"""simple docstring"""


import numpy as np
import requests





def       lowerCamelCase_			( )							-> Any:




						'''simple docstring'''



						lowerCamelCase__ =requests.get(
						    "https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"
						    "master/Week1/ADRvsRating.csv"    )
						lowerCamelCase__ =response.text.splitlines()
						lowerCamelCase__ =[]
						for item in lines:
												lowerCamelCase__ =item.split(","    )
												data.append(__lowerCAmelCase    )
						data.pop(0    )  # This is for removing the labels from the list
						lowerCamelCase__ =np.matrix(__lowerCAmelCase    )
						return dataset





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> List[str]:




						'''simple docstring'''



						lowerCamelCase__ =len_data

						lowerCamelCase__ =np.dot(__lowerCAmelCase       ,   data_x.transpose()    )
						prod -= data_y.transpose()
						lowerCamelCase__ =np.dot(__lowerCAmelCase       ,   __lowerCAmelCase    )
						lowerCamelCase__ =theta - (alpha / n) * sum_grad
						return theta





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> List[str]:




						'''simple docstring'''



						lowerCamelCase__ =np.dot(__lowerCAmelCase       ,   data_x.transpose()    )
						prod -= data_y.transpose()
						lowerCamelCase__ =np.sum(np.square(__lowerCAmelCase    )    )
						lowerCamelCase__ =sum_elem / (2 * len_data)
						return error





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Union[str, Any]:




						'''simple docstring'''



						lowerCamelCase__ =10_0000
						lowerCamelCase__ =0.000_1550

						lowerCamelCase__ =data_x.shape[1]
						lowerCamelCase__ =data_x.shape[0] - 1

						lowerCamelCase__ =np.zeros((1, no_features)    )

						for i in range(0       ,   __lowerCAmelCase    ):
												lowerCamelCase__ =run_steep_gradient_descent(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )
												lowerCamelCase__ =sum_of_square_error(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )
												print(F'''At Iteration {i + 1} - Error is {error:.5f}'''    )

						return theta





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Optional[Any]:




						'''simple docstring'''



						lowerCamelCase__ =sum(abs(y - predicted_y[i]    ) for i, y in enumerate(__lowerCAmelCase    )    )
						return total / len(__lowerCAmelCase    )





def       lowerCamelCase_			( )							-> Tuple:




						'''simple docstring'''



						lowerCamelCase__ =collect_dataset()

						lowerCamelCase__ =data.shape[0]
						lowerCamelCase__ =np.c_[np.ones(__lowerCAmelCase    ), data[:, :-1]].astype(__lowerCAmelCase    )
						lowerCamelCase__ =data[:, -1].astype(__lowerCAmelCase    )

						lowerCamelCase__ =run_linear_regression(__lowerCAmelCase       ,   __lowerCAmelCase    )
						lowerCamelCase__ =theta.shape[1]
						print("Resultant Feature vector : "    )
						for i in range(0       ,   __lowerCAmelCase    ):
												print(F'''{theta[0, i]:.5f}'''    )


if __name__ == "__main__":
	main()


"""simple docstring"""


import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel






class        __UpperCAmelCase						(     utils.Parser     ):
				A__				:       str  							=	"halfcheetah-medium-expert-v2"
				A__				:       str  							=	"config.offline"





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Optional[int]:




						'''simple docstring'''



						lowerCamelCase__ ,				lowerCamelCase__ =utils.load_model(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   epoch=__lowerCAmelCase       ,   device=__lowerCAmelCase    )
						lowerCamelCase__ =TrajectoryTransformerModel(gpt.config    )

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


