  
  							from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
  
  							try:
  														return AutoProcessor.from_pretrained(_A					)
  							except (ValueError, OSError, KeyError):
  														_lowercase				,       _lowercase				=	None, None
  														try:
  																					_lowercase				=	AutoTokenizer.from_pretrained(_A					)
  														except (OSError, KeyError):
  																					pass
  														try:
  																					_lowercase				=	AutoFeatureExtractor.from_pretrained(_A					)
  														except (OSError, KeyError):
  																					pass
  
  														if tokenizer is not None and feature_extractor is not None:
  																					raise ValueError(
  																					    f'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''					)
  														elif tokenizer is None and feature_extractor is None:
  																					return None
  														elif tokenizer is not None:
  																					return tokenizer
  														else:
  																					return feature_extractor
  
  
  