




"""simple docstring"""



import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


SCREAMING_SNAKE_CASE							=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							=			{
    "google/vit-hybrid-base-bit-384": "https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}




class       UpperCAmelCase_    (		A_				):
    lowercase__		   = '''vit-hybrid'''




    def __init__(       self		:			Optional[Any]      ,					snake_case_		:			str=None      ,					snake_case_		:			Any=768      ,					snake_case_		:			Any=12      ,					snake_case_		:			Dict=12      ,					snake_case_		:			Optional[int]=3_072      ,					snake_case_		:			Union[str, Any]="gelu"      ,					snake_case_		:			List[str]=0.0      ,					snake_case_		:			int=0.0      ,					snake_case_		:			Optional[int]=0.02      ,					snake_case_		:			Tuple=1e-12      ,					snake_case_		:			str=224      ,					snake_case_		:			int=1      ,					snake_case_		:			List[Any]=3      ,					snake_case_		:			Tuple=[1, 1_024, 24, 24]      ,					snake_case_		:			int=True      ,					**snake_case_		:			Tuple      ,					) ->				int:




     '''simple docstring'''

     super().__init__(**snake_case_ )

     if backbone_config is None:
      logger.info("`backbone_config` is `None`. Initializing the config with a `BiT` backbone." )
      A__			=							{
          "global_padding": "same",
          "layer_type": "bottleneck",
          "depths": [3, 4, 9],
          "out_features": ["stage3"],
          "embedding_dynamic_padding": True,
      }

     if isinstance(snake_case_      ,					snake_case_ ):
      if "model_type" in backbone_config:
       A__			=							CONFIG_MAPPING[backbone_config["model_type"]]
      else:
       logger.info(
           "`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class." )
       A__			=							BitConfig
      A__			=							backbone_config_class(**snake_case_ )

     A__			=							backbone_featmap_shape
     A__			=							backbone_config
     A__			=							hidden_size
     A__			=							num_hidden_layers
     A__			=							num_attention_heads
     A__			=							intermediate_size
     A__			=							hidden_act
     A__			=							hidden_dropout_prob
     A__			=							attention_probs_dropout_prob
     A__			=							initializer_range
     A__			=							layer_norm_eps
     A__			=							image_size
     A__			=							patch_size
     A__			=							num_channels
     A__			=							qkv_bias




    def 				__magic_name__				(       self		:			str ) ->				Dict[str, any]:




     '''simple docstring'''

     A__			=							copy.deepcopy(self.__dict__ )
     A__			=							self.backbone_config.to_dict()
     A__			=							self.__class__.model_type
     return output











"""simple docstring"""



import numpy as np
import qiskit







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_		)		->						qiskit.QuantumCircuit:
 A__			=							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:
  A__			=							np.random.randint(1     ,					2**num_qubits		)
  # Next, format 'b' as a binary string of length 'n', padded with zeros:
  A__			=							format(lowercase_     ,					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(lowercase_		):
   if bit == "1":
    oracle_qc.x(lowercase_		)
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
  for index in range(lowercase_		):
   oracle_qc.cx(lowercase_     ,					lowercase_		)
  # Next, place the final X-gates
  for index, bit in enumerate(lowercase_		):
   if bit == "1":
    oracle_qc.x(lowercase_		)

    # 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)
  A__			=							np.random.randint(2		)
  if output == 1:
   oracle_qc.x(lowercase_		)

 A__			=							oracle_qc.to_gate()
 A__			=							"Oracle"  # To show when we display the circuit
 return oracle_gate







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

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

 return dj_circuit







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_		)		->						qiskit.result.counts.Counts:
 A__			=							qiskit.Aer.get_backend("aer_simulator"		)

 A__			=							dj_oracle(lowercase_     ,					lowercase_		)
 A__			=							dj_algorithm(lowercase_     ,					lowercase_		)

 # Execute the circuit on the simulator
 A__			=							qiskit.execute(lowercase_     ,					lowercase_     ,					shots=10_00		)

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


if __name__ == "__main__":
      print(f'Deutsch Jozsa - Constant Oracle: {deutsch_jozsa("constant", 3)}')
      print(f'Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa("balanced", 3)}')





