

'''simple docstring'''



def      a__ (						a__   ,					a__   ,					a__   ,					a__   ,					a__   ,					a__   ,					):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   list(a__		)
     __SCREAMING_SNAKE_CASE						     =   list(a__		)
     __SCREAMING_SNAKE_CASE						     =   len(a__		)
     __SCREAMING_SNAKE_CASE						     =   len(a__		)

     __SCREAMING_SNAKE_CASE						     =   [
         [0 for _ in range(len_destination_seq + 1		)] for _ in range(len_source_seq + 1		)
     ]
     __SCREAMING_SNAKE_CASE						     =   [
         ["""0""" for _ in range(len_destination_seq + 1		)] for _ in range(len_source_seq + 1		)
     ]

     for i in range(1   ,					len_source_seq + 1		):
          __SCREAMING_SNAKE_CASE						     =   i * delete_cost
          __SCREAMING_SNAKE_CASE						     =   F'D{source_seq[i - 1]:c}'

     for i in range(1   ,					len_destination_seq + 1		):
          __SCREAMING_SNAKE_CASE						     =   i * insert_cost
          __SCREAMING_SNAKE_CASE						     =   F'I{destination_seq[i - 1]:c}'

     for i in range(1   ,					len_source_seq + 1		):
          for j in range(1   ,					len_destination_seq + 1		):
               if source_seq[i - 1] == destination_seq[j - 1]:
                    __SCREAMING_SNAKE_CASE						     =   costs[i - 1][j - 1] + copy_cost
                    __SCREAMING_SNAKE_CASE						     =   F'C{source_seq[i - 1]:c}'
               else:
                    __SCREAMING_SNAKE_CASE						     =   costs[i - 1][j - 1] + replace_cost
                    __SCREAMING_SNAKE_CASE						     =   F'R{source_seq[i - 1]:c}' + str(destination_seq[j - 1]		)

               if costs[i - 1][j] + delete_cost < costs[i][j]:
                    __SCREAMING_SNAKE_CASE						     =   costs[i - 1][j] + delete_cost
                    __SCREAMING_SNAKE_CASE						     =   F'D{source_seq[i - 1]:c}'

               if costs[i][j - 1] + insert_cost < costs[i][j]:
                    __SCREAMING_SNAKE_CASE						     =   costs[i][j - 1] + insert_cost
                    __SCREAMING_SNAKE_CASE						     =   F'I{destination_seq[j - 1]:c}'

     return costs, ops







def      a__ (						a__   ,					a__   ,					a__		):




     """simple docstring"""


     if i == 0 and j == 0:
          return []
     else:
          if ops[i][j][0] == "C" or ops[i][j][0] == "R":
               __SCREAMING_SNAKE_CASE						     =   assemble_transformation(a__   ,					i - 1   ,					j - 1		)
               seq.append(ops[i][j]		)
               return seq
          elif ops[i][j][0] == "D":
               __SCREAMING_SNAKE_CASE						     =   assemble_transformation(a__   ,					i - 1   ,					a__		)
               seq.append(ops[i][j]		)
               return seq
          else:
               __SCREAMING_SNAKE_CASE						     =   assemble_transformation(a__   ,					a__   ,					j - 1		)
               seq.append(ops[i][j]		)
               return seq


if __name__ == "__main__":
  UpperCAmelCase				: str         =  compute_transform_tables('Python', 'Algorithms', -1, 1, 2, 2)

  UpperCAmelCase				: List[str]         =  len(operations)
  UpperCAmelCase				: Union[str, Any]         =  len(operations[0])
  UpperCAmelCase				: Tuple         =  assemble_transformation(operations, m - 1, n - 1)

  UpperCAmelCase				: Tuple         =  list('Python')
  UpperCAmelCase				: Tuple         =  0
  UpperCAmelCase				: Optional[Any]         =  0

  with open('min_cost.txt', 'w') as file:
    for op in sequence:
      print(''.join(string))

      if op[0] == "C":
        file.write('%-16s' % 'Copy %c' % op[1])
        file.write('\t\t\t' + ''.join(string))
        file.write('\r\n')

        cost -= 1
      elif op[0] == "R":
        UpperCAmelCase				: Any         =  op[2]

        file.write('%-16s' % ('Replace %c' % op[1] + ' with ' + str(op[2])))
        file.write('\t\t' + ''.join(string))
        file.write('\r\n')

        cost += 1
      elif op[0] == "D":
        string.pop(i)

        file.write('%-16s' % 'Delete %c' % op[1])
        file.write('\t\t\t' + ''.join(string))
        file.write('\r\n')

        cost += 2
      else:
        string.insert(i, op[1])

        file.write('%-16s' % 'Insert %c' % op[1])
        file.write('\t\t\t' + ''.join(string))
        file.write('\r\n')

        cost += 2

      i += 1

    print(''.join(string))
    print('Cost: ', cost)

    file.write('\r\nMinimum cost: ' + str(cost))









'''simple docstring'''



import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel







class 		lowerCAmelCase__     (   utils.Parser      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "halfcheetah-medium-expert-v2"
 lowerCAmelCase__						      =   "config.offline"







def      a__ (						a__   ,					a__   ,					a__   ,					a__   ,					a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   utils.load_model(a__   ,					a__   ,					a__   ,					epoch=a__   ,					device=a__		)
     __SCREAMING_SNAKE_CASE						     =   TrajectoryTransformerModel(gpt.config		)

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






