

'''simple docstring'''



from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


UpperCAmelCase				: str         =  logging.get_logger(__name__)

UpperCAmelCase				: str         =  {
    'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
    'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
    'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
    'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
    'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
}







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "gpt2"
 lowerCAmelCase__						      =   ["past_key_values"]
 lowerCAmelCase__						      =   {
     "hidden_size": "n_embd",
     "max_position_embeddings": "n_positions",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }
 def __init__(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=50_257      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=1_024      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=768      ,							__SCREAMING_SNAKE_CASE      : Tuple=12      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=12      ,							__SCREAMING_SNAKE_CASE      : Tuple=None      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]="gelu_new"      ,							__SCREAMING_SNAKE_CASE      : List[str]=0.1      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=0.1      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=0.1      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]=1E-5      ,							__SCREAMING_SNAKE_CASE      : Dict=0.02      ,							__SCREAMING_SNAKE_CASE      : Dict="cls_index"      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]=True      ,							__SCREAMING_SNAKE_CASE      : int=None      ,							__SCREAMING_SNAKE_CASE      : Any=True      ,							__SCREAMING_SNAKE_CASE      : int=0.1      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=True      ,							__SCREAMING_SNAKE_CASE      : Any=True      ,							__SCREAMING_SNAKE_CASE      : List[str]=50_256      ,							__SCREAMING_SNAKE_CASE      : List[str]=50_256      ,							__SCREAMING_SNAKE_CASE      : Any=False      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]=False      ,							**__SCREAMING_SNAKE_CASE      : Optional[int]      ,							)						->				Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   vocab_size
      __SCREAMING_SNAKE_CASE						     =   n_positions
      __SCREAMING_SNAKE_CASE						     =   n_embd
      __SCREAMING_SNAKE_CASE						     =   n_layer
      __SCREAMING_SNAKE_CASE						     =   n_head
      __SCREAMING_SNAKE_CASE						     =   n_inner
      __SCREAMING_SNAKE_CASE						     =   activation_function
      __SCREAMING_SNAKE_CASE						     =   resid_pdrop
      __SCREAMING_SNAKE_CASE						     =   embd_pdrop
      __SCREAMING_SNAKE_CASE						     =   attn_pdrop
      __SCREAMING_SNAKE_CASE						     =   layer_norm_epsilon
      __SCREAMING_SNAKE_CASE						     =   initializer_range
      __SCREAMING_SNAKE_CASE						     =   summary_type
      __SCREAMING_SNAKE_CASE						     =   summary_use_proj
      __SCREAMING_SNAKE_CASE						     =   summary_activation
      __SCREAMING_SNAKE_CASE						     =   summary_first_dropout
      __SCREAMING_SNAKE_CASE						     =   summary_proj_to_labels
      __SCREAMING_SNAKE_CASE						     =   scale_attn_weights
      __SCREAMING_SNAKE_CASE						     =   use_cache
      __SCREAMING_SNAKE_CASE						     =   scale_attn_by_inverse_layer_idx
      __SCREAMING_SNAKE_CASE						     =   reorder_and_upcast_attn

      __SCREAMING_SNAKE_CASE						     =   bos_token_id
      __SCREAMING_SNAKE_CASE						     =   eos_token_id

      super().__init__(bos_token_id=__SCREAMING_SNAKE_CASE      ,							eos_token_id=__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE    )







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 def __init__(	self      : Optional[Any]      ,							__SCREAMING_SNAKE_CASE      : PretrainedConfig      ,							__SCREAMING_SNAKE_CASE      : str = "default"      ,							__SCREAMING_SNAKE_CASE      : List[PatchingSpec] = None      ,							__SCREAMING_SNAKE_CASE      : bool = False      ,							)						->				Optional[int]:


      """simple docstring"""




      super().__init__(__SCREAMING_SNAKE_CASE      ,							task=__SCREAMING_SNAKE_CASE      ,							patching_specs=__SCREAMING_SNAKE_CASE      ,							use_past=__SCREAMING_SNAKE_CASE    )
      if not getattr(self._config      ,							"""pad_token_id"""      ,							__SCREAMING_SNAKE_CASE    ):
           # TODO: how to do that better?
           __SCREAMING_SNAKE_CASE						     =   0
 @property
 def    UpperCAmelCase__	(	self      : Dict    )						->				Mapping[str, Mapping[int, str]]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}    )
      if self.use_past:
           self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE      ,							direction="""inputs"""    )
           __SCREAMING_SNAKE_CASE						     =   {0: """batch""", 1: """past_sequence + sequence"""}
      else:
           __SCREAMING_SNAKE_CASE						     =   {0: """batch""", 1: """sequence"""}

      return common_inputs
 @property
 def    UpperCAmelCase__	(	self      : Optional[int]    )						->				int:


      """simple docstring"""




      return self._config.n_layer
 @property
 def    UpperCAmelCase__	(	self      : str    )						->				int:


      """simple docstring"""




      return self._config.n_head
 def    UpperCAmelCase__	(	self      : str      ,							__SCREAMING_SNAKE_CASE      : PreTrainedTokenizer      ,							__SCREAMING_SNAKE_CASE      : int = -1      ,							__SCREAMING_SNAKE_CASE      : int = -1      ,							__SCREAMING_SNAKE_CASE      : bool = False      ,							__SCREAMING_SNAKE_CASE      : Optional[TensorType] = None      ,							)						->				Mapping[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   super(__SCREAMING_SNAKE_CASE      ,							self    ).generate_dummy_inputs(
          __SCREAMING_SNAKE_CASE      ,							batch_size=__SCREAMING_SNAKE_CASE      ,							seq_length=__SCREAMING_SNAKE_CASE      ,							is_pair=__SCREAMING_SNAKE_CASE      ,							framework=__SCREAMING_SNAKE_CASE    )

      # We need to order the input in the way they appears in the forward()
      __SCREAMING_SNAKE_CASE						     =   OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}    )

      # Need to add the past_keys
      if self.use_past:
           if not is_torch_available():
                raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""    )
           else:
                import torch

                __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   common_inputs["""input_ids"""].shape
                # Not using the same length for past_key_values
                __SCREAMING_SNAKE_CASE						     =   seqlen + 2
                __SCREAMING_SNAKE_CASE						     =   (
                    batch,
                    self.num_attention_heads,
                    past_key_values_length,
                    self._config.hidden_size // self.num_attention_heads,
                )
                __SCREAMING_SNAKE_CASE						     =   [
                    (torch.zeros(__SCREAMING_SNAKE_CASE    ), torch.zeros(__SCREAMING_SNAKE_CASE    )) for _ in range(self.num_layers    )
                ]

      __SCREAMING_SNAKE_CASE						     =   common_inputs["""attention_mask"""]
      if self.use_past:
           __SCREAMING_SNAKE_CASE						     =   ordered_inputs["""attention_mask"""].dtype
           __SCREAMING_SNAKE_CASE						     =   torch.cat(
               [ordered_inputs["""attention_mask"""], torch.ones(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							dtype=__SCREAMING_SNAKE_CASE    )]      ,							dim=1    )

      return ordered_inputs


 @property
 def    UpperCAmelCase__	(	self      : Optional[Any]    )						->				int:


      """simple docstring"""




      return 13









'''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
  )






