



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


a_      :      Optional[int]    =     logging.get_logger(__name__)

a_      :      Tuple    =     {
    '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    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 _lowercase   :    int      						=					'''decision_transformer'''
 _lowercase   :    Union[str, Any]      						=					['''past_key_values''']
 _lowercase   :    Dict      						=					{
     '''max_position_embeddings''': '''n_positions''',
     '''num_attention_heads''': '''n_head''',
     '''num_hidden_layers''': '''n_layer''',
 }

 def __init__(	self		,				SCREAMING_SNAKE_CASE=1_7		,				SCREAMING_SNAKE_CASE=4		,				SCREAMING_SNAKE_CASE=1_2_8		,				SCREAMING_SNAKE_CASE=4_0_9_6		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=1		,				SCREAMING_SNAKE_CASE=1_0_2_4		,				SCREAMING_SNAKE_CASE=3		,				SCREAMING_SNAKE_CASE=1		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE="relu"		,				SCREAMING_SNAKE_CASE=0.1		,				SCREAMING_SNAKE_CASE=0.1		,				SCREAMING_SNAKE_CASE=0.1		,				SCREAMING_SNAKE_CASE=1e-5		,				SCREAMING_SNAKE_CASE=0.02		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=5_0_2_5_6		,				SCREAMING_SNAKE_CASE=5_0_2_5_6		,				SCREAMING_SNAKE_CASE=False		,				SCREAMING_SNAKE_CASE=False		,				**SCREAMING_SNAKE_CASE		,				) ->			Any:
  a__												= state_dim
  a__												= act_dim
  a__												= hidden_size
  a__												= max_ep_len
  a__												= action_tanh
  a__												= vocab_size
  a__												= n_positions
  a__												= n_layer
  a__												= n_head
  a__												= n_inner
  a__												= activation_function
  a__												= resid_pdrop
  a__												= embd_pdrop
  a__												= attn_pdrop
  a__												= layer_norm_epsilon
  a__												= initializer_range
  a__												= scale_attn_weights
  a__												= use_cache
  a__												= scale_attn_by_inverse_layer_idx
  a__												= reorder_and_upcast_attn

  a__												= bos_token_id
  a__												= eos_token_id

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










import logging
import os

from .state import PartialState
class    __UpperCamelCase			(       logging.LoggerAdapter				):

 """simple docstring"""



 @staticmethod
 def 						_UpperCAmelCase       (	SCREAMING_SNAKE_CASE      ) ->			Optional[Any]:
  a__												= PartialState()
  return not main_process_only or (main_process_only and state.is_main_process)



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			Optional[int]:
  if PartialState._shared_state == {}:
   raise RuntimeError(
       '''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'''      )
  a__												= kwargs.pop('''main_process_only'''		,				SCREAMING_SNAKE_CASE      )
  a__												= kwargs.pop('''in_order'''		,				SCREAMING_SNAKE_CASE      )

  if self.isEnabledFor(SCREAMING_SNAKE_CASE      ):
   if self._should_log(SCREAMING_SNAKE_CASE      ):
    a__							,			a__												= self.process(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
    self.logger.log(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

   elif in_order:
    a__												= PartialState()
    for i in range(state.num_processes      ):
     if i == state.process_index:
      a__							,			a__												= self.process(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
      self.logger.log(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )
     state.wait_for_everyone()






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase = None       ):
 if log_level is None:
  a__												= os.environ.get('''ACCELERATE_LOG_LEVEL'''							,  __UpperCAmelCase       )
 a__												= logging.getLogger(__UpperCAmelCase       )
 if log_level is not None:
  logger.setLevel(log_level.upper()       )
  logger.root.setLevel(log_level.upper()       )
 return MultiProcessAdapter(__UpperCAmelCase							,  {}       )





