






"""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[str]  				=		logging.get_logger(__name__)  # pylint: disable=invalid-name

__A		:       Optional[Any]  				=		"""
    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       ( _lowerCamelCase							):
					SCREAMING_SNAKE_CASE_		:				str							    =   42





class 					_UpperCAmelCase       ( _lowerCamelCase							):




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

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




					def   A  (				self     :						int    ,						A     :						List[Any]    ,						A     :						List[Any]    ,						A     :						Optional[int]    ,						A     :						Optional[int]    ,						A     :						Dict    ,						A     :						str   )  ->  Optional[Any]:
							if latents is None:
									lowercase_ : List[str]							       =   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}'''   )
									lowercase_ : Tuple							       =   latents.to(a_   )

							lowercase_ : Any							       =   latents * scheduler.init_noise_sigma
							return latents




					def   A  (				self     :						Optional[int]    ,						A     :						int=0   )  ->  Optional[int]:
							if is_accelerate_available():
									from accelerate import cpu_offload
							else:
									raise ImportError('''Please install accelerate via `pip install accelerate`'''   )

							lowercase_ : Optional[Any]							       =   torch.device(F'''cuda:{gpu_id}'''   )

							lowercase_ : Optional[Any]							       =   [self.image_encoder, self.prior]
							for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
											cpu_offload(a_    ,						a_   )




					@property
					def   A  (				self     :						List[Any]   )  ->  Tuple:
							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   A  (				self     :						Optional[Any]    ,						A     :						Dict    ,						A     :						Optional[Any]    ,						A     :						Optional[Any]    ,						A     :						Optional[Any]    ,						)  ->  Optional[int]:
							if isinstance(a_    ,						a_   ) and isinstance(image[0]    ,						torch.Tensor   ):
									lowercase_ : Dict							       =   torch.cat(a_    ,						axis=0   ) if image[0].ndim == 4 else torch.stack(a_    ,						axis=0   )

							if not isinstance(a_    ,						torch.Tensor   ):
									lowercase_ : List[str]							       =   self.image_processor(a_    ,						return_tensors='''pt'''   ).pixel_values[0].unsqueeze(0   )

							lowercase_ : Tuple							       =   image.to(dtype=self.image_encoder.dtype    ,						device=a_   )

							lowercase_ : Dict							       =   self.image_encoder(a_   )["""last_hidden_state"""]
							lowercase_ : List[str]							       =   image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

							lowercase_ : Tuple							       =   image_embeds.repeat_interleave(a_    ,						dim=0   )

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

							return image_embeds






					@torch.no_grad()
					@replace_example_docstring(a_   )
					def __call__(				self     :						Dict    ,						A     :						Union[str, Any]    ,						A     :						Optional[Any] = 1    ,						A     :						str = 25    ,						A     :						Dict = None    ,						A     :						List[str] = None    ,						A     :						Union[str, Any] = 4.0    ,						A     :						Optional[Any] = 64    ,						A     :						Any = "pil"    ,						A     :						str = True    ,						)  ->  int:

							if isinstance(a_    ,						PIL.Image.Image   ):
									lowercase_ : List[str]							       =   1
							elif isinstance(a_    ,						torch.Tensor   ):
									lowercase_ : int							       =   image.shape[0]
							elif isinstance(a_    ,						a_   ) and isinstance(image[0]    ,						(torch.Tensor, PIL.Image.Image)   ):
									lowercase_ : Optional[int]							       =   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_   )}'''   )

							lowercase_ : Tuple							       =   self._execution_device

							lowercase_ : List[Any]							       =   batch_size * num_images_per_prompt

							lowercase_ : List[Any]							       =   guidance_scale > 1.0
							lowercase_ : Optional[Any]							       =   self._encode_image(a_    ,						a_    ,						a_    ,						a_   )

							# prior

							self.scheduler.set_timesteps(a_    ,						device=a_   )
							lowercase_ : Union[str, Any]							       =   self.scheduler.timesteps

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

							lowercase_ : Optional[int]							       =   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
							lowercase_ : 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
									lowercase_ : Optional[Any]							       =   torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents
									lowercase_ : Optional[int]							       =   self.scheduler.scale_model_input(a_    ,						a_   )

									lowercase_ : int							       =   self.prior(
									    a_    ,						timestep=a_    ,						proj_embedding=a_    ,						).predicted_image_embedding

									# remove the variance
									lowercase_ : List[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:
											lowercase_ : Any							       =   noise_pred.chunk(2   )
											lowercase_ : Union[str, Any]							       =   noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

									lowercase_ : Optional[int]							       =   self.scheduler.step(
									    a_    ,						timestep=a_    ,						sample=a_    ,						).prev_sample

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

							lowercase_ : List[Any]							       =   []
							for i, latent in enumerate(a_   ):
									print()
									lowercase_ : List[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_   )

							lowercase_ : 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}'''   )

							lowercase_ : Union[str, Any]							       =   images.cpu().numpy()

							if output_type == "pil":
									lowercase_ : Tuple							       =   [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_   )








'''simple docstring'''






class 							lowercase       :


 """simple docstring"""


 def __init__(							self							)		->   List[str]:
        _UpperCAmelCase  :						int			     = 0
        _UpperCAmelCase  :						Union[str, Any]			     = 0
        _UpperCAmelCase  :						Optional[int]			     = {}







 def _snake_case (							self  ,a_							)		->   Optional[Any]:
        if vertex not in self.adjacency:
               _UpperCAmelCase  :						int			     = {}
               self.num_vertices += 1







 def _snake_case (							self  ,a_  ,a_  ,a_							)		->   int:
        self.add_vertex(a_							)
        self.add_vertex(a_							)

        if head == tail:
               return

        _UpperCAmelCase  :						List[Any]			     = weight
        _UpperCAmelCase  :						Dict			     = weight







 def _snake_case (							self							)		->   Dict:
        _UpperCAmelCase  :						Optional[int]			     = self.get_edges()
        for edge in edges:
               _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase  :						Dict			     = edge
               edges.remove((tail, head, weight)							)
        for i in range(len(a_							)							):
               _UpperCAmelCase  :						str			     = list(edges[i]							)

        edges.sort(key=lambda a_							: e[2]							)
        for i in range(len(a_							) - 1							):
               if edges[i][2] >= edges[i + 1][2]:
                      _UpperCAmelCase  :						Optional[Any]			     = edges[i][2] + 1
        for edge in edges:
               _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase  :						Union[str, Any]			     = edge
               _UpperCAmelCase  :						str			     = weight
               _UpperCAmelCase  :						List[str]			     = weight







 def __str__(							self							)		->   Any:
        _UpperCAmelCase  :						List[Any]			     = """"""
        for tail in self.adjacency:
               for head in self.adjacency[tail]:
                      _UpperCAmelCase  :						List[str]			     = self.adjacency[head][tail]
                      string += f'''{head} -> {tail} == {weight}\n'''
        return string.rstrip("""\n"""							)







 def _snake_case (							self							)		->   str:
        _UpperCAmelCase  :						int			     = []
        for tail in self.adjacency:
               for head in self.adjacency[tail]:
                      output.append((tail, head, self.adjacency[head][tail])							)
        return output







 def _snake_case (							self							)		->   Optional[int]:
        return self.adjacency.keys()







 @staticmethod
 def _snake_case (							a_=None  ,a_=None							)		->   Tuple:
        _UpperCAmelCase  :						List[Any]			     = Graph()
        if vertices is None:
               _UpperCAmelCase  :						List[str]			     = []
        if edges is None:
               _UpperCAmelCase  :						Optional[Any]			     = []
        for vertex in vertices:
               g.add_vertex(a_							)
        for edge in edges:
               g.add_edge(*a_							)
        return g



 class 							lowercase       :


  """simple docstring"""


  def __init__(							self							)		->   int:
         _UpperCAmelCase  :						List[str]			     = {}
         _UpperCAmelCase  :						int			     = {}







  def __len__(							self							)		->   Tuple:
         return len(self.parent							)







  def _snake_case (							self  ,a_							)		->   str:
         if item in self.parent:
                return self.find(a_							)

         _UpperCAmelCase  :						Optional[Any]			     = item
         _UpperCAmelCase  :						List[Any]			     = 0
         return item







  def _snake_case (							self  ,a_							)		->   List[str]:
         if item not in self.parent:
                return self.make_set(a_							)
         if item != self.parent[item]:
                _UpperCAmelCase  :						List[Any]			     = self.find(self.parent[item]							)
         return self.parent[item]







  def _snake_case (							self  ,a_  ,a_							)		->   Union[str, Any]:
         _UpperCAmelCase  :						Any			     = self.find(a_							)
         _UpperCAmelCase  :						List[str]			     = self.find(a_							)

         if roota == roota:
                return roota

         if self.rank[roota] > self.rank[roota]:
                _UpperCAmelCase  :						Any			     = roota
                return roota

         if self.rank[roota] < self.rank[roota]:
                _UpperCAmelCase  :						Any			     = roota
                return roota

         if self.rank[roota] == self.rank[roota]:
                self.rank[roota] += 1
                _UpperCAmelCase  :						List[str]			     = roota
                return roota
         return None







 @staticmethod
 def _snake_case (							a_							)		->   List[Any]:
        _UpperCAmelCase  :						int			     = graph.num_vertices

        _UpperCAmelCase  :						int			     = Graph.UnionFind()
        _UpperCAmelCase  :						Optional[int]			     = []
        while num_components > 1:
               _UpperCAmelCase  :						int			     = {}
               for vertex in graph.get_vertices():
                      _UpperCAmelCase  :						Union[str, Any]			     = -1

               _UpperCAmelCase  :						Tuple			     = graph.get_edges()
               for edge in edges:
                      _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase  :						str			     = edge
                      edges.remove((tail, head, weight)							)
               for edge in edges:
                      _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase  :						Optional[Any]			     = edge
                      _UpperCAmelCase  :						Any			     = union_find.find(a_							)
                      _UpperCAmelCase  :						Any			     = union_find.find(a_							)
                      if seta != seta:
                             if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
                                    _UpperCAmelCase  :						Tuple			     = [head, tail, weight]

                             if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
                                    _UpperCAmelCase  :						List[str]			     = [head, tail, weight]
               for vertex in cheap_edge:
                      if cheap_edge[vertex] != -1:
                             _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase  :						str			     = cheap_edge[vertex]
                             if union_find.find(a_							) != union_find.find(a_							):
                                    union_find.union(a_  ,a_							)
                                    mst_edges.append(cheap_edge[vertex]							)
                                    _UpperCAmelCase  :						Tuple			     = num_components - 1
        _UpperCAmelCase  :						Optional[int]			     = Graph.build(edges=a_							)
        return mst



