





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


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

snake_case__					:							List[Any]													=     '\n    Examples:\n        ```py\n        >>> from PIL import Image\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif, load_image\n\n        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n\n        >>> repo = "openai/shap-e-img2img"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 3.0\n        >>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"\n        >>> image = load_image(image_url).convert("RGB")\n\n        >>> images = pipe(\n        ...     image,\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], "corgi_3d.gif")\n        ```\n'





@dataclass
class A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = 42





class A_					(			_lowerCamelCase   ):




   def __init__(self	:Any    ,   _UpperCamelCase	:PriorTransformer    ,   _UpperCamelCase	:CLIPVisionModel    ,   _UpperCamelCase	:CLIPImageProcessor    ,   _UpperCamelCase	:HeunDiscreteScheduler    ,   _UpperCamelCase	:ShapERenderer    ,   )->					Union[str, Any]:
         super().__init__()

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




   def _lowerCAmelCase      (self	:Optional[Any]    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:Any    ,   _UpperCamelCase	:Union[str, Any]    ,   _UpperCamelCase	:Optional[int]    ,   _UpperCamelCase	:List[Any]    ,   _UpperCamelCase	:Union[str, Any] )->					List[str]:
         if latents is None:
               __A          =				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}""" )
               __A          =				latents.to(_UpperCamelCase )

         __A          =				latents * scheduler.init_noise_sigma
         return latents




   def _lowerCAmelCase      (self	:Dict    ,   _UpperCamelCase	:int=0 )->					str:
         if is_accelerate_available():
               from accelerate import cpu_offload
         else:
               raise ImportError('''Please install accelerate via `pip install accelerate`''' )

         __A          =				torch.device(f"""cuda:{gpu_id}""" )

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




   @property
   def _lowerCAmelCase      (self	:Optional[Any] )->					Tuple:
         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 _lowerCAmelCase      (self	:str    ,   _UpperCamelCase	:Optional[int]    ,   _UpperCamelCase	:Optional[int]    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:int    ,   )->					str:
         if isinstance(_UpperCamelCase    ,   _UpperCamelCase ) and isinstance(image[0]    ,   torch.Tensor ):
               __A          =				torch.cat(_UpperCamelCase    ,   axis=0 ) if image[0].ndim == 4 else torch.stack(_UpperCamelCase    ,   axis=0 )

         if not isinstance(_UpperCamelCase    ,   torch.Tensor ):
               __A          =				self.image_processor(_UpperCamelCase    ,   return_tensors='''pt''' ).pixel_values[0].unsqueeze(0 )

         __A          =				image.to(dtype=self.image_encoder.dtype    ,   device=_UpperCamelCase )

         __A          =				self.image_encoder(_UpperCamelCase )['''last_hidden_state''']
         __A          =				image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

         __A          =				image_embeds.repeat_interleave(_UpperCamelCase    ,   dim=0 )

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

         return image_embeds






   @torch.no_grad()
   @replace_example_docstring(_UpperCamelCase )
   def __call__(self	:Optional[int]    ,   _UpperCamelCase	:Union[PIL.Image.Image, List[PIL.Image.Image]]    ,   _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    ,   )->					str:

         if isinstance(_UpperCamelCase    ,   PIL.Image.Image ):
               __A          =				1
         elif isinstance(_UpperCamelCase    ,   torch.Tensor ):
               __A          =				image.shape[0]
         elif isinstance(_UpperCamelCase    ,   _UpperCamelCase ) and isinstance(image[0]    ,   (torch.Tensor, PIL.Image.Image) ):
               __A          =				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 )}""" )

         __A          =				self._execution_device

         __A          =				batch_size * num_images_per_prompt

         __A          =				guidance_scale > 1.0
         __A          =				self._encode_image(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )

         # prior

         self.scheduler.set_timesteps(_UpperCamelCase    ,   device=_UpperCamelCase )
         __A          =				self.scheduler.timesteps

         __A          =				self.prior.config.num_embeddings
         __A          =				self.prior.config.embedding_dim

         __A          =				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
         __A          =				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
               __A          =				torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
               __A          =				self.scheduler.scale_model_input(_UpperCamelCase    ,   _UpperCamelCase )

               __A          =				self.prior(
                   _UpperCamelCase    ,   timestep=_UpperCamelCase    ,   proj_embedding=_UpperCamelCase    ,   ).predicted_image_embedding

               # remove the variance
               __A   ,						__A          =				noise_pred.split(
                   scaled_model_input.shape[2]    ,   dim=2 )  # batch_size, num_embeddings, embedding_dim

               if do_classifier_free_guidance is not None:
                     __A   ,						__A          =				noise_pred.chunk(2 )
                     __A          =				noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

               __A          =				self.scheduler.step(
                   _UpperCamelCase    ,   timestep=_UpperCamelCase    ,   sample=_UpperCamelCase    ,   ).prev_sample

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

         __A          =				[]
         for i, latent in enumerate(_UpperCamelCase ):
               print()
               __A          =				self.renderer.decode(
                   latent[None, :]    ,   _UpperCamelCase    ,   size=_UpperCamelCase    ,   ray_batch_size=4096    ,   n_coarse_samples=64    ,   n_fine_samples=128    ,   )

               images.append(_UpperCamelCase )

         __A          =				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}""" )

         __A          =				images.cpu().numpy()

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












def _a				(	lowerCamelCase:   dict )	->						bool:







      '''simple docstring'''






      __A          =				set()
      # To detect a back edge, keep track of vertices currently in the recursion stack
      __A          =				set()
      return any(
          node not in visited and depth_first_search(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )
          for node in graph )



def _a				(	lowerCamelCase:   dict					,				lowerCamelCase:   int					,				lowerCamelCase:   set					,				lowerCamelCase:   set )	->						bool:







      '''simple docstring'''






      visited.add(lowerCamelCase )
      rec_stk.add(lowerCamelCase )

      for node in graph[vertex]:
            if node not in visited:
                  if depth_first_search(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase ):
                        return True
            elif node in rec_stk:
                  return True

    # The node needs to be removed from recursion stack before function ends
      rec_stk.remove(lowerCamelCase )
      return False


if __name__ == "__main__":
       from doctest import testmod

       testmod()





