




'''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


_snake_case   :   Union[str, Any]     							=	logging.get_logger(__name__)  # pylint: disable=invalid-name

_snake_case   :   Optional[int]     							=	'\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					(	_a					):
							lowercase_           =       42



class     A					(	_a					):







							def __init__(						self :						Tuple							,    lowerCAmelCase_ :						PriorTransformer							,    lowerCAmelCase_ :						CLIPVisionModel							,    lowerCAmelCase_ :						CLIPImageProcessor							,    lowerCAmelCase_ :						HeunDiscreteScheduler							,    lowerCAmelCase_ :						ShapERenderer							,    )    ->							List[Any]:


														"""simple docstring"""


														super().__init__()

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







							def      __lowerCAmelCase    (						self :						Tuple							,    lowerCAmelCase_ :						Union[str, Any]							,    lowerCAmelCase_ :						Union[str, Any]							,    lowerCAmelCase_ :						Dict							,    lowerCAmelCase_ :						Tuple							,    lowerCAmelCase_ :						Tuple							,    lowerCAmelCase_ :						Union[str, Any]      )    ->							Union[str, Any]:


														"""simple docstring"""


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

														_a			  =       latents * scheduler.init_noise_sigma
														return latents







							def      __lowerCAmelCase    (						self :						List[Any]							,    lowerCAmelCase_ :						int=0      )    ->							Union[str, Any]:


														"""simple docstring"""


														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(lowerCAmelCase_							,    lowerCAmelCase_      )







							@property
							def      __lowerCAmelCase    (						self :						str      )    ->							Any:


														"""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(lowerCAmelCase_							,    '''_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 :						Union[str, Any]							,    lowerCAmelCase_ :						str							,    lowerCAmelCase_ :						Optional[Any]							,    lowerCAmelCase_ :						str							,    lowerCAmelCase_ :						Union[str, Any]							,    )    ->							Optional[Any]:


														"""simple docstring"""


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

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

														_a			  =       image.to(dtype=self.image_encoder.dtype							,    device=lowerCAmelCase_      )

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

														_a			  =       image_embeds.repeat_interleave(lowerCAmelCase_							,    dim=0      )

														if do_classifier_free_guidance:
																					_a			  =       torch.zeros_like(lowerCAmelCase_      )

																					# 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(lowerCAmelCase_      )
							def __call__(						self :						Tuple							,    lowerCAmelCase_ :						Union[PIL.Image.Image, List[PIL.Image.Image]]							,    lowerCAmelCase_ :						int = 1							,    lowerCAmelCase_ :						int = 25							,    lowerCAmelCase_ :						Optional[Union[torch.Generator, List[torch.Generator]]] = None							,    lowerCAmelCase_ :						Optional[torch.FloatTensor] = None							,    lowerCAmelCase_ :						float = 4.0							,    lowerCAmelCase_ :						int = 64							,    lowerCAmelCase_ :						Optional[str] = "pil"							,    lowerCAmelCase_ :						bool = True							,    )    ->							Tuple:


														"""simple docstring"""


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

														_a			  =       self._execution_device

														_a			  =       batch_size * num_images_per_prompt

														_a			  =       guidance_scale > 1.0
														_a			  =       self._encode_image(lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_      )

														# prior

														self.scheduler.set_timesteps(lowerCAmelCase_							,    device=lowerCAmelCase_      )
														_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							,    lowerCAmelCase_							,    lowerCAmelCase_							,    lowerCAmelCase_							,    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]							,    lowerCAmelCase_							,    lowerCAmelCase_      )

														for i, t in enumerate(self.progress_bar(lowerCAmelCase_      )      ):
																					# 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(lowerCAmelCase_							,    lowerCAmelCase_      )

																					_a			  =       self.prior(
																					    lowerCAmelCase_							,    timestep=lowerCAmelCase_							,    proj_embedding=lowerCAmelCase_							,    ).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(
																					    lowerCAmelCase_							,    timestep=lowerCAmelCase_							,    sample=lowerCAmelCase_							,    ).prev_sample

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

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

																					images.append(lowerCAmelCase_      )

														_a			  =       torch.stack(lowerCAmelCase_      )

														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(lowerCAmelCase_      ) 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=lowerCAmelCase_      )







'''simple docstring'''


from ..utils import DummyObject, requires_backends

class     A					(	metaclass=_a					):
							lowercase_           =       ['torch', 'torchsde']







							def __init__(						self :						Optional[Any]							,    *lowerCAmelCase_ :						Any							,    **lowerCAmelCase_ :						Tuple      )    ->							Dict:


														"""simple docstring"""


														requires_backends(self							,    ['''torch''', '''torchsde''']      )







							@classmethod
							def      __lowerCAmelCase    (						cls :						Dict							,    *lowerCAmelCase_ :						Optional[int]							,    **lowerCAmelCase_ :						Optional[Any]      )    ->							int:


														"""simple docstring"""


														requires_backends(cls							,    ['''torch''', '''torchsde''']      )







							@classmethod
							def      __lowerCAmelCase    (						cls :						List[str]							,    *lowerCAmelCase_ :						str							,    **lowerCAmelCase_ :						Optional[Any]      )    ->							Any:


														"""simple docstring"""


														requires_backends(cls							,    ['''torch''', '''torchsde''']      )

