




"""simple docstring"""

UpperCamelCase__			:     Optional[int]			       =				float("inf")
class      A__   :




	def __init__( self	:						List[Any]   ,     lowercase	:						Optional[Any]						)       ->			Tuple:
						__snake_case        =						[0] * n
						__snake_case        =						[0] * n
						__snake_case        =						[0] * n
						__snake_case        =						[[] for _ in range(lowercase						)]




	def 				a_		( self	:						Dict   ,     lowercase	:						Any   ,     lowercase	:						Any   ,     lowercase	:						str   ,     lowercase	:						int=0						)       ->			Optional[Any]:
						self.adj[a].append([b, len(self.adj[b]						), c, 0]						)
						self.adj[b].append([a, len(self.adj[a]						) - 1, rcap, 0]						)




	def 				a_		( self	:						Dict   ,     lowercase	:						int   ,     lowercase	:						Optional[Any]   ,     lowercase	:						Dict						)       ->			Dict:
						if vertex == sink or not flow:
											return flow

						for i in range(self.ptr[vertex]   ,     len(self.adj[vertex]						)						):
											__snake_case        =						self.adj[vertex][i]
											if self.lvl[e[0]] == self.lvl[vertex] + 1:
																__snake_case        =						self.depth_first_search(e[0]   ,     lowercase   ,     min(lowercase   ,     e[2] - e[3]						)						)
																if p:
																					self.adj[vertex][i][3] += p
																					self.adj[e[0]][e[1]][3] -= p
																					return p
											__snake_case        =						self.ptr[vertex] + 1
						return 0




	def 				a_		( self	:						Union[str, Any]   ,     lowercase	:						Dict   ,     lowercase	:						Tuple						)       ->			Union[str, Any]:
						__snake_case   ,   __snake_case        =						0, source
						for l in range(31						):  # noqa: E741  l = 30 maybe faster for random data
											while True:
																__snake_case   ,   __snake_case        =						[0] * len(self.q						), [0] * len(self.q						)
																__snake_case   ,   __snake_case   ,   __snake_case        =						0, 1, 1
																while qi < qe and not self.lvl[sink]:
																					__snake_case        =						self.q[qi]
																					qi += 1
																					for e in self.adj[v]:
																										if not self.lvl[e[0]] and (e[2] - e[3]) >> (30 - l):
																															__snake_case        =						e[0]
																															qe += 1
																															__snake_case        =						self.lvl[v] + 1

																__snake_case        =						self.depth_first_search(lowercase   ,     lowercase   ,     lowercase						)
																while p:
																					flow += p
																					__snake_case        =						self.depth_first_search(lowercase   ,     lowercase   ,     lowercase						)

																if not self.lvl[sink]:
																					break

						return flow
UpperCamelCase__			:     int			       =				Dinic(10)
UpperCamelCase__			:     Union[str, Any]			       =				0
UpperCamelCase__			:     Optional[Any]			       =				9
for vertex in range(1, 5):
		graph.add_edge(source, vertex, 1)
for vertex in range(5, 9):
		graph.add_edge(vertex, sink, 1)
for vertex in range(1, 5):
		graph.add_edge(vertex, vertex + 4, 1)

# Now we can know that is the maximum flow(source -> sink)
print(graph.max_flow(source, sink))








"""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
		)


