"""simple docstring"""





import numpy as np
import qiskit





def     a_					(				_lowerCAmelCase  :			str       ,			_lowerCAmelCase  :			int					):





       '''simple docstring'''

       lowercase__      :		int          =     qiskit.QuantumCircuit(num_qubits + 1					)

       # First, let's deal with the case in which oracle is balanced
       if case == "balanced":
              # First generate a random number that tells us which CNOTs to
              # wrap in X-gates:
              lowercase__      :		Union[str, Any]          =     np.random.randint(1       ,			2**num_qubits					)
              # Next, format 'b' as a binary string of length 'n', padded with zeros:
              lowercase__      :		Dict          =     format(_lowerCAmelCase       ,			f"""0{num_qubits}b"""					)
              # Next, we place the first X-gates. Each digit in our binary string
              # corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
              # we apply an X-gate to that qubit:
              for index, bit in enumerate(_lowerCAmelCase					):
                     if bit == "1":
                            oracle_qc.x(_lowerCAmelCase					)
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
              for index in range(_lowerCAmelCase					):
                     oracle_qc.cx(_lowerCAmelCase       ,			_lowerCAmelCase					)
              # Next, place the final X-gates
              for index, bit in enumerate(_lowerCAmelCase					):
                     if bit == "1":
                            oracle_qc.x(_lowerCAmelCase					)

    # Case in which oracle is constant
       if case == "constant":
              # First decide what the fixed output of the oracle will be
              # (either always 0 or always 1)
              lowercase__      :		Optional[Any]          =     np.random.randint(2					)
              if output == 1:
                     oracle_qc.x(_lowerCAmelCase					)

       lowercase__      :		str          =     oracle_qc.to_gate()
       lowercase__      :		str          =     'Oracle'  # To show when we display the circuit
       return oracle_gate





def     a_					(				_lowerCAmelCase  :			qiskit.QuantumCircuit       ,			_lowerCAmelCase  :			int					):





       '''simple docstring'''

       lowercase__      :		Dict          =     qiskit.QuantumCircuit(num_qubits + 1       ,			_lowerCAmelCase					)
       # Set up the output qubit:
       dj_circuit.x(_lowerCAmelCase					)
       dj_circuit.h(_lowerCAmelCase					)
       # And set up the input register:
       for qubit in range(_lowerCAmelCase					):
              dj_circuit.h(_lowerCAmelCase					)
       # Let's append the oracle gate to our circuit:
       dj_circuit.append(_lowerCAmelCase       ,			range(num_qubits + 1					)					)
       # Finally, perform the H-gates again and measure:
       for qubit in range(_lowerCAmelCase					):
              dj_circuit.h(_lowerCAmelCase					)

       for i in range(_lowerCAmelCase					):
              dj_circuit.measure(_lowerCAmelCase       ,			_lowerCAmelCase					)

       return dj_circuit





def     a_					(				_lowerCAmelCase  :			str       ,			_lowerCAmelCase  :			int					):





       '''simple docstring'''

       lowercase__      :		int          =     qiskit.Aer.get_backend('aer_simulator'					)

       lowercase__      :		Optional[Any]          =     dj_oracle(_lowerCAmelCase       ,			_lowerCAmelCase					)
       lowercase__      :		List[str]          =     dj_algorithm(_lowerCAmelCase       ,			_lowerCAmelCase					)

       # Execute the circuit on the simulator
       lowercase__      :		List[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__":
       print(f'''Deutsch Jozsa - Constant Oracle: {deutsch_jozsa("constant", 3)}''')
       print(f'''Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa("balanced", 3)}''')


"""simple docstring"""





import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_UpperCamelCase       :						Optional[int]     =   logging.get_logger(__name__)

_UpperCamelCase       :						Any     =   {
    "shi-labs/oneformer_ade20k_swin_tiny": (
        "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}


class 					UpperCAmelCase_     (   _a):
      lowerCamelCase__		:   Optional[Any]			  =  "oneformer"
      lowerCamelCase__		:   Union[str, Any]			  =  {"hidden_size": "hidden_dim"}


      def __init__(       self		,      a = None		,      a = 2_5_5		,      a = 1_5_0		,      a = 0.1		,      a = 2.0		,      a = 5.0		,      a = 5.0		,      a = 0.5		,      a = 0.07		,      a = 1_2_5_4_4		,      a = 3.0		,      a = 0.75		,      a = 0.02		,      a = 1.0		,      a = 1e-05		,      a = False		,      a = True		,      a = True		,      a = [4, 8, 1_6, 3_2]		,      a = 7_7		,      a = 2_5_6		,      a = 7_7		,      a = 6		,      a = 4_9_4_0_8		,      a = 2		,      a = 1_6		,      a = 2_5_6		,      a = 2_5_6		,      a = 2_5_6		,      a = 1_0_2_4		,      a = "GN"		,      a = 6		,      a = 1_0		,      a = True		,      a = 8		,      a = 0.1		,      a = 2_0_4_8		,      a = False		,      a = False		,      a = 2		,      a = 4		,      **a		,      )	-> List[Any]:
             if backbone_config is None:
                    logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'							)
                    lowercase__      :		List[str]          =     CONFIG_MAPPING['swin'](
                        image_size=2_2_4		,      in_channels=3		,      patch_size=4		,      embed_dim=9_6		,      depths=[2, 2, 6, 2]		,      num_heads=[3, 6, 1_2, 2_4]		,      window_size=7		,      drop_path_rate=0.3		,      use_absolute_embeddings=a		,      out_features=['stage1', 'stage2', 'stage3', 'stage4']		,      )
             elif isinstance(a		,      a							):
                    lowercase__      :		Union[str, Any]          =     backbone_config.get('model_type'							)
                    lowercase__      :		Optional[int]          =     CONFIG_MAPPING[backbone_model_type]
                    lowercase__      :		Optional[Any]          =     config_class.from_dict(a							)

             lowercase__      :		Tuple          =     backbone_config

             lowercase__      :		Optional[Any]          =     ignore_value
             lowercase__      :		int          =     num_queries
             lowercase__      :		str          =     no_object_weight
             lowercase__      :		str          =     class_weight
             lowercase__      :		Dict          =     mask_weight
             lowercase__      :		str          =     dice_weight
             lowercase__      :		Union[str, Any]          =     contrastive_weight
             lowercase__      :		Tuple          =     contrastive_temperature
             lowercase__      :		List[str]          =     train_num_points
             lowercase__      :		Tuple          =     oversample_ratio
             lowercase__      :		Tuple          =     importance_sample_ratio
             lowercase__      :		Optional[int]          =     init_std
             lowercase__      :		Optional[int]          =     init_xavier_std
             lowercase__      :		List[str]          =     layer_norm_eps
             lowercase__      :		str          =     is_training
             lowercase__      :		Dict          =     use_auxiliary_loss
             lowercase__      :		Any          =     output_auxiliary_logits
             lowercase__      :		str          =     strides
             lowercase__      :		Any          =     task_seq_len
             lowercase__      :		List[Any]          =     text_encoder_width
             lowercase__      :		Dict          =     text_encoder_context_length
             lowercase__      :		Any          =     text_encoder_num_layers
             lowercase__      :		Any          =     text_encoder_vocab_size
             lowercase__      :		str          =     text_encoder_proj_layers
             lowercase__      :		str          =     text_encoder_n_ctx
             lowercase__      :		List[Any]          =     conv_dim
             lowercase__      :		Optional[Any]          =     mask_dim
             lowercase__      :		List[Any]          =     hidden_dim
             lowercase__      :		int          =     encoder_feedforward_dim
             lowercase__      :		List[str]          =     norm
             lowercase__      :		Union[str, Any]          =     encoder_layers
             lowercase__      :		List[Any]          =     decoder_layers
             lowercase__      :		int          =     use_task_norm
             lowercase__      :		Union[str, Any]          =     num_attention_heads
             lowercase__      :		Union[str, Any]          =     dropout
             lowercase__      :		Any          =     dim_feedforward
             lowercase__      :		Optional[int]          =     pre_norm
             lowercase__      :		Tuple          =     enforce_input_proj
             lowercase__      :		List[Any]          =     query_dec_layers
             lowercase__      :		Optional[int]          =     common_stride
             lowercase__      :		str          =     decoder_layers

             super().__init__(**a							)


      def 		_UpperCAmelCase	(       self							)	-> Dict[str, any]:
             lowercase__      :		str          =     copy.deepcopy(self.__dict__							)
             lowercase__      :		Union[str, Any]          =     self.backbone_config.to_dict()
             lowercase__      :		str          =     self.__class__.model_type
             return output

