


'''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


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

lowerCamelCase							:   Dict  						=	'\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   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Union[PIL.Image.Image, np.ndarray]






class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      def __init__(self    :    Union[str, Any]   ,						UpperCamelCase    :    PriorTransformer   ,						UpperCamelCase    :    CLIPVisionModel   ,						UpperCamelCase    :    CLIPImageProcessor   ,						UpperCamelCase    :    HeunDiscreteScheduler   ,						UpperCamelCase    :    ShapERenderer   ,						):

             '''simple docstring'''







             super().__init__()

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



      def       UpperCamelCase__		(self    :    str   ,						UpperCamelCase    :    str   ,						UpperCamelCase    :    Dict   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    Tuple   ,						UpperCamelCase    :    str   ,						UpperCamelCase    :    Any ):

             '''simple docstring'''







             if latents is None:
                    lowercase__      			= 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}" )
                    lowercase__      			= latents.to(UpperCamelCase )

             lowercase__      			= latents * scheduler.init_noise_sigma
             return latents



      def       UpperCamelCase__		(self    :    Union[str, Any]   ,						UpperCamelCase    :    Any=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(UpperCamelCase   ,						UpperCamelCase )



      @property
      def       UpperCamelCase__		(self    :    str ):

             '''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(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    :    Tuple   ,						UpperCamelCase    :    str   ,						UpperCamelCase    :    str   ,						UpperCamelCase    :    Union[str, Any]   ,						UpperCamelCase    :    Any   ,						):

             '''simple docstring'''







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

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

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

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

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

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

             return image_embeds






      @torch.no_grad()
      @replace_example_docstring(UpperCamelCase )
      def __call__(self    :    str   ,						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   ,						):

             '''simple docstring'''







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

             lowercase__      			= self._execution_device

             lowercase__      			= batch_size * num_images_per_prompt

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

             # prior

             self.scheduler.set_timesteps(UpperCamelCase   ,						device=UpperCamelCase )
             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   ,						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
             lowercase__      			= 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
                    lowercase__      			= torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
                    lowercase__      			= self.scheduler.scale_model_input(UpperCamelCase   ,						UpperCamelCase )

                    lowercase__      			= self.prior(
                        UpperCamelCase   ,						timestep=UpperCamelCase   ,						proj_embedding=UpperCamelCase   ,						).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(
                        UpperCamelCase   ,						timestep=UpperCamelCase   ,						sample=UpperCamelCase   ,						).prev_sample

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

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

                    images.append(UpperCamelCase )

             lowercase__      			= 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}" )

             lowercase__      			= images.cpu().numpy()

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











'''simple docstring'''

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel  # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    "stable diffusion controlnet",
    "0.22.0",
    "Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.",
    standard_warn=False,
    stacklevel=3,
)



