


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


snake_case__      			=    logging.get_logger(__name__)

snake_case__      			=    {
    "CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
        "https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}


class        lowerCAmelCase_			(							lowercase_):
						lowerCamelCase_    =  'trajectory_transformer'
						lowerCamelCase_    =  ['past_key_values']
						lowerCamelCase_    =  {
						    'hidden_size': 'n_embd',
						    'num_attention_heads': 'n_head',
						    'num_hidden_layers': 'n_layer',
						}




						def __init__(						self    : Union[str, Any] ,  __A    : Optional[Any]=100 ,  __A    : Any=5 ,  __A    : int=1 ,  __A    : Optional[Any]=1 ,  __A    : str=249 ,  __A    : Dict=6 ,  __A    : Optional[Any]=17 ,  __A    : Optional[int]=25 ,  __A    : int=4 ,  __A    : Dict=4 ,  __A    : List[str]=128 ,  __A    : List[str]=0.1 ,  __A    : Any=0.1 ,  __A    : Dict=0.1 ,  __A    : Tuple=0.0_006 ,  __A    : List[str]=512 ,  __A    : Union[str, Any]=0.02 ,  __A    : List[str]=1E-12 ,  __A    : List[str]=1 ,  __A    : List[Any]=True ,  __A    : Dict=1 ,  __A    : Optional[int]=50256 ,  __A    : List[Any]=50256 ,  **__A    : str ,  )   ->Optional[int]:






									"""simple docstring"""






									a__			:Tuple    	=     vocab_size
									a__			:Union[str, Any]    	=     action_weight
									a__			:List[Any]    	=     reward_weight
									a__			:str    	=     value_weight
									a__			:Optional[int]    	=     max_position_embeddings
									a__			:int    	=     block_size
									a__			:List[str]    	=     action_dim
									a__			:List[str]    	=     observation_dim
									a__			:int    	=     transition_dim
									a__			:Optional[Any]    	=     learning_rate
									a__			:Tuple    	=     n_layer
									a__			:List[Any]    	=     n_head
									a__			:Union[str, Any]    	=     n_embd
									a__			:Union[str, Any]    	=     embd_pdrop
									a__			:Any    	=     attn_pdrop
									a__			:Dict    	=     resid_pdrop
									a__			:Any    	=     initializer_range
									a__			:List[Any]    	=     layer_norm_eps
									a__			:List[Any]    	=     kaiming_initializer_range
									a__			:List[Any]    	=     use_cache
									super().__init__(pad_token_id=UpperCamelCase_ ,  bos_token_id=UpperCamelCase_ ,  eos_token_id=UpperCamelCase_ ,  **UpperCamelCase_    )










from __future__ import annotations



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      list[int]      ,					__lowercase :      int     )    ->   bool:





    """simple docstring"""

    if len(__lowercase     ) == 0:
        return False
    __A       =						len(__lowercase     ) // 2
    if a_list[midpoint] == item:
        return True
    if item < a_list[midpoint]:
        return binary_search(a_list[:midpoint]      ,					__lowercase     )
    else:
        return binary_search(a_list[midpoint + 1 :]      ,					__lowercase     )


if __name__ == "__main__":
  __a   :							Tuple						= input("Enter numbers separated by comma:\n").strip()
  __a   :							Any						= [int(item.strip()) for item in user_input.split(",")]
  __a   :							List[Any]						= int(input("Enter the number to be found in the list:\n").strip())
  __a   :							Optional[int]						= "" if binary_search(sequence, target) else "not "
  print(f"""{target} was {not_str}found in {sequence}""")




