
'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_ :  Dict     =					logging.get_logger(__name__)

snake_case_ :  List[Any]     =					{
    'edbeeching/decision-transformer-gym-hopper-medium': (
        'https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							"""decision_transformer"""
   _snake_case		   =							["""past_key_values"""]
   _snake_case		   =							{
       """max_position_embeddings""": """n_positions""",
       """num_attention_heads""": """n_head""",
       """num_hidden_layers""": """n_layer""",
   }







   def __init__(			self ,   lowerCamelCase__=1_7 ,   lowerCamelCase__=4 ,   lowerCamelCase__=1_2_8 ,   lowerCamelCase__=4_0_9_6 ,   lowerCamelCase__=True ,   lowerCamelCase__=1 ,   lowerCamelCase__=1_0_2_4 ,   lowerCamelCase__=3 ,   lowerCamelCase__=1 ,   lowerCamelCase__=None ,   lowerCamelCase__="relu" ,   lowerCamelCase__=0.1 ,   lowerCamelCase__=0.1 ,   lowerCamelCase__=0.1 ,   lowerCamelCase__=1e-5 ,   lowerCamelCase__=0.02 ,   lowerCamelCase__=True ,   lowerCamelCase__=True ,   lowerCamelCase__=5_0_2_5_6 ,   lowerCamelCase__=5_0_2_5_6 ,   lowerCamelCase__=False ,   lowerCamelCase__=False ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      UpperCamelCase       =  state_dim
      UpperCamelCase       =  act_dim
      UpperCamelCase       =  hidden_size
      UpperCamelCase       =  max_ep_len
      UpperCamelCase       =  action_tanh
      UpperCamelCase       =  vocab_size
      UpperCamelCase       =  n_positions
      UpperCamelCase       =  n_layer
      UpperCamelCase       =  n_head
      UpperCamelCase       =  n_inner
      UpperCamelCase       =  activation_function
      UpperCamelCase       =  resid_pdrop
      UpperCamelCase       =  embd_pdrop
      UpperCamelCase       =  attn_pdrop
      UpperCamelCase       =  layer_norm_epsilon
      UpperCamelCase       =  initializer_range
      UpperCamelCase       =  scale_attn_weights
      UpperCamelCase       =  use_cache
      UpperCamelCase       =  scale_attn_by_inverse_layer_idx
      UpperCamelCase       =  reorder_and_upcast_attn

      UpperCamelCase       =  bos_token_id
      UpperCamelCase       =  eos_token_id

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







'''simple docstring'''


import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp



def  __snake_case     (    ):
   UpperCamelCase       =  ArgumentParser(
       description=(
           '''PyTorch TPU distributed training launch '''
           '''helper utility that will spawn up '''
           '''multiple distributed processes'''
       ))

   # Optional arguments for the launch helper
   parser.add_argument('''--num_cores''',      type=_UpperCAmelCase,      default=1,      help='''Number of TPU cores to use (1 or 8).''')

   # positional
   parser.add_argument(
       '''training_script''',      type=_UpperCAmelCase,      help=(
           '''The full path to the single TPU training '''
           '''program/script to be launched in parallel, '''
           '''followed by all the arguments for the '''
           '''training script'''
       ),      )

   # rest from the training program
   parser.add_argument('''training_script_args''',      nargs=_UpperCAmelCase)

   return parser.parse_args()



def  __snake_case     (    ):
   UpperCamelCase       =  parse_args()

   # Import training_script as a module.
   UpperCamelCase       =  Path(args.training_script)
   sys.path.append(str(script_fpath.parent.resolve()))
   UpperCamelCase       =  script_fpath.stem
   UpperCamelCase       =  importlib.import_module(_UpperCAmelCase)

   # Patch sys.argv
   UpperCamelCase       =  [args.training_script] + args.training_script_args + ['''--tpu_num_cores''', str(args.num_cores)]
   xmp.spawn(mod._mp_fn,      args=(),      nprocs=args.num_cores)


if __name__ == "__main__":
   main()





