



import math
import sys


class 						lowerCamelCase__			:


						def __init__(			self    :	int      ):
													a__:							Optional[Any]									=0
													a__:							int									=[]
													a__:							Union[str, Any]									={}  # To store the pos of node in array


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													return self.cur_size == 0


						def 	_lowerCamelCase      (			self    :	str   ,							_a    :	Dict      ):
													a__:							Any									=self.left(_UpperCamelCase      )
													a__:							Optional[int]									=self.right(_UpperCamelCase      )
													if lc < self.cur_size and self.array(_UpperCamelCase      )[0] < self.array(_UpperCamelCase      )[0]:
																				a__:							Any									=lc
													else:
																				a__:							List[str]									=idx
													if rc < self.cur_size and self.array(_UpperCamelCase      )[0] < self.array(_UpperCamelCase      )[0]:
																				a__:							Optional[int]									=rc
													if smallest != idx:
																				self.swap(_UpperCamelCase   ,							_UpperCamelCase      )
																				self.min_heapify(_UpperCamelCase      )


						def 	_lowerCamelCase      (			self    :	int   ,							_a    :	str      ):
													# Inserts a node into the Priority Queue
													a__:							Optional[int]									=self.cur_size
													self.cur_size += 1
													self.array.append((sys.maxsize, tup[1])      )
													self.decrease_key((sys.maxsize, tup[1])   ,							tup[0]      )


						def 	_lowerCamelCase      (			self    :	Tuple      ):
													# Removes and returns the min element at top of priority queue
													a__:							List[Any]									=self.array[0][1]
													a__:							List[Any]									=self.array[self.cur_size - 1]
													self.cur_size -= 1
													self.min_heapify(1      )
													del self.pos[min_node]
													return min_node


						def 	_lowerCamelCase      (			self    :	Any   ,							_a    :	Optional[int]      ):
													# returns the index of left child
													return 2 * i + 1


						def 	_lowerCamelCase      (			self    :	Any   ,							_a    :	Dict      ):
													# returns the index of right child
													return 2 * i + 2


						def 	_lowerCamelCase      (			self    :	Tuple   ,							_a    :	int      ):
													# returns the index of parent
													return math.floor(i / 2      )


						def 	_lowerCamelCase      (			self    :	Tuple   ,							_a    :	Tuple   ,							_a    :	List[Any]      ):
													# swaps array elements at indices i and j
													# update the pos{}
													a__:							str									=j
													a__:							Any									=i
													a__:							str									=self.array[i]
													a__:							Tuple									=self.array[j]
													a__:							Dict									=temp







						def 	_lowerCamelCase      (			self    :	int   ,							_a    :	List[str]   ,							_a    :	Tuple      ):
													a__:							Tuple									=self.pos[tup[1]]
													# assuming the new_d is atmost old_d
													a__:							Optional[Any]									=(new_d, tup[1])
													while idx > 0 and self.array[self.par(_UpperCamelCase      )][0] > self.array[idx][0]:
																				self.swap(_UpperCamelCase   ,							self.par(_UpperCamelCase      )      )
																				a__:							Optional[Any]									=self.par(_UpperCamelCase      )


class 						lowerCamelCase__			:


						def __init__(			self    :	Tuple   ,							_a    :	Optional[int]      ):
													a__:							List[str]									={}  # To store graph: u -> (v,w)
													a__:							Optional[int]									=num  # Number of nodes in graph
													# To store the distance from source vertex
													a__:							Tuple									=[0] * self.num_nodes
													a__:							List[Any]									=[-1] * self.num_nodes  # To store the path


						def 	_lowerCamelCase      (			self    :	Tuple   ,							_a    :	Optional[int]   ,							_a    :	List[Any]   ,							_a    :	int      ):
													#  Edge going from node u to v and v to u with weight w
													# u (w)-> v, v (w) -> u
													# Check if u already in graph
													if u in self.adjList:
																				self.adjList[u].append((v, w)      )
													else:
																				a__:							Optional[int]									=[(v, w)]

													# Assuming undirected graph
													if v in self.adjList:
																				self.adjList[v].append((u, w)      )
													else:
																				a__:							Optional[Any]									=[(u, w)]


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													# u -> v(w)
													for u in self.adjList:
																				print(_UpperCamelCase   ,							"->"   ,							" -> ".join(str(F"{v}({w})"      ) for v, w in self.adjList[u]      )      )


						def 	_lowerCamelCase      (			self    :	Any   ,							_a    :	Dict      ):
													# Flush old junk values in par[]
													a__:							List[Any]									=[-1] * self.num_nodes
													# src is the source node
													a__:							List[Any]									=0
													a__:							Dict									=PriorityQueue()
													q.insert((0, src)      )  # (dist from src, node)
													for u in self.adjList:
																				if u != src:
																											a__:							List[Any]									=sys.maxsize  # Infinity
																											a__:							Dict									=-1

													while not q.is_empty():
																				a__:							Dict									=q.extract_min()  # Returns node with the min dist from source
																				# Update the distance of all the neighbours of u and
																				# if their prev dist was INFINITY then push them in Q
																				for v, w in self.adjList[u]:
																											a__:							Optional[int]									=self.dist[u] + w
																											if self.dist[v] > new_dist:
																																		if self.dist[v] == sys.maxsize:
																																									q.insert((new_dist, v)      )
																																		else:
																																									q.decrease_key((self.dist[v], v)   ,							_UpperCamelCase      )
																																		a__:							List[str]									=new_dist
																																		a__:							Union[str, Any]									=u

        # Show the shortest distances from src
													self.show_distances(_UpperCamelCase      )


						def 	_lowerCamelCase      (			self    :	Tuple   ,							_a    :	Optional[Any]      ):
													print(F"Distance from node: {src}"      )
													for u in range(self.num_nodes      ):
																				print(F"Node {u} has distance: {self.dist[u]}"      )







						def 	_lowerCamelCase      (			self    :	Dict   ,							_a    :	Optional[Any]   ,							_a    :	Optional[Any]      ):
													# To show the shortest path from src to dest
													# WARNING: Use it *after* calling dijkstra
													a__:							Any									=[]
													a__:							Dict									=0
													a__:							List[Any]									=dest
													# Backtracking from dest to src
													while self.par[temp] != -1:
																				path.append(_UpperCamelCase      )
																				if temp != src:
																											for v, w in self.adjList[temp]:
																																		if v == self.par[temp]:
																																									cost += w
																																									break
																				a__:							Any									=self.par[temp]
													path.append(_UpperCamelCase      )
													path.reverse()

													print(F"----Path to reach {dest} from {src}----"      )
													for u in path:
																				print(F"{u}"   ,							end=" "      )
																				if u != dest:
																											print("-> "   ,							end=""      )

													print("\nTotal cost of path: "   ,							_UpperCamelCase      )


if __name__ == "__main__":
		__UpperCAmelCase    	=  Graph(9)
		graph.add_edge(0, 1, 4)
		graph.add_edge(0, 7, 8)
		graph.add_edge(1, 2, 8)
		graph.add_edge(1, 7, 11)
		graph.add_edge(2, 3, 7)
		graph.add_edge(2, 8, 2)
		graph.add_edge(2, 5, 4)
		graph.add_edge(3, 4, 9)
		graph.add_edge(3, 5, 14)
		graph.add_edge(4, 5, 10)
		graph.add_edge(5, 6, 2)
		graph.add_edge(6, 7, 1)
		graph.add_edge(6, 8, 6)
		graph.add_edge(7, 8, 7)
		graph.show_graph()
		graph.dijkstra(0)
		graph.show_path(0, 4)

# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path:  21












import math
from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


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

snake_case__   :				Any						=	'\n    Examples:\n        ```py\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif\n\n        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n\n        >>> repo = "openai/shap-e"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 15.0\n        >>> prompt = "a shark"\n\n        >>> images = pipe(\n        ...     prompt,\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], "shark_3d.gif")\n        ```\n'





@dataclass
class A_   (     _lowerCamelCase  ):
   lowerCAmelCase__  	=					42





class A_   (     _lowerCamelCase  ):




   def __init__(self      :List[str]       ,  _UpperCamelCase      :PriorTransformer       ,  _UpperCamelCase      :CLIPTextModelWithProjection       ,  _UpperCamelCase      :CLIPTokenizer       ,  _UpperCamelCase      :HeunDiscreteScheduler       ,  _UpperCamelCase      :ShapERenderer       ,  )->   Tuple:
         super().__init__()

         self.register_modules(
             prior=_UpperCamelCase       ,  text_encoder=_UpperCamelCase       ,  tokenizer=_UpperCamelCase       ,  scheduler=_UpperCamelCase       ,  renderer=_UpperCamelCase       ,  )




   def _lowerCAmelCase (self      :Any       ,  _UpperCamelCase      :str       ,  _UpperCamelCase      :Optional[int]       ,  _UpperCamelCase      :Union[str, Any]       ,  _UpperCamelCase      :Optional[int]       ,  _UpperCamelCase      :str       ,  _UpperCamelCase      :Union[str, Any]					)->   Any:
         if latents is None:
               __A			  =							randn_tensor(_UpperCamelCase       ,  generator=_UpperCamelCase       ,  device=_UpperCamelCase       ,  dtype=_UpperCamelCase					)
         else:
               if latents.shape != shape:
                     raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""					)
               __A			  =							latents.to(_UpperCamelCase					)

         __A			  =							latents * scheduler.init_noise_sigma
         return latents




   def _lowerCAmelCase (self      :Union[str, Any]       ,  _UpperCamelCase      :Dict=0					)->   Optional[Any]:
         if is_accelerate_available():
               from accelerate import cpu_offload
         else:
               raise ImportError('''Please install accelerate via `pip install accelerate`'''					)

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

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




   def _lowerCAmelCase (self      :Tuple       ,  _UpperCamelCase      :Dict=0					)->   Tuple:
         if is_accelerate_available() and is_accelerate_version('''>='''       ,  '''0.17.0.dev0'''					):
               from accelerate import cpu_offload_with_hook
         else:
               raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''					)

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

         if self.device.type != "cpu":
               self.to('''cpu'''       ,  silence_dtype_warnings=_UpperCamelCase					)
               torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

         __A			  =							None
         for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
               __A  ,		__A			  =							cpu_offload_with_hook(_UpperCamelCase       ,  _UpperCamelCase       ,  prev_module_hook=_UpperCamelCase					)

         if self.safety_checker is not None:
               __A  ,		__A			  =							cpu_offload_with_hook(self.safety_checker       ,  _UpperCamelCase       ,  prev_module_hook=_UpperCamelCase					)

         # We'll offload the last model manually.
         __A			  =							hook




   @property
   def _lowerCAmelCase (self      :int					)->   int:
         if self.device != torch.device('''meta'''					) or not hasattr(self.text_encoder       ,  '''_hf_hook'''					):
               return self.device
         for module in self.text_encoder.modules():
               if (
                   hasattr(_UpperCamelCase       ,  '''_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      :str       ,  _UpperCamelCase      :Optional[Any]       ,  _UpperCamelCase      :Optional[int]       ,  _UpperCamelCase      :Any       ,  _UpperCamelCase      :int       ,  )->   Dict:
         len(_UpperCamelCase					) if isinstance(_UpperCamelCase       ,  _UpperCamelCase					) else 1

         # YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
         __A			  =							0
         # get prompt text embeddings
         __A			  =							self.tokenizer(
             _UpperCamelCase       ,  padding='''max_length'''       ,  max_length=self.tokenizer.model_max_length       ,  truncation=_UpperCamelCase       ,  return_tensors='''pt'''       ,  )
         __A			  =							text_inputs.input_ids
         __A			  =							self.tokenizer(_UpperCamelCase       ,  padding='''longest'''       ,  return_tensors='''pt'''					).input_ids

         if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(_UpperCamelCase       ,  _UpperCamelCase					):
               __A			  =							self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]					)
               logger.warning(
                   '''The following part of your input was truncated because CLIP can only handle sequences up to'''
                   f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""					)

         __A			  =							self.text_encoder(text_input_ids.to(_UpperCamelCase					)					)
         __A			  =							text_encoder_output.text_embeds

         __A			  =							prompt_embeds.repeat_interleave(_UpperCamelCase       ,  dim=0					)
         # in Shap-E it normalize the prompt_embeds and then later rescale it
         __A			  =							prompt_embeds / torch.linalg.norm(_UpperCamelCase       ,  dim=-1       ,  keepdim=_UpperCamelCase					)

         if do_classifier_free_guidance:
               __A			  =							torch.zeros_like(_UpperCamelCase					)

               # 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
               __A			  =							torch.cat([negative_prompt_embeds, prompt_embeds]					)

         # Rescale the features to have unit variance
         __A			  =							math.sqrt(prompt_embeds.shape[1]					) * prompt_embeds

         return prompt_embeds






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

         if isinstance(_UpperCamelCase       ,  _UpperCamelCase					):
               __A			  =							1
         elif isinstance(_UpperCamelCase       ,  _UpperCamelCase					):
               __A			  =							len(_UpperCamelCase					)
         else:
               raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(_UpperCamelCase					)}"""					)

         __A			  =							self._execution_device

         __A			  =							batch_size * num_images_per_prompt

         __A			  =							guidance_scale > 1.0
         __A			  =							self._encode_prompt(_UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase					)

         # prior

         self.scheduler.set_timesteps(_UpperCamelCase       ,  device=_UpperCamelCase					)
         __A			  =							self.scheduler.timesteps

         __A			  =							self.prior.config.num_embeddings
         __A			  =							self.prior.config.embedding_dim

         __A			  =							self.prepare_latents(
             (batch_size, num_embeddings * embedding_dim)       ,  prompt_embeds.dtype       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  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
         __A			  =							latents.reshape(latents.shape[0]       ,  _UpperCamelCase       ,  _UpperCamelCase					)

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

               __A			  =							self.prior(
                   _UpperCamelCase       ,  timestep=_UpperCamelCase       ,  proj_embedding=_UpperCamelCase       ,  ).predicted_image_embedding

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

               if do_classifier_free_guidance is not None:
                     __A  ,		__A			  =							noise_pred.chunk(2					)
                     __A			  =							noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

               __A			  =							self.scheduler.step(
                   _UpperCamelCase       ,  timestep=_UpperCamelCase       ,  sample=_UpperCamelCase       ,  ).prev_sample

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

         __A			  =							[]
         for i, latent in enumerate(_UpperCamelCase					):
               __A			  =							self.renderer.decode(
                   latent[None, :]       ,  _UpperCamelCase       ,  size=_UpperCamelCase       ,  ray_batch_size=4096       ,  n_coarse_samples=64       ,  n_fine_samples=128       ,  )
               images.append(_UpperCamelCase					)

         __A			  =							torch.stack(_UpperCamelCase					)

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

         __A			  =							images.cpu().numpy()

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





