



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    :		int  =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 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     =42



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		def __init__(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,)					->      List[Any]:

						"""simple docstring"""






						super().__init__()

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



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase			)					->      Union[str, Any]:

						"""simple docstring"""






						if latents is None:
										lowerCAmelCase__					:    Tuple												=					randn_tensor(__A		,generator=__A		,device=__A		,dtype=__A			)
						else:
										if latents.shape != shape:
														raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""			)
										lowerCAmelCase__					:    List[str]												=					latents.to(__A			)

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



		def 		lowerCAmelCase__				(self		,__lowerCamelCase=0			)					->      Tuple:

						"""simple docstring"""






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

						lowerCAmelCase__					:    int												=					torch.device(f"""cuda:{gpu_id}"""			)

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



		@property
		def 		lowerCAmelCase__				(self			)					->      Optional[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(__A		,'''_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		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,)					->      Optional[int]:

						"""simple docstring"""






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

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

						lowerCAmelCase__					:    Dict												=					image.to(dtype=self.image_encoder.dtype		,device=__A			)

						lowerCAmelCase__					:    int												=					self.image_encoder(__A			)['''last_hidden_state''']
						lowerCAmelCase__					:    Optional[Any]												=					image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

						lowerCAmelCase__					:    Union[str, Any]												=					image_embeds.repeat_interleave(__A		,dim=0			)

						if do_classifier_free_guidance:
										lowerCAmelCase__					:    int												=					torch.zeros_like(__A			)

										# 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
										lowerCAmelCase__					:    str												=					torch.cat([negative_image_embeds, image_embeds]			)

						return image_embeds



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

						"""simple docstring"""






						if isinstance(__A		,PIL.Image.Image			):
										lowerCAmelCase__					:    Tuple												=					1
						elif isinstance(__A		,torch.Tensor			):
										lowerCAmelCase__					:    Tuple												=					image.shape[0]
						elif isinstance(__A		,__A			) and isinstance(image[0]		,(torch.Tensor, PIL.Image.Image)			):
										lowerCAmelCase__					:    Dict												=					len(__A			)
						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(__A			)}"""			)

						lowerCAmelCase__					:    Tuple												=					self._execution_device

						lowerCAmelCase__					:    int												=					batch_size * num_images_per_prompt

						lowerCAmelCase__					:    str												=					guidance_scale > 1.0
						lowerCAmelCase__					:    List[Any]												=					self._encode_image(__A		,__A		,__A		,__A			)

						# prior

						self.scheduler.set_timesteps(__A		,device=__A			)
						lowerCAmelCase__					:    Dict												=					self.scheduler.timesteps

						lowerCAmelCase__					:    Union[str, Any]												=					self.prior.config.num_embeddings
						lowerCAmelCase__					:    Optional[int]												=					self.prior.config.embedding_dim

						lowerCAmelCase__					:    Any												=					self.prepare_latents(
						    (batch_size, num_embeddings * embedding_dim)		,image_embeds.dtype		,__A		,__A		,__A		,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
						lowerCAmelCase__					:    List[Any]												=					latents.reshape(latents.shape[0]		,__A		,__A			)

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

										lowerCAmelCase__					:    Any												=					self.prior(
										    __A		,timestep=__A		,proj_embedding=__A		,).predicted_image_embedding

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

										if do_classifier_free_guidance is not None:
														lowerCAmelCase__					:    List[str]												=					noise_pred.chunk(2			)
														lowerCAmelCase__					:    int												=					noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

										lowerCAmelCase__					:    int												=					self.scheduler.step(
										    __A		,timestep=__A		,sample=__A		,).prev_sample

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

						lowerCAmelCase__					:    Union[str, Any]												=					[]
						for i, latent in enumerate(__A			):
										print()
										lowerCAmelCase__					:    Any												=					self.renderer.decode(
										    latent[None, :]		,__A		,size=__A		,ray_batch_size=40_96		,n_coarse_samples=64		,n_fine_samples=1_28		,)

										images.append(__A			)

						lowerCAmelCase__					:    Optional[int]												=					torch.stack(__A			)

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

						lowerCAmelCase__					:    str												=					images.cpu().numpy()

						if output_type == "pil":
										lowerCAmelCase__					:    str												=					[self.numpy_to_pil(__A			) 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=__A			)










import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize






def      lowerCAmelCase__		(   lowerCamelCase_		:		ndarray):





				'''simple docstring'''

				return np.dot(lowerCamelCase_	,lowerCamelCase_)



class 	lowerCamelCase__      :




		'''simple docstring'''



		def __init__(self		,*,
		    __lowerCamelCase = np.inf		,__lowerCamelCase = "linear"		,__lowerCamelCase = 0.0		,)					->      None:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					regularization
						lowerCAmelCase__					:    str												=					gamma
						if kernel == "linear":
										lowerCAmelCase__					:    Dict												=					self.__linear
						elif kernel == "rbf":
										if self.gamma == 0:
														raise ValueError('''rbf kernel requires gamma'''			)
										if not isinstance(self.gamma		,(float, int)			):
														raise ValueError('''gamma must be float or int'''			)
										if not self.gamma > 0:
														raise ValueError('''gamma must be > 0'''			)
										lowerCAmelCase__					:    Optional[Any]												=					self.__rbf
										# in the future, there could be a default value like in sklearn
										# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
										# previously it was 1/(n_features)
						else:
										lowerCAmelCase__					:    List[str]												=					f"""Unknown kernel: {kernel}"""
										raise ValueError(__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      float:

						"""simple docstring"""






						return np.dot(__lowerCamelCase		,__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      float:

						"""simple docstring"""






						return np.exp(-(self.gamma * norm_squared(vectora - vectora			))			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      None:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					observations
						lowerCAmelCase__					:    Optional[int]												=					classes

						# using Wolfe's Dual to calculate w.
						# Primal problem: minimize 1/2*norm_squared(w)
						#   constraint: yn(w . xn + b) >= 1
						#
						# With l a vector
						# Dual problem: maximize sum_n(ln) -
						#       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
						#   constraint: self.C >= ln >= 0
						#           and sum_n(ln*yn) = 0
						# Then we get w using w = sum_n(ln*yn*xn)
						# At the end we can get b ~= mean(yn - w . xn)
						#
						# Since we use kernels, we only need l_star to calculate b
						# and to classify observations

						((lowerCAmelCase__)						,		)					:    List[str]												=					np.shape(__lowerCamelCase			)

						def to_minimize(__lowerCamelCase			) -> float:
										lowerCAmelCase__					:    List[str]												=					0
										((lowerCAmelCase__)						,		)					:    str												=					np.shape(__lowerCamelCase			)
										for i in range(__lowerCamelCase			):
														for j in range(__lowerCamelCase			):
																		s += (
																		    candidate[i]
																		    * candidate[j]
																		    * classes[i]
																		    * classes[j]
																		    * self.kernel(observations[i]		,observations[j]			)
																		)
										return 1 / 2 * s - sum(__lowerCamelCase			)

						lowerCAmelCase__					:    List[str]												=					LinearConstraint(__lowerCamelCase		,0		,0			)
						lowerCAmelCase__					:    List[str]												=					Bounds(0		,self.regularization			)

						lowerCAmelCase__					:    int												=					minimize(
						    __lowerCamelCase		,np.ones(__lowerCamelCase			)		,bounds=__lowerCamelCase		,constraints=[ly_contraint]			).x
						lowerCAmelCase__					:    List[Any]												=					l_star

						# calculating mean offset of separation plane to points
						lowerCAmelCase__					:    Optional[Any]												=					0
						for i in range(__lowerCamelCase			):
										for j in range(__lowerCamelCase			):
														s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
														    observations[i]		,observations[j]			)
						lowerCAmelCase__					:    Dict												=					s / n



		def 		lowerCAmelCase__				(self		,__lowerCamelCase			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					sum(
						    self.optimum[n]
						    * self.classes[n]
						    * self.kernel(self.observations[n]		,__lowerCamelCase			)
						    for n in range(len(self.classes			)			)			)
						return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
							import doctest

							doctest.testmod()

