

'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase				: Any         =  logging.get_logger(__name__)

UpperCAmelCase				: Union[str, Any]         =  {
    'facebook/convnext-tiny-224': 'https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json',
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}







class 		lowerCAmelCase__     (   a				,      a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "convnext"
 def __init__(	self      : List[Any]      ,							__SCREAMING_SNAKE_CASE      : List[Any]=3      ,							__SCREAMING_SNAKE_CASE      : int=4      ,							__SCREAMING_SNAKE_CASE      : Optional[int]=4      ,							__SCREAMING_SNAKE_CASE      : str=None      ,							__SCREAMING_SNAKE_CASE      : Dict=None      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]="gelu"      ,							__SCREAMING_SNAKE_CASE      : Dict=0.02      ,							__SCREAMING_SNAKE_CASE      : Tuple=1E-12      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=1E-6      ,							__SCREAMING_SNAKE_CASE      : Any=0.0      ,							__SCREAMING_SNAKE_CASE      : int=224      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=None      ,							__SCREAMING_SNAKE_CASE      : List[str]=None      ,							**__SCREAMING_SNAKE_CASE      : Union[str, Any]      ,							)						->				Tuple:


      """simple docstring"""




      super().__init__(**__SCREAMING_SNAKE_CASE    )

      __SCREAMING_SNAKE_CASE						     =   num_channels
      __SCREAMING_SNAKE_CASE						     =   patch_size
      __SCREAMING_SNAKE_CASE						     =   num_stages
      __SCREAMING_SNAKE_CASE						     =   [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
      __SCREAMING_SNAKE_CASE						     =   [3, 3, 9, 3] if depths is None else depths
      __SCREAMING_SNAKE_CASE						     =   hidden_act
      __SCREAMING_SNAKE_CASE						     =   initializer_range
      __SCREAMING_SNAKE_CASE						     =   layer_norm_eps
      __SCREAMING_SNAKE_CASE						     =   layer_scale_init_value
      __SCREAMING_SNAKE_CASE						     =   drop_path_rate
      __SCREAMING_SNAKE_CASE						     =   image_size
      __SCREAMING_SNAKE_CASE						     =   ["""stem"""] + [f'stage{idx}' for idx in range(1      ,							len(self.depths    ) + 1    )]
      __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   get_aligned_output_features_output_indices(
          out_features=__SCREAMING_SNAKE_CASE      ,							out_indices=__SCREAMING_SNAKE_CASE      ,							stage_names=self.stage_names    )







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   version.parse("1.11"      )
 @property
 def    UpperCAmelCase__	(	self      : Optional[Any]    )						->				Mapping[str, Mapping[int, str]]:


      """simple docstring"""




      return OrderedDict(
          [
              ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
          ]    )


 @property
 def    UpperCAmelCase__	(	self      : Dict    )						->				float:


      """simple docstring"""




      return 1E-5









'''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
  )






