






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


lowerCamelCase			:					Tuple         =	logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCamelCase			:					str         =	'''
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        >>> repo = "openai/shap-e-img2img"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"
        >>> image = load_image(image_url).convert("RGB")

        >>> images = pipe(
        ...     image,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], "corgi_3d.gif")
        ```
'''



@dataclass
class 							_UpperCamelCase			(a_			):
      snake_case_  =							42



class 							_UpperCamelCase			(a_			):


      def __init__(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						)->	Dict:
           super().__init__()

           self.register_modules(
               prior=__UpperCamelCase					,						image_encoder=__UpperCamelCase					,						image_processor=__UpperCamelCase					,						scheduler=__UpperCamelCase					,						renderer=__UpperCamelCase					,						)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)->	Union[str, Any]:
           if latents is None:
                __lowerCAmelCase								=					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								=					latents.to(__UpperCamelCase	)

           __lowerCAmelCase								=					latents * scheduler.init_noise_sigma
           return latents


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase=0	)->	str:
           if is_accelerate_available():
                from accelerate import cpu_offload
           else:
                raise ImportError("Please install accelerate via `pip install accelerate`"	)

           __lowerCAmelCase								=					torch.device(F"""cuda:{gpu_id}"""	)

           __lowerCAmelCase								=					[self.image_encoder, self.prior]
           for cpu_offloaded_model in models:
                if cpu_offloaded_model is not None:
                     cpu_offload(__UpperCamelCase					,						__UpperCamelCase	)


      @property
      def  __UpperCAmelCase		(	self	)->	List[str]:
           if self.device != torch.device("meta"	) or not hasattr(self.image_encoder					,						"_hf_hook"	):
                return self.device
           for module in self.image_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  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						)->	Union[str, Any]:
           if isinstance(__UpperCamelCase					,						__UpperCamelCase	) and isinstance(image[0]					,						torch.Tensor	):
                __lowerCAmelCase								=					torch.cat(__UpperCamelCase					,						axis=0	) if image[0].ndim == 4 else torch.stack(__UpperCamelCase					,						axis=0	)

           if not isinstance(__UpperCamelCase					,						torch.Tensor	):
                __lowerCAmelCase								=					self.image_processor(__UpperCamelCase					,						return_tensors="pt"	).pixel_values[0].unsqueeze(0	)

           __lowerCAmelCase								=					image.to(dtype=self.image_encoder.dtype					,						device=__UpperCamelCase	)

           __lowerCAmelCase								=					self.image_encoder(__UpperCamelCase	)["last_hidden_state"]
           __lowerCAmelCase								=					image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

           __lowerCAmelCase								=					image_embeds.repeat_interleave(__UpperCamelCase					,						dim=0	)

           if do_classifier_free_guidance:
                __lowerCAmelCase								=					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								=					torch.cat([negative_image_embeds, image_embeds]	)

           return image_embeds


      @torch.no_grad()
      @replace_example_docstring(__UpperCamelCase	)
      def __call__(	self					,						__UpperCamelCase					,						__UpperCamelCase = 1					,						__UpperCamelCase = 2_5					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = 4.0					,						__UpperCamelCase = 6_4					,						__UpperCamelCase = "pil"					,						__UpperCamelCase = True					,						)->	Dict:

           if isinstance(__UpperCamelCase					,						PIL.Image.Image	):
                __lowerCAmelCase								=					1
           elif isinstance(__UpperCamelCase					,						torch.Tensor	):
                __lowerCAmelCase								=					image.shape[0]
           elif isinstance(__UpperCamelCase					,						__UpperCamelCase	) and isinstance(image[0]					,						(torch.Tensor, PIL.Image.Image)	):
                __lowerCAmelCase								=					len(__UpperCamelCase	)
           else:
                raise ValueError(
                    F"""`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(__UpperCamelCase	)}"""	)

           __lowerCAmelCase								=					self._execution_device

           __lowerCAmelCase								=					batch_size * num_images_per_prompt

           __lowerCAmelCase								=					guidance_scale > 1.0
           __lowerCAmelCase								=					self._encode_image(__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase	)

           # prior

           self.scheduler.set_timesteps(__UpperCamelCase					,						device=__UpperCamelCase	)
           __lowerCAmelCase								=					self.scheduler.timesteps

           __lowerCAmelCase								=					self.prior.config.num_embeddings
           __lowerCAmelCase								=					self.prior.config.embedding_dim

           __lowerCAmelCase								=					self.prepare_latents(
               (batch_size, num_embeddings * embedding_dim)					,						image_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								=					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								=					torch.cat([latents] * 2	) if do_classifier_free_guidance else latents
                __lowerCAmelCase								=					self.scheduler.scale_model_input(__UpperCamelCase					,						__UpperCamelCase	)

                __lowerCAmelCase								=					self.prior(
                    __UpperCamelCase					,						timestep=__UpperCamelCase					,						proj_embedding=__UpperCamelCase					,						).predicted_image_embedding

                # remove the variance
                __lowerCAmelCase			, __lowerCAmelCase								=					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								=					noise_pred.chunk(2	)
                     __lowerCAmelCase								=					noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

                __lowerCAmelCase								=					self.scheduler.step(
                    __UpperCamelCase					,						timestep=__UpperCamelCase					,						sample=__UpperCamelCase					,						).prev_sample

           if output_type == "latent":
                return ShapEPipelineOutput(images=__UpperCamelCase	)

           __lowerCAmelCase								=					[]
           for i, latent in enumerate(__UpperCamelCase	):
                print()
                __lowerCAmelCase								=					self.renderer.decode(
                    latent[None, :]					,						__UpperCamelCase					,						size=__UpperCamelCase					,						ray_batch_size=4_0_9_6					,						n_coarse_samples=6_4					,						n_fine_samples=1_2_8					,						)

                images.append(__UpperCamelCase	)

           __lowerCAmelCase								=					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								=					images.cpu().numpy()

           if output_type == "pil":
                __lowerCAmelCase								=					[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	)














from __future__ import annotations





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case       ):
     __lowerCAmelCase								=					list(range(len(__snake_case       )       )       )
     __lowerCAmelCase								=					[v / w for v, w in zip(__snake_case					, __snake_case       )]
     index.sort(key=lambda __snake_case       : ratio[i]					, reverse=__snake_case       )

     __lowerCAmelCase								=					0
     __lowerCAmelCase								=					[0] * len(__snake_case       )
     for i in index:
          if weight[i] <= capacity:
               __lowerCAmelCase								=					1
               max_value += value[i]
               capacity -= weight[i]
          else:
               __lowerCAmelCase								=					capacity / weight[i]
               max_value += value[i] * capacity / weight[i]
               break

     return max_value, fractions


if __name__ == "__main__":
 import doctest

 doctest.testmod()






