





"""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


a		:					List[Any]	      =     logging.get_logger(__name__)  # pylint: disable=invalid-name

a		:					int	      =     '''
    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							(		a__		):
							lowerCamelCase  :   Union[PIL.Image.Image, np.ndarray]


class 			__UpperCamelCase							(		a__		):






							def __init__(  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   )      ->					Union[str, Any]:
								super().__init__()

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






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					Dict:
								if latents is None:
									a :						Tuple      =							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 :						List[str]      =							latents.to(lowerCAmelCase__ )

								a :						Union[str, Any]      =							latents * scheduler.init_noise_sigma
								return latents






							def        __a  (  self ,   lowerCAmelCase__=0 )      ->					str:
								if is_accelerate_available():
									from accelerate import cpu_offload
								else:
									raise ImportError("Please install accelerate via `pip install accelerate`" )

								a :						List[str]      =							torch.device(f"""cuda:{gpu_id}""" )

								a :						List[str]      =							[self.image_encoder, self.prior]
								for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
										cpu_offload(lowerCAmelCase__ ,   lowerCAmelCase__ )






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

								if not isinstance(lowerCAmelCase__ ,   torch.Tensor ):
									a :						Union[str, Any]      =							self.image_processor(lowerCAmelCase__ ,   return_tensors="pt" ).pixel_values[0].unsqueeze(0 )

								a :						int      =							image.to(dtype=self.image_encoder.dtype ,   device=lowerCAmelCase__ )

								a :						List[str]      =							self.image_encoder(lowerCAmelCase__ )["last_hidden_state"]
								a :						Union[str, Any]      =							image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

								a :						Optional[int]      =							image_embeds.repeat_interleave(lowerCAmelCase__ ,   dim=0 )

								if do_classifier_free_guidance:
									a :						str      =							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 :						Dict      =							torch.cat([negative_image_embeds, image_embeds] )

								return image_embeds






							@torch.no_grad()
							@replace_example_docstring(lowerCAmelCase__ )
							def __call__(  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ = 1 ,   lowerCAmelCase__ = 25 ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = 4.0 ,   lowerCAmelCase__ = 64 ,   lowerCAmelCase__ = "pil" ,   lowerCAmelCase__ = True ,   )      ->					Any:

								if isinstance(lowerCAmelCase__ ,   PIL.Image.Image ):
									a :						List[str]      =							1
								elif isinstance(lowerCAmelCase__ ,   torch.Tensor ):
									a :						int      =							image.shape[0]
								elif isinstance(lowerCAmelCase__ ,   lowerCAmelCase__ ) and isinstance(image[0] ,   (torch.Tensor, PIL.Image.Image) ):
									a :						List[str]      =							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 :						Any      =							self._execution_device

								a :						Optional[int]      =							batch_size * num_images_per_prompt

								a :						Optional[int]      =							guidance_scale > 1.0
								a :						Optional[int]      =							self._encode_image(lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )

								# prior

								self.scheduler.set_timesteps(lowerCAmelCase__ ,   device=lowerCAmelCase__ )
								a :						Tuple      =							self.scheduler.timesteps

								a :						str      =							self.prior.config.num_embeddings
								a :						List[Any]      =							self.prior.config.embedding_dim

								a :						List[str]      =							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 :						Tuple      =							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 :						Dict      =							torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
									a :						int      =							self.scheduler.scale_model_input(lowerCAmelCase__ ,   lowerCAmelCase__ )

									a :						Optional[Any]      =							self.prior(
									    lowerCAmelCase__ ,   timestep=lowerCAmelCase__ ,   proj_embedding=lowerCAmelCase__ ,   ).predicted_image_embedding

									# remove the variance
									a,			a :						Optional[Any]      =							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 :						Optional[Any]      =							noise_pred.chunk(2 )
										a :						Tuple      =							noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

									a :						List[str]      =							self.scheduler.step(
									    lowerCAmelCase__ ,   timestep=lowerCAmelCase__ ,   sample=lowerCAmelCase__ ,   ).prev_sample

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

								a :						str      =							[]
								for i, latent in enumerate(lowerCAmelCase__ ):
									print()
									a :						Optional[int]      =							self.renderer.decode(
									    latent[None, :] ,   lowerCAmelCase__ ,   size=lowerCAmelCase__ ,   ray_batch_size=4096 ,   n_coarse_samples=64 ,   n_fine_samples=128 ,   )

									images.append(lowerCAmelCase__ )

								a :						Dict      =							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 :						Optional[int]      =							images.cpu().numpy()

								if output_type == "pil":
									a :						Optional[Any]      =							[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 numpy import exp, pi, sqrt






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Tuple       ,			_lowercase		:       float = 0.0       ,			_lowercase		:       float = 1.0   )					->int:






	'''simple docstring'''






	return 1 / sqrt(2 * pi * sigma**2   ) * exp(-((x - mu) ** 2) / (2 * sigma**2)   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()


