
'''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								=			logging.get_logger(__name__)  # pylint: disable=invalid-name

__A								=			"\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							(				__lowerCAmelCase ):
			lowerCamelCase							:							str										=     42





class 	A							(				__lowerCAmelCase ):



			def __init__(    self				,   lowerCamelCase__				,   lowerCamelCase__				,   lowerCamelCase__				,   lowerCamelCase__				,   lowerCamelCase__				,   )	->						Optional[Any]:




									'''simple docstring'''



									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__				)	->						Union[str, Any]:




									'''simple docstring'''



									if latents is None:
															lowercase__							       =				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}'''				)
															lowercase__							       =				latents.to(lowerCamelCase__				)

									lowercase__							       =				latents * scheduler.init_noise_sigma
									return latents



			def A__       (    self				,   lowerCamelCase__=0				)	->						List[Any]:




									'''simple docstring'''



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

									lowercase__							       =				torch.device(F'''cuda:{gpu_id}'''				)

									lowercase__							       =				[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				)	->						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 A__       (    self				,   lowerCamelCase__				,   lowerCamelCase__				,   lowerCamelCase__				,   lowerCamelCase__				,   )	->						str:




									'''simple docstring'''



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

									if not isinstance(lowerCamelCase__				,   torch.Tensor				):
															lowercase__							       =				self.image_processor(lowerCamelCase__				,   return_tensors="""pt"""				).pixel_values[0].unsqueeze(0				)

									lowercase__							       =				image.to(dtype=self.image_encoder.dtype				,   device=lowerCamelCase__				)

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

									lowercase__							       =				image_embeds.repeat_interleave(lowerCamelCase__				,   dim=0				)

									if do_classifier_free_guidance:
															lowercase__							       =				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
															lowercase__							       =				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				,   )	->						Union[str, Any]:




									'''simple docstring'''



									if isinstance(lowerCamelCase__				,   PIL.Image.Image				):
															lowercase__							       =				1
									elif isinstance(lowerCamelCase__				,   torch.Tensor				):
															lowercase__							       =				image.shape[0]
									elif isinstance(lowerCamelCase__				,   lowerCamelCase__				) and isinstance(image[0]				,   (torch.Tensor, PIL.Image.Image)				):
															lowercase__							       =				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__				)}'''				)

									lowercase__							       =				self._execution_device

									lowercase__							       =				batch_size * num_images_per_prompt

									lowercase__							       =				guidance_scale > 1.0
									lowercase__							       =				self._encode_image(lowerCamelCase__				,   lowerCamelCase__				,   lowerCamelCase__				,   lowerCamelCase__				)

									# prior

									self.scheduler.set_timesteps(lowerCamelCase__				,   device=lowerCamelCase__				)
									lowercase__							       =				self.scheduler.timesteps

									lowercase__							       =				self.prior.config.num_embeddings
									lowercase__							       =				self.prior.config.embedding_dim

									lowercase__							       =				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
									lowercase__							       =				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
															lowercase__							       =				torch.cat([latents] * 2				) if do_classifier_free_guidance else latents
															lowercase__							       =				self.scheduler.scale_model_input(lowerCamelCase__				,   lowerCamelCase__				)

															lowercase__							       =				self.prior(
															    lowerCamelCase__				,   timestep=lowerCamelCase__				,   proj_embedding=lowerCamelCase__				,   ).predicted_image_embedding

															# remove the variance
															lowercase__							       =				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__							       =				noise_pred.chunk(2				)
																					lowercase__							       =				noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

															lowercase__							       =				self.scheduler.step(
															    lowerCamelCase__				,   timestep=lowerCamelCase__				,   sample=lowerCamelCase__				,   ).prev_sample

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

									lowercase__							       =				[]
									for i, latent in enumerate(lowerCamelCase__				):
															print()
															lowercase__							       =				self.renderer.decode(
															    latent[None, :]				,   lowerCamelCase__				,   size=lowerCamelCase__				,   ray_batch_size=4_096				,   n_coarse_samples=64				,   n_fine_samples=128				,   )

															images.append(lowerCamelCase__				)

									lowercase__							       =				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}'''				)

									lowercase__							       =				images.cpu().numpy()

									if output_type == "pil":
															lowercase__							       =				[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"""
class    __snake_case       :



      def __init__(						self    ,			lowercase    ,			lowercase=None    ,			lowercase=None)	->			List[str]:





        '''simple docstring'''





        a__:     Dict										=	data
        a__:     List[Any]										=	previous
        a__:     Any										=	next_node



      def __str__(						self)	->			str:





        '''simple docstring'''





        return f'{self.data}'



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        return self.data



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





        '''simple docstring'''





        return self.next



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        return self.previous







class    __snake_case       :



      def __init__(						self    ,			lowercase)	->			Dict:





        '''simple docstring'''





        a__:     List[Any]										=	head



      def __iter__(						self)	->			List[Any]:





        '''simple docstring'''





        return self



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        if not self.current:
          raise StopIteration
        else:
          a__:     Dict										=	self.current.get_data()
          a__:     Optional[Any]										=	self.current.get_next()
          return value







class    __snake_case       :



      def __init__(						self)	->			Dict:





        '''simple docstring'''





        a__:     List[Any]										=	None  # First node in list
        a__:     Optional[int]										=	None  # Last node in list



      def __str__(						self)	->			Optional[Any]:





        '''simple docstring'''





        a__:     Dict										=	self.head
        a__:     Optional[Any]										=	[]
        while current is not None:
          nodes.append(current.get_data())
          a__:     str										=	current.get_next()
        return " ".join(str(lowercase) for node in nodes)



      def __contains__(						self    ,			lowercase)	->			Optional[int]:





        '''simple docstring'''





        a__:     Optional[int]										=	self.head
        while current:
          if current.get_data() == value:
            return True
          a__:     Dict										=	current.get_next()
        return False



      def __iter__(						self)	->			int:





        '''simple docstring'''





        return LinkedListIterator(self.head)



      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        if self.head:
          return self.head.get_data()
        return None



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        if self.tail:
          return self.tail.get_data()
        return None



      def   lowerCamelCase_   (						self    ,			lowercase)	->			None:





        '''simple docstring'''





        if self.head is None:
          a__:     Optional[Any]										=	node
          a__:     Optional[Any]										=	node
        else:
          self.insert_before_node(self.head    ,			lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			None:





        '''simple docstring'''





        if self.head is None:
          self.set_head(lowercase)
        else:
          self.insert_after_node(self.tail    ,			lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			None:





        '''simple docstring'''





        a__:     Tuple										=	Node(lowercase)
        if self.head is None:
          self.set_head(lowercase)
        else:
          self.set_tail(lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			None:





        '''simple docstring'''





        a__:     Union[str, Any]										=	node
        a__:     Optional[Any]										=	node.previous

        if node.get_previous() is None:
          a__:     Tuple										=	node_to_insert
        else:
          a__:     int										=	node_to_insert

        a__:     Optional[int]										=	node_to_insert



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			None:





        '''simple docstring'''





        a__:     Optional[int]										=	node
        a__:     Tuple										=	node.next

        if node.get_next() is None:
          a__:     Optional[int]										=	node_to_insert
        else:
          a__:     Any										=	node_to_insert

        a__:     str										=	node_to_insert



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase)	->			None:





        '''simple docstring'''





        a__:     Any										=	1
        a__:     Tuple										=	Node(lowercase)
        a__:     Tuple										=	self.head
        while node:
          if current_position == position:
            self.insert_before_node(lowercase    ,			lowercase)
            return
          current_position += 1
          a__:     List[Any]										=	node.next
        self.insert_after_node(self.tail    ,			lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			Node:





        '''simple docstring'''





        a__:     Tuple										=	self.head
        while node:
          if node.get_data() == item:
            return node
          a__:     List[str]										=	node.get_next()
        raise Exception('Node not found')



      def   lowerCamelCase_   (						self    ,			lowercase)	->			Any:





        '''simple docstring'''





        if (node := self.get_node(lowercase)) is not None:
          if node == self.head:
            a__:     Any										=	self.head.get_next()

          if node == self.tail:
            a__:     List[Any]										=	self.tail.get_previous()

          self.remove_node_pointers(lowercase)



      @staticmethod
      def   lowerCamelCase_   (						lowercase)	->			None:





        '''simple docstring'''





        if node.get_next():
          a__:     Any										=	node.previous

        if node.get_previous():
          a__:     List[str]										=	node.next

        a__:     int										=	None
        a__:     Union[str, Any]										=	None



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        return self.head is None







def 						__a   (	)	->None:
  pass


if __name__ == "__main__":
 import doctest

 doctest.testmod()


