



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


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

UpperCAmelCase__       =	"\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   (      A						):
			UpperCamelCase        =					42







class       __lowerCAmelCase   (      A						):



			def __init__(						self       :  List[Any]			,		A       :  PriorTransformer			,		A       :  CLIPVisionModel			,		A       :  CLIPImageProcessor			,		A       :  HeunDiscreteScheduler			,		A       :  ShapERenderer			,		)       ->   Tuple:



						"""simple docstring"""






						super().__init__()

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



			def    _lowerCamelCase	(						self       :  int			,		A       :  Dict			,		A       :  str			,		A       :  Optional[int]			,		A       :  int			,		A       :  str			,		A       :  Tuple)       ->   Optional[Any]:



						"""simple docstring"""






						if latents is None:
									_UpperCAmelCase			=       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			=       latents.to(A)

						_UpperCAmelCase			=       latents * scheduler.init_noise_sigma
						return latents



			def    _lowerCamelCase	(						self       :  Tuple			,		A       :  Union[str, Any]=0)       ->   Dict:



						"""simple docstring"""






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

						_UpperCAmelCase			=       torch.device(F"cuda:{gpu_id}")

						_UpperCAmelCase			=       [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       :  str)       ->   List[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(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       :  List[str]			,		A       :  Union[str, Any]			,		A       :  Any			,		A       :  Optional[Any]			,		A       :  Optional[int]			,		)       ->   str:



						"""simple docstring"""






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

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

						_UpperCAmelCase			=       image.to(dtype=self.image_encoder.dtype			,		device=A)

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

						_UpperCAmelCase			=       image_embeds.repeat_interleave(A			,		dim=0)

						if do_classifier_free_guidance:
									_UpperCAmelCase			=       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			=       torch.cat([negative_image_embeds, image_embeds])

						return image_embeds



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



						"""simple docstring"""






						if isinstance(A			,		PIL.Image.Image):
									_UpperCAmelCase			=       1
						elif isinstance(A			,		torch.Tensor):
									_UpperCAmelCase			=       image.shape[0]
						elif isinstance(A			,		A) and isinstance(image[0]			,		(torch.Tensor, PIL.Image.Image)):
									_UpperCAmelCase			=       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			=       self._execution_device

						_UpperCAmelCase			=       batch_size * num_images_per_prompt

						_UpperCAmelCase			=       guidance_scale > 1.0
						_UpperCAmelCase			=       self._encode_image(A			,		A			,		A			,		A)

						# prior

						self.scheduler.set_timesteps(A			,		device=A)
						_UpperCAmelCase			=       self.scheduler.timesteps

						_UpperCAmelCase			=       self.prior.config.num_embeddings
						_UpperCAmelCase			=       self.prior.config.embedding_dim

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

									_UpperCAmelCase			=       self.prior(
									    A			,		timestep=A			,		proj_embedding=A			,		).predicted_image_embedding

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

									_UpperCAmelCase			=       self.scheduler.step(
									    A			,		timestep=A			,		sample=A			,		).prev_sample

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

						_UpperCAmelCase			=       []
						for i, latent in enumerate(A):
									print()
									_UpperCAmelCase			=       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)

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

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





UpperCAmelCase__       =	{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}





def 							A     (					_UpperCAmelCase    :       dict							,       _UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       List[Any]   )    ->			list[str]:







			'''simple docstring'''
			_UpperCAmelCase			=       set()
			# keep track of all the paths to be checked
			_UpperCAmelCase			=       [[start]]

			# return path if start is goal
			if start == goal:
						return [start]

			# keeps looping until all possible paths have been checked
			while queue:
						# pop the first path from the queue
						_UpperCAmelCase			=       queue.pop(0   )
						# get the last node from the path
						_UpperCAmelCase			=       path[-1]
						if node not in explored:
									_UpperCAmelCase			=       graph[node]
									# go through all neighbour nodes, construct a new path and
									# push it into the queue
									for neighbour in neighbours:
												_UpperCAmelCase			=       list(_UpperCAmelCase   )
												new_path.append(_UpperCAmelCase   )
												queue.append(_UpperCAmelCase   )
												# return path if neighbour is goal
												if neighbour == goal:
															return new_path

            # mark node as explored
									explored.add(_UpperCAmelCase   )

    # in case there's no path between the 2 nodes
			return []





def 							A     (					_UpperCAmelCase    :       dict							,       _UpperCAmelCase    :       Any							,       _UpperCAmelCase    :       List[str]   )    ->			int:







			'''simple docstring'''
			if not graph or start not in graph or target not in graph:
						return -1
			if start == target:
						return 0
			_UpperCAmelCase			=       [start]
			_UpperCAmelCase			=       set(_UpperCAmelCase   )
			# Keep tab on distances from `start` node.
			_UpperCAmelCase			=       {start: 0, target: -1}
			while queue:
						_UpperCAmelCase			=       queue.pop(0   )
						if node == target:
									_UpperCAmelCase			=       (
									    dist[node] if dist[target] == -1 else min(dist[target]							,       dist[node]   )
									)
						for adjacent in graph[node]:
									if adjacent not in visited:
												visited.add(_UpperCAmelCase   )
												queue.append(_UpperCAmelCase   )
												_UpperCAmelCase			=       dist[node] + 1
			return dist[target]


if __name__ == "__main__":
							print(bfs_shortest_path(demo_graph, "G", "D"))  # returns ['G', 'C', 'A', 'B', 'D']
							print(bfs_shortest_path_distance(demo_graph, "G", "D"))  # returns 4
