





'''simple docstring'''


import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel

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







def 		__a(SCREAMING_SNAKE_CASE_					:   Optional[int]						,    SCREAMING_SNAKE_CASE_					:   int						,    SCREAMING_SNAKE_CASE_					:   Optional[Any]						,    SCREAMING_SNAKE_CASE_					:   int						,    SCREAMING_SNAKE_CASE_					:   Dict      ):



    '''simple docstring'''





    _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__":
       _SCREAMING_SNAKE_CASE			=							Parser().parse_args("plan")
       convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
           args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
       )






'''simple docstring'''


_SCREAMING_SNAKE_CASE			=							{
    "A": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "B": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "C": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "D": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "E": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "F": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "G": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "H": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "I": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "J": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "K": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "L": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "M": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "N": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "O": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "P": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "Q": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "R": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "S": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "T": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "U": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "V": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "W": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "X": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "Y": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
    "Z": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
}


def 		__a(SCREAMING_SNAKE_CASE_					:   str      ):



    '''simple docstring'''





    return [alphabet[char] for char in key.upper()]


def 		__a(SCREAMING_SNAKE_CASE_					:   str						,    SCREAMING_SNAKE_CASE_					:   str      ):



    '''simple docstring'''





    _lowerCAmelCase		     = ""
    _lowerCAmelCase		     = 0
    _lowerCAmelCase		     = generate_table(SCREAMING_SNAKE_CASE_      )
    for char in words.upper():
        cipher += get_opponent(table[count]						,    SCREAMING_SNAKE_CASE_      )
        _lowerCAmelCase		     = (count + 1) % len(SCREAMING_SNAKE_CASE_      )
    return cipher


def 		__a(SCREAMING_SNAKE_CASE_					:   str						,    SCREAMING_SNAKE_CASE_					:   str      ):



    '''simple docstring'''





    return encrypt(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_      )


def 		__a(SCREAMING_SNAKE_CASE_					:   tuple[str, str]						,    SCREAMING_SNAKE_CASE_					:   str      ):



    '''simple docstring'''





    _lowerCAmelCase		     = 0 if char in table[0] else 1
    _lowerCAmelCase		     = table[row].index(SCREAMING_SNAKE_CASE_      )
    return row, col


def 		__a(SCREAMING_SNAKE_CASE_					:   tuple[str, str]						,    SCREAMING_SNAKE_CASE_					:   str      ):



    '''simple docstring'''





    _lowerCAmelCase     ,     _lowerCAmelCase		     = get_position(SCREAMING_SNAKE_CASE_						,    char.upper()      )
    if row == 1:
        return table[0][col]
    else:
        return table[1][col] if row == 0 else char


if __name__ == "__main__":
       import doctest

       doctest.testmod()  # Fist ensure that all our tests are passing...
       _SCREAMING_SNAKE_CASE			=							input("Enter key: ").strip()
       _SCREAMING_SNAKE_CASE			=							input("Enter text to encrypt: ").strip()
       _SCREAMING_SNAKE_CASE			=							encrypt(key, text)

       print(f'''Encrypted: {cipher_text}''')
       print(f'''Decrypted with key: {decrypt(key, cipher_text)}''')


