'''simple docstring'''

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


__a:			Union[str, Any]	       =       logging.get_logger(__name__)

__a:			str	       =       {
    """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       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   "trajectory_transformer"
       SCREAMING_SNAKE_CASE 		=   ["past_key_values"]
       SCREAMING_SNAKE_CASE 		=   {
           "hidden_size": "n_embd",
           "num_attention_heads": "n_head",
           "num_hidden_layers": "n_layer",
       }

       def __init__(						self     ,       __lowerCAmelCase=100     ,       __lowerCAmelCase=5     ,       __lowerCAmelCase=1     ,       __lowerCAmelCase=1     ,       __lowerCAmelCase=249     ,       __lowerCAmelCase=6     ,       __lowerCAmelCase=17     ,       __lowerCAmelCase=25     ,       __lowerCAmelCase=4     ,       __lowerCAmelCase=4     ,       __lowerCAmelCase=128     ,       __lowerCAmelCase=0.1     ,       __lowerCAmelCase=0.1     ,       __lowerCAmelCase=0.1     ,       __lowerCAmelCase=0.0_0_0_6     ,       __lowerCAmelCase=512     ,       __lowerCAmelCase=0.0_2     ,       __lowerCAmelCase=1E-12     ,       __lowerCAmelCase=1     ,       __lowerCAmelCase=True     ,       __lowerCAmelCase=1     ,       __lowerCAmelCase=50256     ,       __lowerCAmelCase=50256     ,       **__lowerCAmelCase     ,       )  ->  Union[str, Any]:
        lowercase__    :   Optional[int]						= vocab_size
        lowercase__    :   List[Any]						= action_weight
        lowercase__    :   Any						= reward_weight
        lowercase__    :   Tuple						= value_weight
        lowercase__    :   List[Any]						= max_position_embeddings
        lowercase__    :   Tuple						= block_size
        lowercase__    :   Optional[Any]						= action_dim
        lowercase__    :   List[Any]						= observation_dim
        lowercase__    :   List[str]						= transition_dim
        lowercase__    :   int						= learning_rate
        lowercase__    :   int						= n_layer
        lowercase__    :   Tuple						= n_head
        lowercase__    :   int						= n_embd
        lowercase__    :   List[str]						= embd_pdrop
        lowercase__    :   Optional[Any]						= attn_pdrop
        lowercase__    :   List[str]						= resid_pdrop
        lowercase__    :   str						= initializer_range
        lowercase__    :   Optional[int]						= layer_norm_eps
        lowercase__    :   Optional[Any]						= kaiming_initializer_range
        lowercase__    :   int						= use_cache
        super().__init__(pad_token_id=__lowerCAmelCase     ,       bos_token_id=__lowerCAmelCase     ,       eos_token_id=__lowerCAmelCase     ,       **__lowerCAmelCase			)


'''simple docstring'''

import qiskit
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Dict						= qiskit.Aer.get_backend('''aer_simulator'''					)

 # Create a Quantum Circuit acting on the q register
 lowercase__    :   Any						= qiskit.QuantumCircuit(UpperCAmelCase		,					UpperCAmelCase					)

 # Map the quantum measurement to the classical bits
 circuit.measure([0]		,					[0]					)

 # Execute the circuit on the simulator
 lowercase__    :   Any						= qiskit.execute(UpperCAmelCase		,					UpperCAmelCase		,					shots=1000					)

 # Return the histogram data of the results of the experiment.
 return job.result().get_counts(UpperCAmelCase					)


if __name__ == "__main__":
  print(F'Total count for various states are: {single_qubit_measure(1, 1)}')

