

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_            = '\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					(		UpperCAmelCase			):
						_lowercase        =     42







class 			a					(		UpperCAmelCase			):


						def __init__(    self  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   ):







										'''simple docstring'''


										super().__init__()

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


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_		):







										'''simple docstring'''


										if latents is None:
														_UpperCAmelCase    :						Dict							=      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}'		)
														_UpperCAmelCase    :						Optional[int]							=      latents.to(A_		)

										_UpperCAmelCase    :						Dict							=      latents * scheduler.init_noise_sigma
										return latents


						def     _UpperCAmelCase			(    self  ,   A_=0		):







										'''simple docstring'''


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

										_UpperCAmelCase    :						Union[str, Any]							=      torch.device(f'cuda:{gpu_id}'		)

										_UpperCAmelCase    :						Optional[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     _UpperCAmelCase			(    self		):







										'''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     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_  ,   A_  ,   ):







										'''simple docstring'''


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

										if not isinstance(A_  ,   torch.Tensor		):
														_UpperCAmelCase    :						List[str]							=      self.image_processor(A_  ,   return_tensors="pt"		).pixel_values[0].unsqueeze(0		)

										_UpperCAmelCase    :						Any							=      image.to(dtype=self.image_encoder.dtype  ,   device=A_		)

										_UpperCAmelCase    :						Optional[Any]							=      self.image_encoder(A_		)["last_hidden_state"]
										_UpperCAmelCase    :						List[Any]							=      image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

										_UpperCAmelCase    :						Optional[Any]							=      image_embeds.repeat_interleave(A_  ,   dim=0		)

										if do_classifier_free_guidance:
														_UpperCAmelCase    :						Union[str, Any]							=      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
														_UpperCAmelCase    :						Optional[Any]							=      torch.cat([negative_image_embeds, image_embeds]		)

										return image_embeds





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







										'''simple docstring'''


										if isinstance(A_  ,   PIL.Image.Image		):
														_UpperCAmelCase    :						Dict							=      1
										elif isinstance(A_  ,   torch.Tensor		):
														_UpperCAmelCase    :						Tuple							=      image.shape[0]
										elif isinstance(A_  ,   A_		) and isinstance(image[0]  ,   (torch.Tensor, PIL.Image.Image)		):
														_UpperCAmelCase    :						List[Any]							=      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_		)}'		)

										_UpperCAmelCase    :						List[Any]							=      self._execution_device

										_UpperCAmelCase    :						Optional[Any]							=      batch_size * num_images_per_prompt

										_UpperCAmelCase    :						List[str]							=      guidance_scale > 1.0
										_UpperCAmelCase    :						Dict							=      self._encode_image(A_  ,   A_  ,   A_  ,   A_		)

										# prior

										self.scheduler.set_timesteps(A_  ,   device=A_		)
										_UpperCAmelCase    :						Optional[int]							=      self.scheduler.timesteps

										_UpperCAmelCase    :						Union[str, Any]							=      self.prior.config.num_embeddings
										_UpperCAmelCase    :						Union[str, Any]							=      self.prior.config.embedding_dim

										_UpperCAmelCase    :						Union[str, 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
										_UpperCAmelCase    :						List[str]							=      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
														_UpperCAmelCase    :						Tuple							=      torch.cat([latents] * 2		) if do_classifier_free_guidance else latents
														_UpperCAmelCase    :						Dict							=      self.scheduler.scale_model_input(A_  ,   A_		)

														_UpperCAmelCase    :						Union[str, Any]							=      self.prior(
														    A_  ,   timestep=A_  ,   proj_embedding=A_  ,   ).predicted_image_embedding

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

														_UpperCAmelCase    :						Union[str, Any]							=      self.scheduler.step(
														    A_  ,   timestep=A_  ,   sample=A_  ,   ).prev_sample

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

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

														images.append(A_		)

										_UpperCAmelCase    :						Dict							=      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}'		)

										_UpperCAmelCase    :						Any							=      images.cpu().numpy()

										if output_type == "pil":
														_UpperCAmelCase    :						List[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_		)





from __future__ import annotations

from random import choice

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Any	)				->    Optional[int]:
				return choice(lowerCAmelCase	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					list[int]					, lowerCAmelCase:					int	)				->    int:
				_UpperCAmelCase    :						List[Any]							=      random_pivot(lowerCAmelCase	)

				# partition based on pivot
				# linear time
				_UpperCAmelCase    :						List[str]							=      [e for e in lst if e < pivot]
				_UpperCAmelCase    :						Any							=      [e for e in lst if e > pivot]

				# if we get lucky, pivot might be the element we want.
				# we can easily see this:
				# small (elements smaller than k)
				# + pivot (kth element)
				# + big (elements larger than k)
				if len(lowerCAmelCase	) == k - 1:
								return pivot
				# pivot is in elements bigger than k
				elif len(lowerCAmelCase	) < k - 1:
								return kth_number(lowerCAmelCase					, k - len(lowerCAmelCase	) - 1	)
				# pivot is in elements smaller than k
				else:
								return kth_number(lowerCAmelCase					, lowerCAmelCase	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


