





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

_snake_case           =			"""
    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  lowerCAmelCase							(       lowercase_ ):
  __lowerCamelCase		=      42





class  lowerCAmelCase							(       lowercase_ ):







  def __init__(      self  :Tuple   ,							_lowercase  :PriorTransformer   ,							_lowercase  :CLIPVisionModel   ,							_lowercase  :CLIPImageProcessor   ,							_lowercase  :HeunDiscreteScheduler   ,							_lowercase  :ShapERenderer   ,							):






     '''simple docstring'''





     super().__init__()

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







  def   UpperCAmelCase					(      self  :List[Any]   ,							_lowercase  :str   ,							_lowercase  :str   ,							_lowercase  :Optional[Any]   ,							_lowercase  :Tuple   ,							_lowercase  :Tuple   ,							_lowercase  :Dict ):






     '''simple docstring'''





     if latents is None:
        lowercase__			       =  randn_tensor(_lowercase   ,							generator=_lowercase   ,							device=_lowercase   ,							dtype=_lowercase )
     else:
        if latents.shape != shape:
           raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
        lowercase__			       =  latents.to(_lowercase )

     lowercase__			       =  latents * scheduler.init_noise_sigma
     return latents







  def   UpperCAmelCase					(      self  :Optional[Any]   ,							_lowercase  :str=0 ):






     '''simple docstring'''





     if is_accelerate_available():
        from accelerate import cpu_offload
     else:
        raise ImportError("Please install accelerate via `pip install accelerate`" )

     lowercase__			       =  torch.device(f'''cuda:{gpu_id}''' )

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







  @property
  def   UpperCAmelCase					(      self  :int ):






     '''simple docstring'''





     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(_lowercase   ,							"_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  :Union[str, Any]   ,							_lowercase  :Optional[int]   ,							_lowercase  :Optional[int]   ,							_lowercase  :List[str]   ,							_lowercase  :int   ,							):






     '''simple docstring'''





     if isinstance(_lowercase   ,							_lowercase ) and isinstance(image[0]   ,							torch.Tensor ):
        lowercase__			       =  torch.cat(_lowercase   ,							axis=0 ) if image[0].ndim == 4 else torch.stack(_lowercase   ,							axis=0 )

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

     lowercase__			       =  image.to(dtype=self.image_encoder.dtype   ,							device=_lowercase )

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

     lowercase__			       =  image_embeds.repeat_interleave(_lowercase   ,							dim=0 )

     if do_classifier_free_guidance:
        lowercase__			       =  torch.zeros_like(_lowercase )

        # 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
        lowercase__			       =  torch.cat([negative_image_embeds, image_embeds] )

     return image_embeds







  @torch.no_grad()
  @replace_example_docstring(_lowercase )
  def __call__(      self  :Dict   ,							_lowercase  :Union[PIL.Image.Image, List[PIL.Image.Image]]   ,							_lowercase  :int = 1   ,							_lowercase  :int = 25   ,							_lowercase  :Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,							_lowercase  :Optional[torch.FloatTensor] = None   ,							_lowercase  :float = 4.0   ,							_lowercase  :int = 64   ,							_lowercase  :Optional[str] = "pil"   ,							_lowercase  :bool = True   ,							):






     '''simple docstring'''





     if isinstance(_lowercase   ,							PIL.Image.Image ):
        lowercase__			       =  1
     elif isinstance(_lowercase   ,							torch.Tensor ):
        lowercase__			       =  image.shape[0]
     elif isinstance(_lowercase   ,							_lowercase ) and isinstance(image[0]   ,							(torch.Tensor, PIL.Image.Image) ):
        lowercase__			       =  len(_lowercase )
     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(_lowercase )}''' )

     lowercase__			       =  self._execution_device

     lowercase__			       =  batch_size * num_images_per_prompt

     lowercase__			       =  guidance_scale > 1.0
     lowercase__			       =  self._encode_image(_lowercase   ,							_lowercase   ,							_lowercase   ,							_lowercase )

     # prior

     self.scheduler.set_timesteps(_lowercase   ,							device=_lowercase )
     lowercase__			       =  self.scheduler.timesteps

     lowercase__			       =  self.prior.config.num_embeddings
     lowercase__			       =  self.prior.config.embedding_dim

     lowercase__			       =  self.prepare_latents(
         (batch_size, num_embeddings * embedding_dim)   ,							image_embeds.dtype   ,							_lowercase   ,							_lowercase   ,							_lowercase   ,							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
     lowercase__			       =  latents.reshape(latents.shape[0]   ,							_lowercase   ,							_lowercase )

     for i, t in enumerate(self.progress_bar(_lowercase ) ):
        # expand the latents if we are doing classifier free guidance
        lowercase__			       =  torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
        lowercase__			       =  self.scheduler.scale_model_input(_lowercase   ,							_lowercase )

        lowercase__			       =  self.prior(
            _lowercase   ,							timestep=_lowercase   ,							proj_embedding=_lowercase   ,							).predicted_image_embedding

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

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

        lowercase__			       =  self.scheduler.step(
            _lowercase   ,							timestep=_lowercase   ,							sample=_lowercase   ,							).prev_sample

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

     lowercase__			       =  []
     for i, latent in enumerate(_lowercase ):
        print()
        lowercase__			       =  self.renderer.decode(
            latent[None, :]   ,							_lowercase   ,							size=_lowercase   ,							ray_batch_size=40_96   ,							n_coarse_samples=64   ,							n_fine_samples=1_28   ,							)

        images.append(_lowercase )

     lowercase__			       =  torch.stack(_lowercase )

     if output_type not in ["np", "pil"]:
        raise ValueError(f'''Only the output types `pil` and `np` are supported not output_type={output_type}''' )

     lowercase__			       =  images.cpu().numpy()

     if output_type == "pil":
        lowercase__			       =  [self.numpy_to_pil(_lowercase ) 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=_lowercase )













from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments


def _A       (   ):
   lowercase__			       =  HfArgumentParser(__magic_name__	)
   lowercase__			       =  parser.parse_args_into_dataclasses()[0]
   lowercase__			       =  TensorFlowBenchmark(args=__magic_name__	)
   try:
      lowercase__			       =  parser.parse_args_into_dataclasses()[0]
   except ValueError as e:
      lowercase__			       =  "Arg --no_{0} is no longer used, please use --no-{0} instead."
      lowercase__			       =  " ".join(str(__magic_name__	).split(" "	)[:-1]	)
      lowercase__			       =  ""
      lowercase__			       =  eval(str(__magic_name__	).split(" "	)[-1]	)
      lowercase__			       =  []
      for arg in depreciated_args:
         # arg[2:] removes '--'
         if arg[2:] in TensorFlowBenchmark.deprecated_args:
            # arg[5:] removes '--no_'
            full_error_msg += arg_error_msg.format(arg[5:]	)
         else:
            wrong_args.append(__magic_name__	)
      if len(__magic_name__	) > 0:
         lowercase__			       =  full_error_msg + begin_error_msg + str(__magic_name__	)
      raise ValueError(__magic_name__	)
   benchmark.run()


if __name__ == "__main__":
  main()






