





from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass

@dataclass
class SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


					A__				 =    42
					A__				 =    42

class SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	):
										lowerCamelCase__       =       [[] for _ in range(_lowerCAmelCase	)]
										lowerCamelCase__       =       size





					def __getitem__( self	,  _lowerCAmelCase	):
										return iter(self._graph[vertex]	)





					@property
					def 					__magic_name__ ( self	):
										return self._size





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										if weight not in (0, 1):
															raise ValueError("Edge weight must be either 0 or 1."	)

										if to_vertex < 0 or to_vertex >= self.size:
															raise ValueError("Vertex indexes must be in [0; size)."	)

										self._graph[from_vertex].append(Edge(_lowerCAmelCase	,  _lowerCAmelCase	)	)






					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       deque([start_vertex]	)
										lowerCamelCase__       =       [None] * self.size
										lowerCamelCase__       =       0

										while queue:
															lowerCamelCase__       =       queue.popleft()
															lowerCamelCase__       =       distances[current_vertex]
															if current_distance is None:
																				continue

															for edge in self[current_vertex]:
																				lowerCamelCase__       =       current_distance + edge.weight
																				lowerCamelCase__       =       distances[edge.destination_vertex]
																				if (
																				    isinstance(_lowerCAmelCase	,  _lowerCAmelCase	)
																				    and new_distance >= dest_vertex_distance
																				):
																									continue
																				lowerCamelCase__       =       new_distance
																				if edge.weight == 0:
																									queue.appendleft(edge.destination_vertex	)
																				else:
																									queue.append(edge.destination_vertex	)

										if distances[finish_vertex] is None:
															raise ValueError("No path from start_vertex to finish_vertex."	)

										return distances[finish_vertex]


if __name__ == "__main__":
						import doctest

						doctest.testmod()








import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel





def    __UpperCamelCase      (    a,     a=False) ->Optional[Any]:
					lowerCamelCase__       =       OmegaConf.load(a)
					if display:
										print(yaml.dump(OmegaConf.to_container(a)))
					return config





def    __UpperCamelCase      (    a,     a=None,     a=None) ->List[Any]:
					if conf_path is None:
										lowerCamelCase__       =       "./model_checkpoints/vqgan_only.yaml"
					lowerCamelCase__       =       load_config(a,     display=a)
					lowerCamelCase__       =       VQModel(**config.model.params)
					if ckpt_path is None:
										lowerCamelCase__       =       "./model_checkpoints/vqgan_only.pt"
					lowerCamelCase__       =       torch.load(a,     map_location=a)
					if ".ckpt" in ckpt_path:
										lowerCamelCase__       =       sd["state_dict"]
					model.load_state_dict(a,     strict=a)
					model.to(a)
					del sd
					return model





def    __UpperCamelCase      (    a,     a) ->int:
					lowerCamelCase__    ,		lowerCamelCase__    ,		lowerCamelCase__       =       model.encode(a)
					print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}")
					lowerCamelCase__       =       model.decode(a)
					return xrec





def    __UpperCamelCase      (    a,     a=False) ->Dict:
					lowerCamelCase__    ,		lowerCamelCase__       =       string.rsplit(".",     1)
					if reload:
										lowerCamelCase__       =       importlib.import_module(a)
										importlib.reload(a)
					return getattr(importlib.import_module(a,     package=a),     cls)





def    __UpperCamelCase      (    a) ->int:
					if "target" not in config:
										raise KeyError("Expected key `target` to instantiate.")
					return get_obj_from_str(config["target"])(**config.get("params",     {}))





def    __UpperCamelCase      (    a,     a,     a=True,     a=True) ->Optional[Any]:
					lowerCamelCase__       =       instantiate_from_config(a)
					if sd is not None:
										model.load_state_dict(a)
					if gpu:
										model.cuda()
					if eval_mode:
										model.eval()
					return {"model": model}







def    __UpperCamelCase      (    a,     a,     a,     a) ->Dict:
					# load the specified checkpoint
					if ckpt:
										lowerCamelCase__       =       torch.load(a,     map_location="cpu")
										lowerCamelCase__       =       pl_sd["global_step"]
										print(f"loaded model from global step {global_step}.")
					else:
										lowerCamelCase__       =       {"state_dict": None}
										lowerCamelCase__       =       None
					lowerCamelCase__       =       load_model_from_config(config.model,     pl_sd["state_dict"],     gpu=a,     eval_mode=a)["model"]
					return model, global_step

