



"""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		)





"""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
