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   :     Any      =			logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  __A   :     List[str]      =			'''
      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    __A				(							lowerCAmelCase ):
      lowerCAmelCase_							:		Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]]
  
  
  
  
  
  
  class    __A				(							lowerCAmelCase ):
  
  
  
  
  
  
      def __init__(	self			:    int    ,				UpperCAmelCase_			:    PriorTransformer    ,				UpperCAmelCase_			:    CLIPTextModelWithProjection    ,				UpperCAmelCase_			:    CLIPTokenizer    ,				UpperCAmelCase_			:    HeunDiscreteScheduler    ,				UpperCAmelCase_			:    ShapERenderer    ,				):
          super().__init__()
  
          self.register_modules(
              prior=UpperCAmelCase_    ,				text_encoder=UpperCAmelCase_    ,				tokenizer=UpperCAmelCase_    ,				scheduler=UpperCAmelCase_    ,				renderer=UpperCAmelCase_    ,				)
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[Any]    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    Optional[Any]    ,				UpperCAmelCase_			:    str    ,				UpperCAmelCase_			:    str    ,				UpperCAmelCase_			:    List[Any]    ):
          if latents is None:
              lowerCAmelCase				:			Optional[Any]    			=      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}"    )
              lowerCAmelCase				:			int    			=      latents.to(UpperCAmelCase_    )
  
          lowerCAmelCase				:			List[str]    			=      latents * scheduler.init_noise_sigma
          return latents
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[Any]    ,				UpperCAmelCase_			:    str=0    ):
          if is_accelerate_available():
              from accelerate import cpu_offload
          else:
              raise ImportError('Please install accelerate via `pip install accelerate`'    )
  
          lowerCAmelCase				:			str    			=      torch.device(f"cuda:{gpu_id}"    )
  
          lowerCAmelCase				:			List[Any]    			=      [self.text_encoder, self.prior]
          for cpu_offloaded_model in models:
              if cpu_offloaded_model is not None:
                  cpu_offload(UpperCAmelCase_    ,				UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Union[str, Any]    ,				UpperCAmelCase_			:    str=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.'    )
  
          lowerCAmelCase				:			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)
  
          lowerCAmelCase				:			List[Any]    			=      None
          for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
              lowerCAmelCase			,  lowerCAmelCase				:			Dict    			=      cpu_offload_with_hook(UpperCAmelCase_    ,				UpperCAmelCase_    ,				prev_module_hook=UpperCAmelCase_    )
  
          if self.safety_checker is not None:
              lowerCAmelCase			,  lowerCAmelCase				:			Dict    			=      cpu_offload_with_hook(self.safety_checker    ,				UpperCAmelCase_    ,				prev_module_hook=UpperCAmelCase_    )
  
          # We'll offload the last model manually.
          lowerCAmelCase				:			Optional[Any]    			=      hook
  
  
  
  
  
  
      @property
      def 	lowercase__ (	self			:    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(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 	lowercase__ (	self			:    Tuple    ,				UpperCAmelCase_			:    Dict    ,				UpperCAmelCase_			:    Optional[int]    ,				UpperCAmelCase_			:    List[Any]    ,				UpperCAmelCase_			:    Any    ,				):
          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
          lowerCAmelCase				:			Dict    			=      0
          # get prompt text embeddings
          lowerCAmelCase				:			List[str]    			=      self.tokenizer(
              UpperCAmelCase_    ,				padding='max_length'    ,				max_length=self.tokenizer.model_max_length    ,				truncation=UpperCAmelCase_    ,				return_tensors='pt'    ,				)
          lowerCAmelCase				:			Union[str, Any]    			=      text_inputs.input_ids
          lowerCAmelCase				:			List[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_    ):
              lowerCAmelCase				:			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}"    )
  
          lowerCAmelCase				:			Any    			=      self.text_encoder(text_input_ids.to(UpperCAmelCase_    )    )
          lowerCAmelCase				:			List[str]    			=      text_encoder_output.text_embeds
  
          lowerCAmelCase				:			Dict    			=      prompt_embeds.repeat_interleave(UpperCAmelCase_    ,				dim=0    )
          # in Shap-E it normalize the prompt_embeds and then later rescale it
          lowerCAmelCase				:			Any    			=      prompt_embeds / torch.linalg.norm(UpperCAmelCase_    ,				dim=-1    ,				keepdim=UpperCAmelCase_    )
  
          if do_classifier_free_guidance:
              lowerCAmelCase				:			Optional[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
              lowerCAmelCase				:			Optional[Any]    			=      torch.cat([negative_prompt_embeds, prompt_embeds]    )
  
          # Rescale the features to have unit variance
          lowerCAmelCase				:			int    			=      math.sqrt(prompt_embeds.shape[1]    ) * prompt_embeds
  
          return prompt_embeds
  
  
  
  
  
  
      @torch.no_grad()
      @replace_example_docstring(UpperCAmelCase_    )
      def __call__(	self			:    Optional[Any]    ,				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    ,				):
  
          if isinstance(UpperCAmelCase_    ,				UpperCAmelCase_    ):
              lowerCAmelCase				:			Tuple    			=      1
          elif isinstance(UpperCAmelCase_    ,				UpperCAmelCase_    ):
              lowerCAmelCase				:			Dict    			=      len(UpperCAmelCase_    )
          else:
              raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(UpperCAmelCase_    )}"    )
  
          lowerCAmelCase				:			int    			=      self._execution_device
  
          lowerCAmelCase				:			Optional[Any]    			=      batch_size * num_images_per_prompt
  
          lowerCAmelCase				:			List[Any]    			=      guidance_scale > 1.0
          lowerCAmelCase				:			Tuple    			=      self._encode_prompt(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    )
  
          # prior
  
          self.scheduler.set_timesteps(UpperCAmelCase_    ,				device=UpperCAmelCase_    )
          lowerCAmelCase				:			Optional[int]    			=      self.scheduler.timesteps
  
          lowerCAmelCase				:			Tuple    			=      self.prior.config.num_embeddings
          lowerCAmelCase				:			Any    			=      self.prior.config.embedding_dim
  
          lowerCAmelCase				:			str    			=      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
          lowerCAmelCase				:			str    			=      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
              lowerCAmelCase				:			Dict    			=      torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents
              lowerCAmelCase				:			str    			=      self.scheduler.scale_model_input(UpperCAmelCase_    ,				UpperCAmelCase_    )
  
              lowerCAmelCase				:			int    			=      self.prior(
                  UpperCAmelCase_    ,				timestep=UpperCAmelCase_    ,				proj_embedding=UpperCAmelCase_    ,				).predicted_image_embedding
  
              # remove the variance
              lowerCAmelCase			,  lowerCAmelCase				:			Tuple    			=      noise_pred.split(
                  scaled_model_input.shape[2]    ,				dim=2    )  # batch_size, num_embeddings, embedding_dim
  
              if do_classifier_free_guidance is not None:
                  lowerCAmelCase			,  lowerCAmelCase				:			Optional[int]    			=      noise_pred.chunk(2    )
                  lowerCAmelCase				:			Dict    			=      noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
  
              lowerCAmelCase				:			int    			=      self.scheduler.step(
                  UpperCAmelCase_    ,				timestep=UpperCAmelCase_    ,				sample=UpperCAmelCase_    ,				).prev_sample
  
          if output_type == "latent":
              return ShapEPipelineOutput(images=UpperCAmelCase_    )
  
          lowerCAmelCase				:			Optional[Any]    			=      []
          for i, latent in enumerate(UpperCAmelCase_    ):
              lowerCAmelCase				:			int    			=      self.renderer.decode(
                  latent[None, :]    ,				UpperCAmelCase_    ,				size=UpperCAmelCase_    ,				ray_batch_size=4096    ,				n_coarse_samples=64    ,				n_fine_samples=128    ,				)
              images.append(UpperCAmelCase_    )
  
          lowerCAmelCase				:			List[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}"    )
  
          lowerCAmelCase				:			Tuple    			=      images.cpu().numpy()
  
          if output_type == "pil":
              lowerCAmelCase				:			Optional[Any]    			=      [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: 
  
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      )					-> tuple[list[list[int]], list[list[str]]]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			List[Any]    			=      list(_UpperCAmelCase				)
      lowerCAmelCase				:			Union[str, Any]    			=      list(_UpperCAmelCase				)
      lowerCAmelCase				:			Optional[int]    			=      len(_UpperCAmelCase				)
      lowerCAmelCase				:			List[Any]    			=      len(_UpperCAmelCase				)
  
      lowerCAmelCase				:			str    			=      [
          [0 for _ in range(len_destination_seq + 1				)] for _ in range(len_source_seq + 1				)
      ]
      lowerCAmelCase				:			str    			=      [
          ['0' for _ in range(len_destination_seq + 1				)] for _ in range(len_source_seq + 1				)
      ]
  
      for i in range(1,      len_source_seq + 1				):
          lowerCAmelCase				:			int    			=      i * delete_cost
          lowerCAmelCase				:			List[str]    			=      f"D{source_seq[i - 1]:c}"
  
      for i in range(1,      len_destination_seq + 1				):
          lowerCAmelCase				:			Tuple    			=      i * insert_cost
          lowerCAmelCase				:			Union[str, Any]    			=      f"I{destination_seq[i - 1]:c}"
  
      for i in range(1,      len_source_seq + 1				):
          for j in range(1,      len_destination_seq + 1				):
              if source_seq[i - 1] == destination_seq[j - 1]:
                  lowerCAmelCase				:			str    			=      costs[i - 1][j - 1] + copy_cost
                  lowerCAmelCase				:			Tuple    			=      f"C{source_seq[i - 1]:c}"
              else:
                  lowerCAmelCase				:			Optional[int]    			=      costs[i - 1][j - 1] + replace_cost
                  lowerCAmelCase				:			Optional[int]    			=      f"R{source_seq[i - 1]:c}" + str(destination_seq[j - 1]				)
  
              if costs[i - 1][j] + delete_cost < costs[i][j]:
                  lowerCAmelCase				:			List[str]    			=      costs[i - 1][j] + delete_cost
                  lowerCAmelCase				:			Optional[int]    			=      f"D{source_seq[i - 1]:c}"
  
              if costs[i][j - 1] + insert_cost < costs[i][j]:
                  lowerCAmelCase				:			Union[str, Any]    			=      costs[i][j - 1] + insert_cost
                  lowerCAmelCase				:			str    			=      f"I{destination_seq[j - 1]:c}"
  
      return costs, ops
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)					-> list[str]:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      if i == 0 and j == 0:
          return []
      else:
          if ops[i][j][0] == "C" or ops[i][j][0] == "R":
              lowerCAmelCase				:			List[str]    			=      assemble_transformation(_UpperCAmelCase,      i - 1,      j - 1				)
              seq.append(ops[i][j]				)
              return seq
          elif ops[i][j][0] == "D":
              lowerCAmelCase				:			int    			=      assemble_transformation(_UpperCAmelCase,      i - 1,      _UpperCAmelCase				)
              seq.append(ops[i][j]				)
              return seq
          else:
              lowerCAmelCase				:			Tuple    			=      assemble_transformation(_UpperCAmelCase,      _UpperCAmelCase,      j - 1				)
              seq.append(ops[i][j]				)
              return seq
  
  
  if __name__ == "__main__":
   __A     ,      __A   :     List[str]      =			compute_transform_tables('''Python''', '''Algorithms''', -1, 1, 2, 2)
  
   __A   :     Optional[int]      =			len(operations)
   __A   :     Optional[Any]      =			len(operations[0])
   __A   :     Any      =			assemble_transformation(operations, m - 1, n - 1)
  
   __A   :     int      =			list('''Python''')
   __A   :     Tuple      =			0
   __A   :     Tuple      =			0
  
   with open('''min_cost.txt''', '''w''') as file:
    for op in sequence:
     print(''''''.join(string))
  
     if op[0] == "C":
      file.write('''%-16s''' % '''Copy %c''' % op[1])
      file.write('''\t\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost -= 1
     elif op[0] == "R":
      __A   :     Any      =			op[2]
  
      file.write('''%-16s''' % ('''Replace %c''' % op[1] + ''' with ''' + str(op[2])))
      file.write('''\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost += 1
     elif op[0] == "D":
      string.pop(i)
  
      file.write('''%-16s''' % '''Delete %c''' % op[1])
      file.write('''\t\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost += 2
     else:
      string.insert(i, op[1])
  
      file.write('''%-16s''' % '''Insert %c''' % op[1])
      file.write('''\t\t\t''' + ''''''.join(string))
      file.write('''\r\n''')
  
      cost += 2
  
     i += 1
  
    print(''''''.join(string))
    print('''Cost: ''', cost)
  
    file.write('''\r\nMinimum cost: ''' + str(cost))
  
  
  
  
  
  