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


__lowercase											=      logging.get_logger(__name__)

__lowercase											=      {
    '''facebook/s2t-wav2vec2-large-en-de''': (
        '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}


class 			lowerCamelCase_  (     UpperCAmelCase_				):






   '''simple docstring'''






   a__			:		str      			=   """speech_to_text_2"""
   a__			:		List[Any]      			=   ["""past_key_values"""]
   a__			:		Optional[int]      			=   {"""num_attention_heads""": """decoder_attention_heads""", """hidden_size""": """d_model"""}







   def __init__(			self				,				__lowercase=10_000				,				__lowercase=6				,				__lowercase=2_048				,				__lowercase=4				,				__lowercase=0.0				,				__lowercase=True				,				__lowercase="relu"				,				__lowercase=256				,				__lowercase=0.1				,				__lowercase=0.0				,				__lowercase=0.0				,				__lowercase=0.02				,				__lowercase=2				,				__lowercase=True				,				__lowercase=1				,				__lowercase=0				,				__lowercase=2				,				__lowercase=1_024				,				**__lowercase				,				) ->			List[str]:
       __UpperCamelCase  :Optional[int]    						=	vocab_size
       __UpperCamelCase  :Dict    						=	d_model
       __UpperCamelCase  :List[str]    						=	decoder_ffn_dim
       __UpperCamelCase  :Union[str, Any]    						=	decoder_layers
       __UpperCamelCase  :List[Any]    						=	decoder_attention_heads
       __UpperCamelCase  :List[Any]    						=	dropout
       __UpperCamelCase  :Optional[int]    						=	attention_dropout
       __UpperCamelCase  :Any    						=	activation_dropout
       __UpperCamelCase  :Tuple    						=	activation_function
       __UpperCamelCase  :Optional[int]    						=	init_std
       __UpperCamelCase  :Optional[int]    						=	decoder_layerdrop
       __UpperCamelCase  :List[Any]    						=	use_cache
       __UpperCamelCase  :List[str]    						=	decoder_layers
       __UpperCamelCase  :List[Any]    						=	scale_embedding  # scale factor will be sqrt(d_model) if True
       __UpperCamelCase  :Dict    						=	max_target_positions

       super().__init__(
           pad_token_id=__lowercase				,				bos_token_id=__lowercase				,				eos_token_id=__lowercase				,				decoder_start_token_id=__lowercase				,				**__lowercase				,				)






import qiskit



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Dict    						=	qiskit.Aer.get_backend('''aer_simulator'''			)

    __UpperCamelCase  :Tuple    						=	qiskit.QuantumCircuit(4						,    2			)
    # encode inputs in qubits 0 and 1
    if bita == 1:
        qc_ha.x(0			)
    if bita == 1:
        qc_ha.x(1			)
    qc_ha.barrier()

    # use cnots to write XOR of the inputs on qubit2
    qc_ha.cx(0						,    2			)
    qc_ha.cx(1						,    2			)

    # use ccx / toffoli gate to write AND of the inputs on qubit3
    qc_ha.ccx(0						,    1						,    3			)
    qc_ha.barrier()

    # extract outputs
    qc_ha.measure(2						,    0			)  # extract XOR value
    qc_ha.measure(3						,    1			)  # extract AND value

    # Execute the circuit on the qasm simulator
    __UpperCamelCase  :Optional[Any]    						=	qiskit.execute(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    shots=1_000			)

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


if __name__ == "__main__":
    __lowercase											=      half_adder(1, 1)
    print(F'Half Adder Output Qubit Counts: {counts}')





