"""simple docstring"""




import qiskit





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	int      ,_lowerCamelCase						:	int     ):
  _lowerCAmelCase						:	Union[str, Any]        =					qiskit.Aer.get_backend("""aer_simulator"""     )

  # Create a Quantum Circuit acting on the q register
  _lowerCAmelCase						:	int        =					qiskit.QuantumCircuit(_lowerCamelCase      ,_lowerCamelCase     )

  # Apply X (NOT) Gate to Qubits 0 & 1
  circuit.x(0     )
  circuit.x(1     )

  # Map the quantum measurement to the classical bits
  circuit.measure([0, 1]      ,[0, 1]     )

  # Execute the circuit on the qasm simulator
  _lowerCAmelCase						:	Optional[Any]        =					qiskit.execute(_lowerCamelCase      ,_lowerCamelCase      ,shots=1000     )

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


if __name__ == "__main__":
     _a				:		Union[str, Any]	   =       single_qubit_measure(2, 2)
     print(F"""Total count for various states are: {counts}""")





"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_a				:		List[Any]	   =       logging.get_logger(__name__)
class 						__A							(			SCREAMING_SNAKE_CASE_   ,     SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Tuple		 =		"maskformer-swin"

     _UpperCamelCase							:      Union[str, Any]		 =		{
         "num_attention_heads": "num_heads",
         "num_hidden_layers": "num_layers",
     }






     def __init__(		self    ,					a__=224    ,					a__=4    ,					a__=3    ,					a__=96    ,					a__=[2, 2, 6, 2]    ,					a__=[3, 6, 12, 24]    ,					a__=7    ,					a__=4.0    ,					a__=True    ,					a__=0.0    ,					a__=0.0    ,					a__=0.1    ,					a__="gelu"    ,					a__=False    ,					a__=0.0_2    ,					a__=1e-5    ,					a__=None    ,					a__=None    ,					**a__    ,					):
       super().__init__(**a__  )

       _lowerCAmelCase						:	Dict        =					image_size
       _lowerCAmelCase						:	List[str]        =					patch_size
       _lowerCAmelCase						:	Any        =					num_channels
       _lowerCAmelCase						:	int        =					embed_dim
       _lowerCAmelCase						:	Optional[Any]        =					depths
       _lowerCAmelCase						:	List[str]        =					len(a__  )
       _lowerCAmelCase						:	List[Any]        =					num_heads
       _lowerCAmelCase						:	Tuple        =					window_size
       _lowerCAmelCase						:	List[Any]        =					mlp_ratio
       _lowerCAmelCase						:	Optional[Any]        =					qkv_bias
       _lowerCAmelCase						:	int        =					hidden_dropout_prob
       _lowerCAmelCase						:	Union[str, Any]        =					attention_probs_dropout_prob
       _lowerCAmelCase						:	Any        =					drop_path_rate
       _lowerCAmelCase						:	Optional[Any]        =					hidden_act
       _lowerCAmelCase						:	Tuple        =					use_absolute_embeddings
       _lowerCAmelCase						:	str        =					layer_norm_eps
       _lowerCAmelCase						:	Any        =					initializer_range
       # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       _lowerCAmelCase						:	Union[str, Any]        =					int(embed_dim * 2 ** (len(a__  ) - 1)  )
       _lowerCAmelCase						:	int        =					["""stem"""] + [F"stage{idx}" for idx in range(1    ,					len(a__  ) + 1  )]
       _lowerCAmelCase      ,     _lowerCAmelCase						:	int        =					get_aligned_output_features_output_indices(
           out_features=a__    ,					out_indices=a__    ,					stage_names=self.stage_names  )




