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_       :				Optional[int]		   =      logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  UpperCAmelCase_       :				Tuple		   =      '''
      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     lowerCamelCase_			(  _lowercase  ):
  	_lowercase		:      Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]]
  
  
  
  
  
  
  class     lowerCamelCase_			(  _lowercase  ):
  
  
  
  
  	def __init__(    self	:    Dict				,			__A	:    PriorTransformer				,			__A	:    CLIPTextModelWithProjection				,			__A	:    CLIPTokenizer				,			__A	:    HeunDiscreteScheduler				,			__A	:    ShapERenderer				,			):
  		super().__init__()
  
  		self.register_modules(
  		    prior=__A				,			text_encoder=__A				,			tokenizer=__A				,			scheduler=__A				,			renderer=__A				,			)
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    List[str]				,			__A	:    List[str]				,			__A	:    str				,			__A	:    Any				,			__A	:    List[str]				,			__A	:    str				,			__A	:    Any       ):
  		if latents is None:
  			__A   : str								= 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   : Dict								= latents.to(__A       )
  
  		__A   : Tuple								= latents * scheduler.init_noise_sigma
  		return latents
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    str				,			__A	:    Optional[Any]=0       ):
  		if is_accelerate_available():
  			from accelerate import cpu_offload
  		else:
  			raise ImportError("""Please install accelerate via `pip install accelerate`"""       )
  
  		__A   : List[Any]								= torch.device(F"""cuda:{gpu_id}"""       )
  
  		__A   : Optional[int]								= [self.text_encoder, self.prior]
  		for cpu_offloaded_model in models:
  			if cpu_offloaded_model is not None:
  				cpu_offload(__A				,			__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Tuple				,			__A	:    int=0       ):
  		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   : int								= 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   : Optional[int]								= None
  		for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
  			__A   : Tuple								= cpu_offload_with_hook(__A				,			__A				,			prev_module_hook=__A       )
  
  		if self.safety_checker is not None:
  			__A   : Union[str, Any]								= cpu_offload_with_hook(self.safety_checker				,			__A				,			prev_module_hook=__A       )
  
  		# We'll offload the last model manually.
  		__A   : List[Any]								= hook
  
  
  
  
  	@property
  	def 					lowerCAmelCase_					(    self	:    Union[str, Any]       ):
  		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 					lowerCAmelCase_					(    self	:    Optional[int]				,			__A	:    str				,			__A	:    Any				,			__A	:    Optional[Any]				,			__A	:    Optional[int]				,			):
  		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   : List[str]								= 0
  		# get prompt text embeddings
  		__A   : Optional[Any]								= self.tokenizer(
  		    __A				,			padding="""max_length"""				,			max_length=self.tokenizer.model_max_length				,			truncation=__A				,			return_tensors="""pt"""				,			)
  		__A   : str								= text_inputs.input_ids
  		__A   : Optional[Any]								= 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   : Tuple								= 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   : Dict								= self.text_encoder(text_input_ids.to(__A       )       )
  		__A   : Any								= text_encoder_output.text_embeds
  
  		__A   : Tuple								= prompt_embeds.repeat_interleave(__A				,			dim=0       )
  		# in Shap-E it normalize the prompt_embeds and then later rescale it
  		__A   : str								= prompt_embeds / torch.linalg.norm(__A				,			dim=-1				,			keepdim=__A       )
  
  		if do_classifier_free_guidance:
  			__A   : Dict								= 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   : List[str]								= torch.cat([negative_prompt_embeds, prompt_embeds]       )
  
  		# Rescale the features to have unit variance
  		__A   : Tuple								= math.sqrt(prompt_embeds.shape[1]       ) * prompt_embeds
  
  		return prompt_embeds
  
  
  
  
  	@torch.no_grad()
  	@replace_example_docstring(__A       )
  	def __call__(    self	:    Tuple				,			__A	:    str				,			__A	:    int = 1				,			__A	:    int = 25				,			__A	:    Optional[Union[torch.Generator, List[torch.Generator]]] = None				,			__A	:    Optional[torch.FloatTensor] = None				,			__A	:    float = 4.0				,			__A	:    int = 64				,			__A	:    Optional[str] = "pil"				,			__A	:    bool = True				,			):
  
  		if isinstance(__A				,			__A       ):
  			__A   : Any								= 1
  		elif isinstance(__A				,			__A       ):
  			__A   : str								= len(__A       )
  		else:
  			raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(__A       )}"""       )
  
  		__A   : Dict								= self._execution_device
  
  		__A   : Union[str, Any]								= batch_size * num_images_per_prompt
  
  		__A   : Dict								= guidance_scale > 1.0
  		__A   : Any								= self._encode_prompt(__A				,			__A				,			__A				,			__A       )
  
  		# prior
  
  		self.scheduler.set_timesteps(__A				,			device=__A       )
  		__A   : Optional[Any]								= self.scheduler.timesteps
  
  		__A   : Tuple								= self.prior.config.num_embeddings
  		__A   : int								= self.prior.config.embedding_dim
  
  		__A   : Tuple								= 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   : List[str]								= 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   : Optional[Any]								= torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents
  			__A   : Optional[Any]								= self.scheduler.scale_model_input(__A				,			__A       )
  
  			__A   : Any								= self.prior(
  			    __A				,			timestep=__A				,			proj_embedding=__A				,			).predicted_image_embedding
  
  			# remove the variance
  			__A   : 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:
  				__A   : int								= noise_pred.chunk(2       )
  				__A   : Optional[Any]								= noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
  
  			__A   : Any								= self.scheduler.step(
  			    __A				,			timestep=__A				,			sample=__A				,			).prev_sample
  
  		if output_type == "latent":
  			return ShapEPipelineOutput(images=__A       )
  
  		__A   : Optional[Any]								= []
  		for i, latent in enumerate(__A       ):
  			__A   : List[str]								= self.renderer.decode(
  			    latent[None, :]				,			__A				,			size=__A				,			ray_batch_size=4096				,			n_coarse_samples=64				,			n_fine_samples=128				,			)
  			images.append(__A       )
  
  		__A   : str								= 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   : Dict								= images.cpu().numpy()
  
  		if output_type == "pil":
  			__A   : Any								= [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: 
  
  import os
  from typing import List, Union
  
  import tensorflow as tf
  from tensorflow_text import BertTokenizer as BertTokenizerLayer
  from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs
  
  from .tokenization_bert import BertTokenizer
  
  
  
  
  
  
  class     lowerCamelCase_			(  tf.keras.layers.Layer  ):
  
  
  
  
  	def __init__(    self	:    int				,			__A	:    List				,			__A	:    bool				,			__A	:    int = None				,			__A	:    int = None				,			__A	:    int = None				,			__A	:    str = "longest"				,			__A	:    bool = True				,			__A	:    int = 512				,			__A	:    int = None				,			__A	:    bool = True				,			__A	:    bool = True				,			__A	:    bool = True				,			**__A	:    Any				,			):
  		super().__init__()
  		if use_fast_bert_tokenizer:
  			__A   : Optional[int]								= FastBertTokenizer(
  			    __A				,			token_out_type=tf.intaa				,			lower_case_nfd_strip_accents=__A				,			**__A       )
  		else:
  			__A   : Optional[Any]								= tf.lookup.StaticVocabularyTable(
  			    tf.lookup.KeyValueTensorInitializer(
  			        keys=__A				,			key_dtype=tf.string				,			values=tf.range(tf.size(__A				,			out_type=tf.intaa       )				,			dtype=tf.intaa       )				,			value_dtype=tf.intaa				,			)				,			num_oov_buckets=1				,			)
  			__A   : Any								= BertTokenizerLayer(
  			    __A				,			token_out_type=tf.intaa				,			lower_case=__A				,			**__A       )
  
  		__A   : Union[str, Any]								= vocab_list
  		__A   : List[str]								= do_lower_case
  		__A   : List[Any]								= cls_token_id or vocab_list.index("""[CLS]"""       )
  		__A   : Tuple								= sep_token_id or vocab_list.index("""[SEP]"""       )
  		__A   : str								= pad_token_id or vocab_list.index("""[PAD]"""       )
  		__A   : Optional[int]								= ShrinkLongestTrimmer(max_length - 3				,			axis=1       )  # Allow room for special tokens
  		__A   : Union[str, Any]								= max_length
  		__A   : List[Any]								= padding
  		__A   : Optional[int]								= truncation
  		__A   : Tuple								= pad_to_multiple_of
  		__A   : Optional[Any]								= return_token_type_ids
  		__A   : int								= return_attention_mask
  
  
  
  
  	@classmethod
  	def 					lowerCAmelCase_					(    cls	:    List[Any]				,			__A	:    "PreTrainedTokenizerBase"				,			**__A	:    List[str]       ):  # noqa: F821
  		__A   : Any								= kwargs.pop("""do_lower_case"""				,			__A       )
  		__A   : Dict								= tokenizer.do_lower_case if do_lower_case is None else do_lower_case
  		__A   : List[str]								= kwargs.pop("""cls_token_id"""				,			__A       )
  		__A   : str								= tokenizer.cls_token_id if cls_token_id is None else cls_token_id
  		__A   : str								= kwargs.pop("""sep_token_id"""				,			__A       )
  		__A   : Dict								= tokenizer.sep_token_id if sep_token_id is None else sep_token_id
  		__A   : Tuple								= kwargs.pop("""pad_token_id"""				,			__A       )
  		__A   : List[Any]								= tokenizer.pad_token_id if pad_token_id is None else pad_token_id
  
  		__A   : Union[str, Any]								= tokenizer.get_vocab()
  		__A   : Any								= sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]				,			key=lambda __A       : x[1]       )
  		__A   : int								= [entry[0] for entry in vocab]
  		return cls(
  		    vocab_list=__A				,			do_lower_case=__A				,			cls_token_id=__A				,			sep_token_id=__A				,			pad_token_id=__A				,			**__A				,			)
  
  
  
  
  	@classmethod
  	def 					lowerCAmelCase_					(    cls	:    List[str]				,			__A	:    Union[str, os.PathLike]				,			*__A	:    Tuple				,			**__A	:    Optional[int]       ):
  		try:
  			__A   : int								= BertTokenizer.from_pretrained(__A				,			*__A				,			**__A       )
  		except:  # noqa: E722
  			from .tokenization_bert_fast import BertTokenizerFast
  
  			__A   : Optional[int]								= BertTokenizerFast.from_pretrained(__A				,			*__A				,			**__A       )
  		return cls.from_tokenizer(__A				,			**__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]				,			__A	:    Optional[Any]       ):
  		if self.do_lower_case:
  			__A   : Tuple								= case_fold_utfa(__A       )
  		__A   : Optional[int]								= self.tf_tokenizer.tokenize(__A       )
  		return tokens.merge_dims(1				,			-1       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Any				,			__A	:    Optional[Any]				,			__A	:    Dict=None				,			__A	:    List[Any]=None				,			__A	:    str=None				,			__A	:    Union[str, Any]=None				,			__A	:    Union[str, Any]=None				,			__A	:    int=None				,			__A	:    Any=None				,			):
  		if padding is None:
  			__A   : List[Any]								= self.padding
  		if padding not in ("longest", "max_length"):
  			raise ValueError("""Padding must be either 'longest' or 'max_length'!"""       )
  		if max_length is not None and text_pair is not None:
  			# Because we have to instantiate a Trimmer to do it properly
  			raise ValueError("""max_length cannot be overridden at call time when truncating paired texts!"""       )
  		if max_length is None:
  			__A   : Any								= self.max_length
  		if truncation is None:
  			__A   : int								= self.truncation
  		if pad_to_multiple_of is None:
  			__A   : List[str]								= self.pad_to_multiple_of
  		if return_token_type_ids is None:
  			__A   : int								= self.return_token_type_ids
  		if return_attention_mask is None:
  			__A   : Any								= self.return_attention_mask
  		if not isinstance(__A				,			tf.Tensor       ):
  			__A   : str								= tf.convert_to_tensor(__A       )
  		if text_pair is not None and not isinstance(__A				,			tf.Tensor       ):
  			__A   : Optional[Any]								= tf.convert_to_tensor(__A       )
  		if text_pair is not None:
  			if text.shape.rank > 1:
  				raise ValueError("""text argument should not be multidimensional when a text pair is supplied!"""       )
  			if text_pair.shape.rank > 1:
  				raise ValueError("""text_pair should not be multidimensional!"""       )
  		if text.shape.rank == 2:
  			__A						,							__A   : Optional[int]								= text[:, 0], text[:, 1]
  		__A   : List[Any]								= self.unpaired_tokenize(__A       )
  		if text_pair is None:  # Unpaired text
  			if truncation:
  				__A   : Union[str, Any]								= text[:, : max_length - 2]  # Allow room for special tokens
  			__A						,							__A   : List[Any]								= combine_segments(
  			    (text,)				,			start_of_sequence_id=self.cls_token_id				,			end_of_segment_id=self.sep_token_id       )
  		else:  # Paired text
  			__A   : Tuple								= self.unpaired_tokenize(__A       )
  			if truncation:
  				__A						,							__A   : Optional[int]								= self.paired_trimmer.trim([text, text_pair]       )
  			__A						,							__A   : int								= combine_segments(
  			    (text, text_pair)				,			start_of_sequence_id=self.cls_token_id				,			end_of_segment_id=self.sep_token_id       )
  		if padding == "longest":
  			__A   : str								= input_ids.bounding_shape(axis=1       )
  			if pad_to_multiple_of is not None:
  				# No ceiling division in tensorflow, so we negate floordiv instead
  				__A   : Optional[Any]								= pad_to_multiple_of * (-tf.math.floordiv(-pad_length				,			__A       ))
  		else:
  			__A   : Dict								= max_length
  
  		__A						,							__A   : str								= pad_model_inputs(__A				,			max_seq_length=__A				,			pad_value=self.pad_token_id       )
  		__A   : List[str]								= {"""input_ids""": input_ids}
  		if return_attention_mask:
  			__A   : Any								= attention_mask
  		if return_token_type_ids:
  			__A						,							__A   : Tuple								= pad_model_inputs(
  			    __A				,			max_seq_length=__A				,			pad_value=self.pad_token_id       )
  			__A   : List[str]								= token_type_ids
  		return output
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]       ):
  		return {
  		    "vocab_list": self.vocab_list,
  		    "do_lower_case": self.do_lower_case,
  		    "cls_token_id": self.cls_token_id,
  		    "sep_token_id": self.sep_token_id,
  		    "pad_token_id": self.pad_token_id,
  		}
  
  
  
  
  
  