code: 
  
  
  
  """simple docstring"""
  
  
  
  
  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
  
  
  _lowerCAmelCase       :      Union[str, Any]							=							logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  _lowerCAmelCase       :      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 			UpperCAmelCase_						(       _UpperCamelCase		):
        __SCREAMING_SNAKE_CASE		:     Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]]
  
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       _UpperCamelCase		):
  
  
  
        def __init__(  self  :			List[Any]  ,			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 		snake_case_      (  self  :			int  ,			A  :			List[str]  ,			A  :			Tuple  ,			A  :			str  ,			A  :			Any  ,			A  :			str  ,			A  :			int		):
             if latents is None:
                  _UpperCAmelCase    :			Dict              =					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}'		)
                  _UpperCAmelCase    :			List[Any]              =					latents.to(A		)
  
             _UpperCAmelCase    :			int              =					latents * scheduler.init_noise_sigma
             return latents
  
  
  
        def 		snake_case_      (  self  :			Union[str, Any]  ,			A  :			str=0		):
             if is_accelerate_available():
                  from accelerate import cpu_offload
             else:
                  raise ImportError("Please install accelerate via `pip install accelerate`"		)
  
             _UpperCAmelCase    :			int              =					torch.device(f'cuda:{gpu_id}'		)
  
             _UpperCAmelCase    :			str              =					[self.text_encoder, self.prior]
             for cpu_offloaded_model in models:
                  if cpu_offloaded_model is not None:
                       cpu_offload(A  ,			A		)
  
  
  
        def 		snake_case_      (  self  :			Tuple  ,			A  :			List[Any]=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."		)
  
             _UpperCAmelCase    :			Any              =					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)
  
             _UpperCAmelCase    :			List[Any]              =					None
             for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
                  _UpperCAmelCase       , _UpperCAmelCase    :			Dict              =					cpu_offload_with_hook(A  ,			A  ,			prev_module_hook=A		)
  
             if self.safety_checker is not None:
                  _UpperCAmelCase       , _UpperCAmelCase    :			Optional[int]              =					cpu_offload_with_hook(self.safety_checker  ,			A  ,			prev_module_hook=A		)
  
             # We'll offload the last model manually.
             _UpperCAmelCase    :			Any              =					hook
  
  
  
        @property
        def 		snake_case_      (  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(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 		snake_case_      (  self  :			List[Any]  ,			A  :			Tuple  ,			A  :			Any  ,			A  :			Optional[Any]  ,			A  :			Optional[Any]  ,			):
             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
             _UpperCAmelCase    :			List[Any]              =					0
             # get prompt text embeddings
             _UpperCAmelCase    :			str              =					self.tokenizer(
                 A  ,			padding="max_length"  ,			max_length=self.tokenizer.model_max_length  ,			truncation=A  ,			return_tensors="pt"  ,			)
             _UpperCAmelCase    :			Optional[int]              =					text_inputs.input_ids
             _UpperCAmelCase    :			List[str]              =					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		):
                  _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    :			List[str]              =					self.text_encoder(text_input_ids.to(A		)		)
             _UpperCAmelCase    :			Optional[Any]              =					text_encoder_output.text_embeds
  
             _UpperCAmelCase    :			List[Any]              =					prompt_embeds.repeat_interleave(A  ,			dim=0		)
             # in Shap-E it normalize the prompt_embeds and then later rescale it
             _UpperCAmelCase    :			int              =					prompt_embeds / torch.linalg.norm(A  ,			dim=-1  ,			keepdim=A		)
  
             if do_classifier_free_guidance:
                  _UpperCAmelCase    :			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
                  _UpperCAmelCase    :			Optional[int]              =					torch.cat([negative_prompt_embeds, prompt_embeds]		)
  
             # Rescale the features to have unit variance
             _UpperCAmelCase    :			int              =					math.sqrt(prompt_embeds.shape[1]		) * prompt_embeds
  
             return prompt_embeds
  
  
  
        @torch.no_grad()
        @replace_example_docstring(A		)
        def __call__(  self  :			List[str]  ,			A  :			str  ,			A  :			int = 1  ,			A  :			int = 2_5  ,			A  :			Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,			A  :			Optional[torch.FloatTensor] = None  ,			A  :			float = 4.0  ,			A  :			int = 6_4  ,			A  :			Optional[str] = "pil"  ,			A  :			bool = True  ,			):
  
             if isinstance(A  ,			A		):
                  _UpperCAmelCase    :			Dict              =					1
             elif isinstance(A  ,			A		):
                  _UpperCAmelCase    :			Union[str, Any]              =					len(A		)
             else:
                  raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(A		)}'		)
  
             _UpperCAmelCase    :			List[str]              =					self._execution_device
  
             _UpperCAmelCase    :			Optional[int]              =					batch_size * num_images_per_prompt
  
             _UpperCAmelCase    :			str              =					guidance_scale > 1.0
             _UpperCAmelCase    :			Dict              =					self._encode_prompt(A  ,			A  ,			A  ,			A		)
  
             # prior
  
             self.scheduler.set_timesteps(A  ,			device=A		)
             _UpperCAmelCase    :			Tuple              =					self.scheduler.timesteps
  
             _UpperCAmelCase    :			Tuple              =					self.prior.config.num_embeddings
             _UpperCAmelCase    :			Optional[int]              =					self.prior.config.embedding_dim
  
             _UpperCAmelCase    :			Optional[Any]              =					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
             _UpperCAmelCase    :			Optional[int]              =					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
                  _UpperCAmelCase    :			Union[str, Any]              =					torch.cat([latents] * 2		) if do_classifier_free_guidance else latents
                  _UpperCAmelCase    :			Dict              =					self.scheduler.scale_model_input(A  ,			A		)
  
                  _UpperCAmelCase    :			str              =					self.prior(
                      A  ,			timestep=A  ,			proj_embedding=A  ,			).predicted_image_embedding
  
                  # remove the variance
                  _UpperCAmelCase       , _UpperCAmelCase    :			Union[str, Any]              =					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    :			str              =					noise_pred.chunk(2		)
                       _UpperCAmelCase    :			List[Any]              =					noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
  
                  _UpperCAmelCase    :			int              =					self.scheduler.step(
                      A  ,			timestep=A  ,			sample=A  ,			).prev_sample
  
             if output_type == "latent":
                  return ShapEPipelineOutput(images=A		)
  
             _UpperCAmelCase    :			List[Any]              =					[]
             for i, latent in enumerate(A		):
                  _UpperCAmelCase    :			str              =					self.renderer.decode(
                      latent[None, :]  ,			A  ,			size=A  ,			ray_batch_size=4_0_9_6  ,			n_coarse_samples=6_4  ,			n_fine_samples=1_2_8  ,			)
                  images.append(A		)
  
             _UpperCAmelCase    :			Optional[Any]              =					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}'		)
  
             _UpperCAmelCase    :			str              =					images.cpu().numpy()
  
             if output_type == "pil":
                  _UpperCAmelCase    :			Optional[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: 
  
  
  
  """simple docstring"""
  
  
  
  
  import sys
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		list[int]       )						->     int:
  
  
  
  
  
       '''simple docstring'''
  
       if not numbers:
            return 0
       if target == 0 and target in numbers:
            return 0
       if not isinstance(SCREAMING_SNAKE_CASE__		,						(list, tuple)       ) or not all(
           isinstance(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       ) for number in numbers       ):
            raise ValueError("numbers must be an iterable of integers"       )
  
       _UpperCAmelCase    :			List[Any]              =					0
       _UpperCAmelCase    :			str              =					sys.maxsize
  
       while right < len(SCREAMING_SNAKE_CASE__       ):
            curr_sum += numbers[right]
            while curr_sum >= target and left <= right:
                 _UpperCAmelCase    :			Any              =					min(SCREAMING_SNAKE_CASE__		,						right - left + 1       )
                 curr_sum -= numbers[left]
                 left += 1
            right += 1
  
       return 0 if min_len == sys.maxsize else min_len
  