code: 
  
  
  """simple docstring"""
  import logging
  from pathlib import Path
  
  import numpy as np
  import pytorch_lightning as pl
  import torch
  from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
  from pytorch_lightning.utilities import rank_zero_only
  
  from utils import save_json
  
  def 							_snake_case			(      __snake_case       :			List[str]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Dict      =		filter(lambda __snake_case      : p.requires_grad	,   model.parameters()      )
     _lowerCamelCase  :				Dict      =		sum([np.prod(p.size()      ) for p in model_parameters]      )
     return params
  
  
  UpperCAmelCase   =	logging.getLogger(__name__)
  
  
  
  
  class   lowercase__							(				pl.Callback							):
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     Any:
        _lowerCamelCase  :				List[str]      =		{F'lr_group_{i}': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups)}
        pl_module.logger.log_metrics(_lowerCAmelCase)
  
  
  
  
  
     @rank_zero_only
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE=True)     ->     List[Any]:
        logger.info(F'***** {type_path} results at step {trainer.global_step:05d} *****')
        _lowerCamelCase  :				int      =		trainer.callback_metrics
        trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["""log""", """progress_bar""", """preds"""]})
        # Log results
        _lowerCamelCase  :				List[str]      =		Path(pl_module.hparams.output_dir)
        if type_path == "test":
           _lowerCamelCase  :				Any      =		od / """test_results.txt"""
           _lowerCamelCase  :				Tuple      =		od / """test_generations.txt"""
        else:
           # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
           # If people want this it will be easy enough to add back.
           _lowerCamelCase  :				Any      =		od / F'{type_path}_results/{trainer.global_step:05d}.txt'
           _lowerCamelCase  :				int      =		od / F'{type_path}_generations/{trainer.global_step:05d}.txt'
           results_file.parent.mkdir(exist_ok=_lowerCAmelCase)
           generations_file.parent.mkdir(exist_ok=_lowerCAmelCase)
        with open(_lowerCAmelCase			,      """a+""") as writer:
           for key in sorted(_lowerCAmelCase):
              if key in ["log", "progress_bar", "preds"]:
                 continue
              _lowerCamelCase  :				Dict      =		metrics[key]
              if isinstance(_lowerCAmelCase			,      torch.Tensor):
                 _lowerCamelCase  :				Union[str, Any]      =		val.item()
              _lowerCamelCase  :				List[str]      =		F'{key}: {val:.6f}\n'
              writer.write(_lowerCAmelCase)
  
        if not save_generations:
           return
  
        if "preds" in metrics:
           _lowerCamelCase  :				List[Any]      =		"""\n""".join(metrics["""preds"""])
           generations_file.open("""w+""").write(_lowerCAmelCase)
  
  
  
  
  
     @rank_zero_only
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     Optional[int]:
        try:
           _lowerCamelCase  :				Tuple      =		pl_module.model.model.num_parameters()
        except AttributeError:
           _lowerCamelCase  :				Dict      =		pl_module.model.num_parameters()
  
        _lowerCamelCase  :				Tuple      =		count_trainable_parameters(_lowerCAmelCase)
        # mp stands for million parameters
        trainer.logger.log_metrics({"""n_params""": npars, """mp""": npars / 1e6, """grad_mp""": n_trainable_pars / 1e6})
  
  
  
  
  
     @rank_zero_only
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     List[str]:
        save_json(pl_module.metrics			,      pl_module.metrics_save_path)
        return self._write_logs(_lowerCAmelCase			,      _lowerCAmelCase			,      """test""")
  
  
  
  
  
     @rank_zero_only
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     Optional[Any]:
        save_json(pl_module.metrics			,      pl_module.metrics_save_path)
        # Uncommenting this will save val generations
        # return self._write_logs(trainer, pl_module, "valid")
  
  
  
  
  
  
  
  def 							_snake_case			(      __snake_case       :			List[str]	,   __snake_case       :			int	,   __snake_case       :			List[str]=1	,   __snake_case       :			Optional[Any]=False      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     if metric == "rouge2":
        _lowerCamelCase  :				int      =		"""{val_avg_rouge2:.4f}-{step_count}"""
     elif metric == "bleu":
        _lowerCamelCase  :				Tuple      =		"""{val_avg_bleu:.4f}-{step_count}"""
     elif metric == "loss":
        _lowerCamelCase  :				Union[str, Any]      =		"""{val_avg_loss:.4f}-{step_count}"""
     else:
        raise NotImplementedError(
            F'seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to'
            """ this function."""      )
  
     _lowerCamelCase  :				Any      =		ModelCheckpoint(
         dirpath=__lowerCamelCase	,   filename=__lowerCamelCase	,   monitor=F'val_{metric}'	,   mode="""min""" if """loss""" in metric else """max"""	,   save_top_k=__lowerCamelCase	,   )
     return checkpoint_callback
  
  
  
  
  
  def 							_snake_case			(      __snake_case       :			Any	,   __snake_case       :			List[Any]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return EarlyStopping(
         monitor=F'val_{metric}'	,   mode="""min""" if """loss""" in metric else """max"""	,   patience=__lowerCamelCase	,   verbose=__lowerCamelCase	,   )
  
  
style_context: 
  
  
  """simple docstring"""
  import torch
  import trajectory.utils as utils
  
  from transformers import TrajectoryTransformerModel
  
  
  
  
  class   lowercase__							(				utils.Parser							):
     __UpperCAmelCase       					=				"halfcheetah-medium-expert-v2"
     __UpperCAmelCase       					=				"config.offline"
  
  
  
  
  
  
  
  def 							_snake_case			(      __snake_case       :			Any	,   __snake_case       :			Optional[Any]	,   __snake_case       :			Optional[int]	,   __snake_case       :			List[str]	,   __snake_case       :			Union[str, Any]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase      ,						_lowerCamelCase  :				int      =		utils.load_model(__snake_case	,   __snake_case	,   __snake_case	,   epoch=__snake_case	,   device=__snake_case      )
     _lowerCamelCase  :				List[str]      =		TrajectoryTransformerModel(gpt.config      )
  
     trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()      )
     _lowerCamelCase  :				str      =		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   =	Parser().parse_args("""plan""")
   convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
       args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
   )
  
  