

'''simple docstring'''



import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


UpperCAmelCase				: int         =  1_0_0_0_0_0_0_0_0_0_0_0
UpperCAmelCase				: Dict         =  1_0_0_0_0

UpperCAmelCase     ,  UpperCAmelCase				: Optional[int]         =  os.path.split(__file__)
UpperCAmelCase				: Dict         =  os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))







def      a__ (						a__   ,					a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   pa.Table.from_pydict({"""col""": [0] * chunk_size}		)
     __SCREAMING_SNAKE_CASE						     =   pa.concat_tables([table] * (num_examples // chunk_size)		)
     return datasets.Dataset(a__   ,					fingerprint="""table_100B"""		)







@dataclass
class 		lowerCAmelCase__     :



 """simple docstring"""



 lowerCAmelCase__						      =   42
 lowerCAmelCase__						      =   42
 lowerCAmelCase__						      =   42
 lowerCAmelCase__						      =   42
 def    UpperCAmelCase__	(	self      : str    )						->				Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   np.random.default_rng(self.seed    )
      __SCREAMING_SNAKE_CASE						     =   rng.integers(low=self.low      ,							high=self.high      ,							size=self.size    ).tolist()
 def __iter__(	self      : List[Any]    )						->				int:


      """simple docstring"""




      return iter(self._sampled_values    )


 def __len__(	self      : Any    )						->				Any:


      """simple docstring"""




      return self.size







@get_duration
def      a__ (						a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   dataset[0]







@get_duration
def      a__ (						a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   dataset[-1]







@get_duration
def      a__ (						a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   dataset[range(len(a__		) // 2   ,					len(a__		) // 2 + 10_24		)]







@get_duration
def      a__ (						a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   dataset[RandIter(0   ,					len(a__		)   ,					10_24   ,					seed=42		)]







def      a__ (						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   {"""num examples""": SPEED_TEST_N_EXAMPLES}
     __SCREAMING_SNAKE_CASE						     =   (get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
     print("""generating dataset"""		)
     __SCREAMING_SNAKE_CASE						     =   generate_100B_dataset(num_examples=a__   ,					chunk_size=a__		)
     print("""Functions"""		)
     for func in functions:
          print(func.__name__		)
          __SCREAMING_SNAKE_CASE						     =   func(a__		)

     with open(a__   ,					"""wb"""		) as f:
          f.write(json.dumps(a__		).encode("""utf-8"""		)		)


if __name__ == "__main__":  # useful to run the profiler
  benchmark_table_100B()









'''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
  )






