





import numpy as np
import qiskit


def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__	):
		lowerCamelCase_  :    List[Any]   	=			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:
				lowerCamelCase_  :    Any   	=			np.random.randint(1       ,2**num_qubits	)
				# Next, format 'b' as a binary string of length 'n', padded with zeros:
				lowerCamelCase_  :    Tuple   	=			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)
				lowerCamelCase_  :    Dict   	=			np.random.randint(2	)
				if output == 1:
						oracle_qc.x(lowerCAmelCase__	)

		lowerCamelCase_  :    Optional[int]   	=			oracle_qc.to_gate()
		lowerCamelCase_  :    str   	=			'Oracle'  # To show when we display the circuit
		return oracle_gate


def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__	):
		lowerCamelCase_  :    int   	=			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 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__	):
		lowerCamelCase_  :    Any   	=			qiskit.Aer.get_backend('aer_simulator'	)

		lowerCamelCase_  :    Dict   	=			dj_oracle(lowerCAmelCase__       ,lowerCAmelCase__	)
		lowerCamelCase_  :    Optional[int]   	=			dj_algorithm(lowerCAmelCase__       ,lowerCAmelCase__	)

		# Execute the circuit on the simulator
		lowerCamelCase_  :    Any   	=			qiskit.execute(lowerCAmelCase__       ,lowerCAmelCase__       ,shots=10_00	)

		# 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)}''')









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


_lowercase		:     Dict 					=logging.get_logger(__name__)

_lowercase		:     Any 					={
    """dandelin/vilt-b32-mlm""": """https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"""
}






class 	UpperCamelCase_     (     snake_case__       ):
					_a			:	List[str]							=				'vilt'



					def __init__(		self    :    int					,			lowerCamelCase    :    Any=3_05_22					,			lowerCamelCase    :    Union[str, Any]=2					,			lowerCamelCase    :    Optional[Any]=2					,			lowerCamelCase    :    str=40					,			lowerCamelCase    :    List[str]=7_68					,			lowerCamelCase    :    Optional[int]=12					,			lowerCamelCase    :    List[Any]=12					,			lowerCamelCase    :    List[str]=30_72					,			lowerCamelCase    :    Union[str, Any]="gelu"					,			lowerCamelCase    :    int=0.0					,			lowerCamelCase    :    str=0.0					,			lowerCamelCase    :    int=0.02					,			lowerCamelCase    :    Union[str, Any]=1E-12					,			lowerCamelCase    :    Dict=3_84					,			lowerCamelCase    :    List[str]=32					,			lowerCamelCase    :    Dict=3					,			lowerCamelCase    :    List[str]=True					,			lowerCamelCase    :    int=-1					,			lowerCamelCase    :    List[Any]=False					,			lowerCamelCase    :    Any=-1					,			**lowerCamelCase    :    str					,			):
							super().__init__(tie_word_embeddings=lowerCamelCase					,			**lowerCamelCase	)

							lowerCamelCase_  :    str   	=			vocab_size
							lowerCamelCase_  :    Dict   	=			type_vocab_size
							lowerCamelCase_  :    str   	=			modality_type_vocab_size
							lowerCamelCase_  :    Optional[Any]   	=			max_position_embeddings

							lowerCamelCase_  :    List[Any]   	=			hidden_size
							lowerCamelCase_  :    Union[str, Any]   	=			num_hidden_layers
							lowerCamelCase_  :    Any   	=			num_attention_heads
							lowerCamelCase_  :    List[Any]   	=			intermediate_size
							lowerCamelCase_  :    Union[str, Any]   	=			hidden_act
							lowerCamelCase_  :    Any   	=			hidden_dropout_prob
							lowerCamelCase_  :    Optional[int]   	=			attention_probs_dropout_prob
							lowerCamelCase_  :    Any   	=			initializer_range
							lowerCamelCase_  :    str   	=			layer_norm_eps

							lowerCamelCase_  :    Optional[Any]   	=			image_size
							lowerCamelCase_  :    List[Any]   	=			patch_size
							lowerCamelCase_  :    Tuple   	=			num_channels
							lowerCamelCase_  :    Optional[Any]   	=			qkv_bias
							lowerCamelCase_  :    Optional[int]   	=			max_image_length
							lowerCamelCase_  :    Optional[Any]   	=			num_images
