import matplotlib.pyplot as plt
import numpy as np


def SCREAMING_SNAKE_CASE			(       lowercase_:		np.ndarray						,  lowercase_:		np.ndarray						,  lowercase_:		float			):
       _UpperCamelCase       :    Any					  =					len(lowercase_			)  # Number of training samples
       _UpperCamelCase       :    Optional[int]					  =					np.eye(lowercase_			)  # Initialize weights as identity matrix
       for j in range(lowercase_			):
              _UpperCamelCase       :    str					  =					point - x_train[j]
              _UpperCamelCase       :    Optional[Any]					  =					np.exp(diff @ diff.T / (-2.0 * tau**2)			)

       return weights


def SCREAMING_SNAKE_CASE			(       lowercase_:		np.ndarray						,  lowercase_:		np.ndarray						,  lowercase_:		np.ndarray						,  lowercase_:		float			):
       _UpperCamelCase       :    Tuple					  =					weight_matrix(lowercase_						,  lowercase_						,  lowercase_			)
       _UpperCamelCase       :    Union[str, Any]					  =					np.linalg.inv(x_train.T @ weight_mat @ x_train			) @ (
           x_train.T @ weight_mat @ y_train.T
       )

       return weight


def SCREAMING_SNAKE_CASE			(       lowercase_:		np.ndarray						,  lowercase_:		np.ndarray						,  lowercase_:		float			):
       _UpperCamelCase       :    Any					  =					np.zeros(len(lowercase_			)			)  # Initialize array of predictions
       for i, item in enumerate(lowercase_			):
              _UpperCamelCase       :    Dict					  =					item @ local_weight(lowercase_						,  lowercase_						,  lowercase_						,  lowercase_			)

       return y_pred


def SCREAMING_SNAKE_CASE			(       lowercase_:		str						,  lowercase_:		str						,  lowercase_:		str			):
       import seaborn as sns

       _UpperCamelCase       :    Union[str, Any]					  =					sns.load_dataset(lowercase_			)
       _UpperCamelCase       :    Dict					  =					np.array(data[x_name]			)
       _UpperCamelCase       :    Optional[Any]					  =					np.array(data[y_name]			)

       _UpperCamelCase       :    int					  =					np.ones(len(lowercase_			)			)

       # pairing elements of one and x_data
       _UpperCamelCase       :    Any					  =					np.column_stack((one, x_data)			)

       return x_train, x_data, y_data


def SCREAMING_SNAKE_CASE			(       lowercase_:		np.ndarray						,  lowercase_:		np.ndarray						,  lowercase_:		np.ndarray						,  lowercase_:		np.ndarray						,  lowercase_:		str						,  lowercase_:		str						,  ):
       _UpperCamelCase       :    str					  =					np.sort(lowercase_						,  axis=0			)
       plt.scatter(lowercase_						,  lowercase_						,  color='blue'			)
       plt.plot(
           x_train_sorted[:, 1]						,  preds[x_train[:, 1].argsort(0			)]						,  color='yellow'						,  linewidth=5						,  )
       plt.title('Local Weighted Regression'			)
       plt.xlabel(lowercase_			)
       plt.ylabel(lowercase_			)
       plt.show()


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # Demo with a dataset from the seaborn module
 UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase     						=			load_data('''tips''', '''total_bill''', '''tip''')
 UpperCAmelCase     						=			local_weight_regression(training_data_x, tip, 5)
 plot_preds(training_data_x, predictions, total_bill, tip, '''total_bill''', '''tip''')




import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel




class 				__lowerCAmelCase					(    utils.Parser  ):



 '''simple docstring'''
 _lowerCamelCase  :		str			      =	"halfcheetah-medium-expert-v2"
 _lowerCamelCase  :		str			      =	"config.offline"







def SCREAMING_SNAKE_CASE			(       lowercase_:		Optional[Any]						,  lowercase_:		Tuple						,  lowercase_:		Dict						,  lowercase_:		Union[str, Any]						,  lowercase_:		List[Any]			):
       _UpperCamelCase  , _UpperCamelCase       :    List[Any]					  =					utils.load_model(lowercase_						,  lowercase_						,  lowercase_						,  epoch=lowercase_						,  device=lowercase_			)
       _UpperCamelCase       :    Tuple					  =					TrajectoryTransformerModel(gpt.config			)

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



