


"""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	,   )





"""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
 )

