




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_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )							->						int:

    """simple docstring"""


    __UpperCamelCase		,   __UpperCamelCase			=    utils.load_model(lowercase_       ,  lowercase_       ,  lowercase_       ,  epoch=lowercase_       ,  device=lowercase_   )
    __UpperCamelCase			=    TrajectoryTransformerModel(gpt.config   )

    trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()   )
    __UpperCamelCase			=    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
 )













"""simple docstring"""






from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_      =				logging.get_logger(__name__)

lowerCAmelCase_      =				{
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}



class 						lowerCAmelCase       (   snake_case				):
       lowerCAmelCase__    			=	"""prophetnet"""
       lowerCAmelCase__    			=	["""past_key_values"""]
       lowerCAmelCase__    			=	{
           """num_attention_heads""": """num_encoder_attention_heads""",
       }






       def __init__( self     ,	a__ = 0.1     ,	a__ = "gelu"     ,	a__ = 3_05_22     ,	a__ = 10_24     ,	a__ = 40_96     ,	a__ = 12     ,	a__ = 16     ,	a__ = 40_96     ,	a__ = 12     ,	a__ = 16     ,	a__ = 0.1     ,	a__ = 0.1     ,	a__ = 5_12     ,	a__ = 0.02     ,	a__ = True     ,	a__ = True     ,	a__ = 0     ,	a__ = 2     ,	a__ = 32     ,	a__ = 1_28     ,	a__ = False     ,	a__ = 0.0     ,	a__ = True     ,	a__ = 0     ,	a__ = 1     ,	a__ = 2     ,	**a__     ,	):
              _UpperCAmelCase			     =  vocab_size
              _UpperCAmelCase			     =  hidden_size
              _UpperCAmelCase			     =  encoder_ffn_dim
              _UpperCAmelCase			     =  num_encoder_layers
              _UpperCAmelCase			     =  num_encoder_attention_heads
              _UpperCAmelCase			     =  decoder_ffn_dim
              _UpperCAmelCase			     =  num_decoder_layers
              _UpperCAmelCase			     =  num_decoder_attention_heads
              _UpperCAmelCase			     =  max_position_embeddings
              _UpperCAmelCase			     =  init_std  # Normal(0, this parameter)
              _UpperCAmelCase			     =  activation_function

              # parameters for prophetnet
              _UpperCAmelCase			     =  ngram
              _UpperCAmelCase			     =  num_buckets
              _UpperCAmelCase			     =  relative_max_distance
              _UpperCAmelCase			     =  disable_ngram_loss
              _UpperCAmelCase			     =  eps

              # 3 Types of Dropout
              _UpperCAmelCase			     =  attention_dropout
              _UpperCAmelCase			     =  activation_dropout
              _UpperCAmelCase			     =  dropout

              _UpperCAmelCase			     =  use_cache

              super().__init__(
                  pad_token_id=a__     ,	bos_token_id=a__     ,	eos_token_id=a__     ,	is_encoder_decoder=a__     ,	add_cross_attention=a__     ,	decoder_start_token_id=a__     ,	**a__     ,	)






       @property
       def 				__A			( self  ):
              return self.num_encoder_layers + self.num_decoder_layers






       @num_hidden_layers.setter
       def 				__A			( self     ,	a__  ):
              raise NotImplementedError(
                  'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
                  ' `num_decoder_layers`.'  )






