
"""simple docstring"""

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


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

SCREAMING_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      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							Union[PIL.Image.Image, np.ndarray]

class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):




      def __init__(							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       )						->					Optional[int]:
          super().__init__()

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




      def     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_							)						->					str:
          if latents is None:
              UpperCAmelCase									=					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}"							)
              UpperCAmelCase									=					latents.to(lowercase_							)

          UpperCAmelCase									=					latents * scheduler.init_noise_sigma
          return latents




      def     a_      (							self   ,       lowercase_=0							)						->					List[Any]:
          if is_accelerate_available():
              from accelerate import cpu_offload
          else:
              raise ImportError('Please install accelerate via `pip install accelerate`'							)

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

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




      @property
      def     a_      (							self							)						->					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(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     a_      (							self   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       )						->					Dict:
          if isinstance(lowercase_   ,       lowercase_							) and isinstance(image[0]   ,       torch.Tensor							):
              UpperCAmelCase									=					torch.cat(lowercase_   ,       axis=0							) if image[0].ndim == 4 else torch.stack(lowercase_   ,       axis=0							)

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

          UpperCAmelCase									=					image.to(dtype=self.image_encoder.dtype   ,       device=lowercase_							)

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

          UpperCAmelCase									=					image_embeds.repeat_interleave(lowercase_   ,       dim=0							)

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

          return image_embeds




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

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

          UpperCAmelCase									=					self._execution_device

          UpperCAmelCase									=					batch_size * num_images_per_prompt

          UpperCAmelCase									=					guidance_scale > 1.0
          UpperCAmelCase									=					self._encode_image(lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_							)

          # prior

          self.scheduler.set_timesteps(lowercase_   ,       device=lowercase_							)
          UpperCAmelCase									=					self.scheduler.timesteps

          UpperCAmelCase									=					self.prior.config.num_embeddings
          UpperCAmelCase									=					self.prior.config.embedding_dim

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

              UpperCAmelCase									=					self.prior(
                  lowercase_   ,       timestep=lowercase_   ,       proj_embedding=lowercase_   ,       ).predicted_image_embedding

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

              UpperCAmelCase									=					self.scheduler.step(
                  lowercase_   ,       timestep=lowercase_   ,       sample=lowercase_   ,       ).prev_sample

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

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

              images.append(lowercase_							)

          UpperCAmelCase									=					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}"							)

          UpperCAmelCase									=					images.cpu().numpy()

          if output_type == "pil":
              UpperCAmelCase									=					[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_							)





"""simple docstring"""

def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					int ) ->      int:






    """simple docstring"""




    return number | (1 << position)




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					int ) ->      int:






    """simple docstring"""




    return number & ~(1 << position)




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					int ) ->      int:






    """simple docstring"""




    return number ^ (1 << position)




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					int ) ->      bool:






    """simple docstring"""




    return ((number >> position) & 1) == 1




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					int ) ->      int:






    """simple docstring"""




    return int((number & (1 << position)) != 0 )


if __name__ == "__main__":
   import doctest

   doctest.testmod()



