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
  
  
  __snake_case							      =						logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  __snake_case							      =						"""
      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     UpperCAmelCase_		(      lowercase				):
  
  
  
        """simple docstring"""
        UpperCamelCase_       :      Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]]
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      lowercase				):
  
  
  
        """simple docstring"""
        def __init__(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       )       ->   Any:
            super().__init__()
  
            self.register_modules(
                prior=SCREAMING_SNAKE_CASE_					,       text_encoder=SCREAMING_SNAKE_CASE_					,       tokenizer=SCREAMING_SNAKE_CASE_					,       scheduler=SCREAMING_SNAKE_CASE_					,       renderer=SCREAMING_SNAKE_CASE_					,       )
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   str:
            if latents is None:
                UpperCamelCase :str										=     randn_tensor(SCREAMING_SNAKE_CASE_					,       generator=SCREAMING_SNAKE_CASE_					,       device=SCREAMING_SNAKE_CASE_					,       dtype=SCREAMING_SNAKE_CASE_				)
            else:
                if latents.shape != shape:
                    raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''				)
                UpperCamelCase :Dict										=     latents.to(SCREAMING_SNAKE_CASE_				)
  
            UpperCamelCase :int										=     latents * scheduler.init_noise_sigma
            return latents
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_=0				)       ->   Optional[int]:
            if is_accelerate_available():
                from accelerate import cpu_offload
            else:
                raise ImportError('''Please install accelerate via `pip install accelerate`'''				)
  
            UpperCamelCase :Dict										=     torch.device(F'''cuda:{gpu_id}'''				)
  
            UpperCamelCase :int										=     [self.text_encoder, self.prior]
            for cpu_offloaded_model in models:
                if cpu_offloaded_model is not None:
                    cpu_offload(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_=0				)       ->   Any:
            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.'''				)
  
            UpperCamelCase :List[Any]										=     torch.device(F'''cuda:{gpu_id}'''				)
  
            if self.device.type != "cpu":
                self.to('''cpu'''					,       silence_dtype_warnings=SCREAMING_SNAKE_CASE_				)
                torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
  
            UpperCamelCase :List[Any]										=     None
            for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
                UpperCamelCase							,     UpperCamelCase :int										=     cpu_offload_with_hook(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       prev_module_hook=SCREAMING_SNAKE_CASE_				)
  
            if self.safety_checker is not None:
                UpperCamelCase							,     UpperCamelCase :Optional[int]										=     cpu_offload_with_hook(self.safety_checker					,       SCREAMING_SNAKE_CASE_					,       prev_module_hook=SCREAMING_SNAKE_CASE_				)
  
            # We'll offload the last model manually.
            UpperCamelCase :Tuple										=     hook
        @property
        def 					UpperCAmelCase		(       self				)       ->   str:
            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(SCREAMING_SNAKE_CASE_					,       '''_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					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       )       ->   Any:
            len(SCREAMING_SNAKE_CASE_				) if isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				) else 1
  
            # YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
            UpperCamelCase :Any										=     0
            # get prompt text embeddings
            UpperCamelCase :Optional[Any]										=     self.tokenizer(
                SCREAMING_SNAKE_CASE_					,       padding='''max_length'''					,       max_length=self.tokenizer.model_max_length					,       truncation=SCREAMING_SNAKE_CASE_					,       return_tensors='''pt'''					,       )
            UpperCamelCase :Union[str, Any]										=     text_inputs.input_ids
            UpperCamelCase :Dict										=     self.tokenizer(SCREAMING_SNAKE_CASE_					,       padding='''longest'''					,       return_tensors='''pt'''				).input_ids
  
            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                UpperCamelCase :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}'''				)
  
            UpperCamelCase :Optional[int]										=     self.text_encoder(text_input_ids.to(SCREAMING_SNAKE_CASE_				)				)
            UpperCamelCase :Optional[Any]										=     text_encoder_output.text_embeds
  
            UpperCamelCase :int										=     prompt_embeds.repeat_interleave(SCREAMING_SNAKE_CASE_					,       dim=0				)
            # in Shap-E it normalize the prompt_embeds and then later rescale it
            UpperCamelCase :Optional[Any]										=     prompt_embeds / torch.linalg.norm(SCREAMING_SNAKE_CASE_					,       dim=-1					,       keepdim=SCREAMING_SNAKE_CASE_				)
  
            if do_classifier_free_guidance:
                UpperCamelCase :int										=     torch.zeros_like(SCREAMING_SNAKE_CASE_				)
  
                # 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
                UpperCamelCase :Any										=     torch.cat([negative_prompt_embeds, prompt_embeds]				)
  
            # Rescale the features to have unit variance
            UpperCamelCase :str										=     math.sqrt(prompt_embeds.shape[1]				) * prompt_embeds
  
            return prompt_embeds
  
  
  
  
        @torch.no_grad()
        @replace_example_docstring(SCREAMING_SNAKE_CASE_				)
        def __call__(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_ = 1					,       SCREAMING_SNAKE_CASE_ = 25					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = 4.0					,       SCREAMING_SNAKE_CASE_ = 64					,       SCREAMING_SNAKE_CASE_ = "pil"					,       SCREAMING_SNAKE_CASE_ = True					,       )       ->   Optional[Any]:
  
            if isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                UpperCamelCase :Any										=     1
            elif isinstance(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				):
                UpperCamelCase :Optional[Any]										=     len(SCREAMING_SNAKE_CASE_				)
            else:
                raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_				)}'''				)
  
            UpperCamelCase :Any										=     self._execution_device
  
            UpperCamelCase :Tuple										=     batch_size * num_images_per_prompt
  
            UpperCamelCase :str										=     guidance_scale > 1.0
            UpperCamelCase :str										=     self._encode_prompt(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
  
            # prior
  
            self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_					,       device=SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :Dict										=     self.scheduler.timesteps
  
            UpperCamelCase :Tuple										=     self.prior.config.num_embeddings
            UpperCamelCase :List[str]										=     self.prior.config.embedding_dim
  
            UpperCamelCase :Optional[int]										=     self.prepare_latents(
                (batch_size, num_embeddings * embedding_dim)					,       prompt_embeds.dtype					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       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
            UpperCamelCase :Optional[Any]										=     latents.reshape(latents.shape[0]					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
  
            for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_				)				):
                # expand the latents if we are doing classifier free guidance
                UpperCamelCase :Union[str, Any]										=     torch.cat([latents] * 2				) if do_classifier_free_guidance else latents
                UpperCamelCase :Union[str, Any]										=     self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)
  
                UpperCamelCase :Tuple										=     self.prior(
                    SCREAMING_SNAKE_CASE_					,       timestep=SCREAMING_SNAKE_CASE_					,       proj_embedding=SCREAMING_SNAKE_CASE_					,       ).predicted_image_embedding
  
                # remove the variance
                UpperCamelCase							,     UpperCamelCase :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:
                    UpperCamelCase							,     UpperCamelCase :Union[str, Any]										=     noise_pred.chunk(2				)
                    UpperCamelCase :Optional[Any]										=     noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
  
                UpperCamelCase :Optional[Any]										=     self.scheduler.step(
                    SCREAMING_SNAKE_CASE_					,       timestep=SCREAMING_SNAKE_CASE_					,       sample=SCREAMING_SNAKE_CASE_					,       ).prev_sample
  
            if output_type == "latent":
                return ShapEPipelineOutput(images=SCREAMING_SNAKE_CASE_				)
  
            UpperCamelCase :Optional[Any]										=     []
            for i, latent in enumerate(SCREAMING_SNAKE_CASE_				):
                UpperCamelCase :Optional[Any]										=     self.renderer.decode(
                    latent[None, :]					,       SCREAMING_SNAKE_CASE_					,       size=SCREAMING_SNAKE_CASE_					,       ray_batch_size=4096					,       n_coarse_samples=64					,       n_fine_samples=128					,       )
                images.append(SCREAMING_SNAKE_CASE_				)
  
            UpperCamelCase :List[str]										=     torch.stack(SCREAMING_SNAKE_CASE_				)
  
            if output_type not in ["np", "pil"]:
                raise ValueError(F'''Only the output types `pil` and `np` are supported not output_type={output_type}'''				)
  
            UpperCamelCase :Tuple										=     images.cpu().numpy()
  
            if output_type == "pil":
                UpperCamelCase :Any										=     [self.numpy_to_pil(SCREAMING_SNAKE_CASE_				) 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=SCREAMING_SNAKE_CASE_				)
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from __future__ import annotations
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			int ):
      UpperCamelCase :int										=     {1}
      UpperCamelCase :Optional[int]										=     [1]
      for _ in range(2  ,						depth + 1 ):
          UpperCamelCase :Union[str, Any]										=     [*previous_coefficients, 0]
          UpperCamelCase :List[str]										=     [0, *previous_coefficients]
          UpperCamelCase :List[str]										=     []
          for x, y in zip(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ ):
              coefficients.add(x + y )
              previous_coefficients.append(x + y )
      return coefficients
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			set[int] ):
      UpperCamelCase :Union[str, Any]										=     set()
      for number in unique_coefficients:
          UpperCamelCase :Optional[int]										=     2
          UpperCamelCase :List[Any]										=     number
          while divisor**2 <= copy_number:
              UpperCamelCase :List[Any]										=     0
              while copy_number % divisor == 0:
                  copy_number //= divisor
                  multiplicity += 1
              if multiplicity >= 2:
                  non_squarefrees.add(SCREAMING_SNAKE_CASE__ )
                  break
              divisor += 1
  
      return unique_coefficients.difference(SCREAMING_SNAKE_CASE__ )
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			int = 51 ):
      UpperCamelCase :Any										=     get_pascal_triangle_unique_coefficients(SCREAMING_SNAKE_CASE__ )
      UpperCamelCase :str										=     get_squarefrees(SCREAMING_SNAKE_CASE__ )
      return sum(SCREAMING_SNAKE_CASE__ )
  
  
  if __name__ == "__main__":
   print(f'''{solution() = }''')
  
  
  
  
  
  