




"""simple docstring"""







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


SCREAMING_SNAKE_CASE_	     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_	     =       {
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}

class 				snake_case_			(					lowerCamelCase_			):







   """simple docstring"""




   A_				      =       '''pegasus'''
   A_				      =       ['''past_key_values''']
   A_				      =       {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}







   def __init__(		self       ,					lowerCamelCase_=5_0_2_6_5       ,					lowerCamelCase_=1_0_2_4       ,					lowerCamelCase_=1_2       ,					lowerCamelCase_=4_0_9_6       ,					lowerCamelCase_=1_6       ,					lowerCamelCase_=1_2       ,					lowerCamelCase_=4_0_9_6       ,					lowerCamelCase_=1_6       ,					lowerCamelCase_=0.0       ,					lowerCamelCase_=0.0       ,					lowerCamelCase_=True       ,					lowerCamelCase_=True       ,					lowerCamelCase_="gelu"       ,					lowerCamelCase_=1_0_2_4       ,					lowerCamelCase_=0.1       ,					lowerCamelCase_=0.0       ,					lowerCamelCase_=0.0       ,					lowerCamelCase_=0.02       ,					lowerCamelCase_=0       ,					lowerCamelCase_=False       ,					lowerCamelCase_=0       ,					lowerCamelCase_=1       ,					lowerCamelCase_=1       ,					**lowerCamelCase_       ,					)				->   Tuple:
       UpperCamelCase              =	vocab_size
       UpperCamelCase              =	max_position_embeddings
       UpperCamelCase              =	d_model
       UpperCamelCase              =	encoder_ffn_dim
       UpperCamelCase              =	encoder_layers
       UpperCamelCase              =	encoder_attention_heads
       UpperCamelCase              =	decoder_ffn_dim
       UpperCamelCase              =	decoder_layers
       UpperCamelCase              =	decoder_attention_heads
       UpperCamelCase              =	dropout
       UpperCamelCase              =	attention_dropout
       UpperCamelCase              =	activation_dropout
       UpperCamelCase              =	activation_function
       UpperCamelCase              =	init_std
       UpperCamelCase              =	encoder_layerdrop
       UpperCamelCase              =	decoder_layerdrop
       UpperCamelCase              =	use_cache
       UpperCamelCase              =	encoder_layers
       UpperCamelCase              =	scale_embedding  # scale factor will be sqrt(d_model) if True
       super().__init__(
           pad_token_id=lowerCamelCase_       ,					eos_token_id=lowerCamelCase_       ,					is_encoder_decoder=lowerCamelCase_       ,					decoder_start_token_id=lowerCamelCase_       ,					forced_eos_token_id=lowerCamelCase_       ,					**lowerCamelCase_       ,					)







   @property
   def   UpperCAmelCase__			(		self)				->   int:
       return self.encoder_attention_heads







   @property
   def   UpperCAmelCase__			(		self)				->   int:
       return self.d_model






import qiskit





def  __magic_name__  (				lowercase				,       lowercase				)				-> qiskit.result.counts.Counts:

							"""simple docstring"""



							lowercase_	:   Dict  				=     qiskit.Aer.get_backend("""aer_simulator"""				)

							# Create a Quantum Circuit acting on the q register
							lowercase_	:   str  				=     qiskit.QuantumCircuit(lowercase				,       lowercase				)

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

							# Execute the circuit on the simulator
							lowercase_	:   List[Any]  				=     qiskit.execute(lowercase				,       lowercase				,       shots=1000				)

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


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