


"""simple docstring"""






from __future__ import annotations


class      __lowerCamelCase	:

     '''simple docstring'''


     def __init__(   self     ,					__UpperCAmelCase				)							->				None:
          _a     						=					vertices
          _a     						=					[[0] * vertices for _ in range(__UpperCAmelCase				)]


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				None:
          print('''Vertex \t Distance from Source'''				)
          for vertex in range(self.vertices				):
               print(__UpperCAmelCase     ,					'''\t\t'''     ,					distances_from_source[vertex]				)


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase				)							->				int:
          _a     						=					1e7
          _a     						=					0

          # Search not nearest vertex not in the shortest path tree
          for vertex in range(self.vertices				):
               if distances_from_source[vertex] < minimum and visited[vertex] is False:
                    _a     						=					distances_from_source[vertex]
                    _a     						=					vertex
          return min_index





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				None:
          _a     						=					[int(1e7				)] * self.vertices  # distances from the source
          _a     						=					0
          _a     						=					[False] * self.vertices

          for _ in range(self.vertices				):
               _a     						=					self.minimum_distance(__UpperCAmelCase     ,					__UpperCAmelCase				)
               _a     						=					True

               # Update dist value of the adjacent vertices
               # of the picked vertex only if the current
               # distance is greater than new distance and
               # the vertex in not in the shortest path tree
               for v in range(self.vertices				):
                    if (
                        self.graph[u][v] > 0
                        and visited[v] is False
                        and distances[v] > distances[u] + self.graph[u][v]
                    ):
                         _a     						=					distances[u] + self.graph[u][v]

          self.print_solution(__UpperCAmelCase				)


if __name__ == "__main__":
   __snake_case												=							Graph(9)
   __snake_case												=							[
       [0, 4, 0, 0, 0, 0, 0, 8, 0],
       [4, 0, 8, 0, 0, 0, 0, 11, 0],
       [0, 8, 0, 7, 0, 4, 0, 0, 2],
       [0, 0, 7, 0, 9, 14, 0, 0, 0],
       [0, 0, 0, 9, 0, 10, 0, 0, 0],
       [0, 0, 4, 14, 10, 0, 2, 0, 0],
       [0, 0, 0, 0, 0, 2, 0, 1, 6],
       [8, 11, 0, 0, 0, 0, 1, 0, 7],
       [0, 0, 2, 0, 0, 0, 6, 7, 0],
   ]
   graph.dijkstra(0)





"""simple docstring"""

import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel
class      A__   (   utils.Parser  ):
	A_				:					str      =   "halfcheetah-medium-expert-v2"
	A_				:					str      =   "config.offline"




def    snake_case__						(							lowerCAmelCase :		Optional[int]					,       lowerCAmelCase :		List[Any]					,       lowerCAmelCase :		List[str]					,       lowerCAmelCase :		Optional[int]					,       lowerCAmelCase :		List[Any]		):
					__snake_case   ,   __snake_case        =						utils.load_model(lowerCAmelCase					,       lowerCAmelCase					,       lowerCAmelCase					,       epoch=lowerCAmelCase					,       device=lowerCAmelCase		)
					__snake_case        =						TrajectoryTransformerModel(gpt.config		)

					trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()		)
					__snake_case        =						gpt.pos_emb
					trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()		)
					trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()		)
					trajectory_transformer.head.load_state_dict(gpt.head.state_dict()		)

					for i, block in enumerate(gpt.blocks		):
										trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()		)
										trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()		)
										trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()		)

										trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()		)
										trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()		)
										trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()		)
										trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()		)

					torch.save(trajectory_transformer.state_dict()					,       """pytorch_model.bin"""		)


if __name__ == "__main__":
		UpperCamelCase__			:     int			       =				Parser().parse_args("plan")
		convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
		    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
		)


