code: 
  
  import torch
  import trajectory.utils as utils
  
  from transformers import TrajectoryTransformerModel
  
  
  
  
  
  
  class     lowerCamelCase_			(  utils.Parser  ):
  	_lowercase		:      str    				=							"halfcheetah-medium-expert-v2"
  	_lowercase		:      str    				=							"config.offline"
  
  
  
  
  
  
  def        __SCREAMING_SNAKE_CASE    (      a__						:      int ,a__						:      Any ,a__						:      Optional[int] ,a__						:      List[Any] ,a__						:      Union[str, Any]					)				->							Optional[int]:
  	__A						,							__A   : Dict								= utils.load_model(a__ ,a__ ,a__ ,epoch=a__ ,device=a__					)
  	__A   : str								= TrajectoryTransformerModel(gpt.config					)
  
  	trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()					)
  	__A   : List[Any]								= 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
  			)
  
  
  
  
  
  
style_context: 
  
  import math
  
  
  
  
  
  
  class     lowerCamelCase_			:
  
  
  
  
  	def __init__(    self	:    Dict				,			__A	:    int				,			__A	:    int				,			__A	:    int				,			__A	:    Any       ):
  		__A   : Union[str, Any]								= x
  		__A   : Union[str, Any]								= y
  		__A   : Optional[int]								= step_size
  		__A   : Dict								= function_to_optimize
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]       ):
  		return self.function(self.x				,			self.y       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    int       ):
  		__A   : Dict								= self.step_size
  		return [
  		    SearchProblem(__A				,			__A				,			__A				,			self.function       )
  		    for x, y in (
  		        (self.x - step_size, self.y - step_size),
  		        (self.x - step_size, self.y),
  		        (self.x - step_size, self.y + step_size),
  		        (self.x, self.y - step_size),
  		        (self.x, self.y + step_size),
  		        (self.x + step_size, self.y - step_size),
  		        (self.x + step_size, self.y),
  		        (self.x + step_size, self.y + step_size),
  		    )
  		]
  
  
  
  
  	def __hash__(    self	:    str       ):
  		return hash(str(self       )       )
  
  
  
  
  	def __eq__(    self	:    Union[str, Any]				,			__A	:    Optional[int]       ):
  		if isinstance(__A				,			__A       ):
  			return hash(str(self       )       ) == hash(str(__A       )       )
  		return False
  
  
  
  
  	def __str__(    self	:    List[Any]       ):
  		return F"""x: {self.x} y: {self.y}"""
  
  
  
  
  
  
  def        __SCREAMING_SNAKE_CASE    (      a__						:      Optional[Any] ,a__						:      bool = True ,a__						:      float = math.inf ,a__						:      float = -math.inf ,a__						:      float = math.inf ,a__						:      float = -math.inf ,a__						:      bool = False ,a__						:      int = 10000 ,)				->							SearchProblem:
  	__A   : int								= search_prob
  	__A   : List[str]								= []  # list to store the current score at each iteration
  	__A   : Optional[Any]								= 0
  	__A   : Any								= False
  	__A   : Dict								= set()
  	while not solution_found and iterations < max_iter:
  		visited.add(a__					)
  		iterations += 1
  		__A   : Union[str, Any]								= current_state.score()
  		scores.append(a__					)
  		__A   : List[Any]								= current_state.get_neighbors()
  		__A   : Tuple								= -math.inf
  		__A   : str								= math.inf
  		__A   : Optional[int]								= None  # to hold the next best neighbor
  		for neighbor in neighbors:
  			if neighbor in visited:
  				continue  # do not want to visit the same state again
  			if (
  			    neighbor.x > max_x
  			    or neighbor.x < min_x
  			    or neighbor.y > max_y
  			    or neighbor.y < min_y
  			):
  				continue  # neighbor outside our bounds
  			__A   : int								= neighbor.score() - current_score
  			if find_max:  # finding max
  				# going to direction with greatest ascent
  				if change > max_change and change > 0:
  					__A   : str								= change
  					__A   : Optional[int]								= neighbor
  			else:  # finding min
  				# to direction with greatest descent
  				if change < min_change and change < 0:
  					__A   : List[Any]								= change
  					__A   : List[str]								= neighbor
  		if next_state is not None:
  			# we found at least one neighbor which improved the current state
  			__A   : Tuple								= next_state
  		else:
  			# since we have no neighbor that improves the solution we stop the search
  			__A   : str								= True
  
  	if visualization:
  		from matplotlib import pyplot as plt
  
  		plt.plot(range(a__					) ,a__					)
  		plt.xlabel("""Iterations"""					)
  		plt.ylabel("""Function values"""					)
  		plt.show()
  
  	return current_state
  
  
  if __name__ == "__main__":
  			import doctest
  
  			doctest.testmod()
  			def        __SCREAMING_SNAKE_CASE    (      a__						:      str ,a__						:      Any					)				->							Dict:
  				return (x**2) + (y**2)
  
  			# starting the problem with initial coordinates (3, 4)
  			UpperCAmelCase_       :				Tuple		   =      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  			UpperCAmelCase_       :				Any		   =      hill_climbing(prob, find_max=False)
  			print(
  			    '''The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
  			    f"""{local_min.score()}"""
  			)
  
  			# starting the problem with initial coordinates (12, 47)
  			UpperCAmelCase_       :				List[Any]		   =      SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
  			UpperCAmelCase_       :				Any		   =      hill_climbing(
  			    prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
  			)
  			print(
  			    '''The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
  			    f"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
  			)
  			def        __SCREAMING_SNAKE_CASE    (      a__						:      int ,a__						:      List[str]					)				->							Any:
  				return (3 * x**2) - (6 * y)
  
  			UpperCAmelCase_       :				Tuple		   =      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  			UpperCAmelCase_       :				Dict		   =      hill_climbing(prob, find_max=True)
  
  
  
  
  
  
  
  			print(
  			    '''The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
  			    f"""{local_min.score()}"""
  			)
  
  
  
  
  
  