

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
			)








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()}"""
			)





