"""simple docstring"""




def SCREAMING_SNAKE_CASE			(       lowercase_:		Union[str, Any]			):
       # must be
       _UpperCamelCase       :    Tuple					  =					[list(A_			) for row in matrix]
       if matrix and isinstance(A_						,  A_			):
              if isinstance(matrix[0]						,  A_			):
                     _UpperCamelCase       :    int					  =					0
                     for row in matrix:
                            if prev_len == 0:
                                   _UpperCamelCase       :    List[str]					  =					len(A_			)
                                   _UpperCamelCase       :    Optional[int]					  =					True
                            else:
                                   _UpperCamelCase       :    List[str]					  =					prev_len == len(A_			)
              else:
                     _UpperCamelCase       :    str					  =					True
       else:
              _UpperCamelCase       :    Any					  =					False

       return result


def SCREAMING_SNAKE_CASE			(       lowercase_:		List[Any]			):
       if check_matrix(A_			) and len(A_			) > 0:
              _UpperCamelCase       :    Any					  =					[list(A_			) for row in a]
              _UpperCamelCase       :    Optional[Any]					  =					len(A_			)
              if isinstance(a[0]						,  A_			):
                     _UpperCamelCase       :    Optional[int]					  =					len(a[0]			)
              else:
                     for dat in a:
                            print(A_			)
                     return

              # horizotal printing increasing
              for i in range(0						,  A_			):
                     print(a[0][i]			)
              # vertical printing down
              for i in range(1						,  A_			):
                     print(a[i][mat_col - 1]			)
              # horizotal printing decreasing
              if mat_row > 1:
                     for i in range(mat_col - 2						,  -1						,  -1			):
                            print(a[mat_row - 1][i]			)
        # vertical printing up
              for i in range(mat_row - 2						,  0						,  -1			):
                     print(a[i][0]			)
              _UpperCamelCase       :    Dict					  =					[row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
              if len(A_			) > 0:
                     spiral_print_clockwise(A_			)
              else:
                     return
       else:
              print('Not a valid matrix'			)
              return


def SCREAMING_SNAKE_CASE			(       lowercase_:		str			):
       if matrix:
              return list(matrix.pop(0			)			) + spiral_traversal(list(zip(*A_			)			)[::-1]			)
       else:
              return []


# driver code
if __name__ == "__main__":
 import doctest

 doctest.testmod()

 UpperCAmelCase     						=			[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
 spiral_print_clockwise(a)




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
 )



