



def      A_ (		__a       :			str = "mytext.txt"	,						__a       :			bool = False						):

				"""simple docstring"""
				import socket

				a__			 =						12_312  # Reserve a port for your service.
				a__			 =						socket.socket()  # Create a socket object
				a__			 =						socket.gethostname()  # Get local machine name
				sock.bind((host, port)						)  # Bind to the port
				sock.listen(5						)  # Now wait for client connection.

				print("""Server listening...."""						)

				while True:
								a__ ,     a__			 =						sock.accept()  # Establish connection with client.
								print(F'''Got connection from {addr}'''						)
								a__			 =						conn.recv(1_024						)
								print(F'''Server received: {data = }'''						)

								with open(__a	,						"""rb"""						) as in_file:
												a__			 =						in_file.read(1_024						)
												while data:
																conn.send(__a						)
																print(F'''Sent {data!r}'''						)
																a__			 =						in_file.read(1_024						)

								print("""Done sending"""						)
								conn.close()
								if testing:  # Allow the test to complete
												break

				sock.shutdown(1						)
				sock.close()


if __name__ == "__main__":
	send_file()






import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel

class 	__snake_case (				utils.Parser):







							'''simple docstring'''






							UpperCamelCase__			: str             =   "halfcheetah-medium-expert-v2"
							UpperCamelCase__			: str             =   "config.offline"




def      A_ (		__a       :			List[Any]	,						__a       :			Union[str, Any]	,						__a       :			Optional[Any]	,						__a       :			Dict	,						__a       :			Union[str, Any]						):

				"""simple docstring"""
				a__ ,     a__			 =						utils.load_model(__a	,						__a	,						__a	,						epoch=__a	,						device=__a						)
				a__			 =						TrajectoryTransformerModel(gpt.config						)

				trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()						)
				a__			 =						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								=   Parser().parse_args("""plan""")
	convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
	    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
	)

