





'''simple docstring'''





import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   float = np.pi / 2      ,						_lowercase    :   float = np.pi / 2      ,						_lowercase    :   float = np.pi / 2 )      ->       qiskit.result.counts.Counts:




		'''simple docstring'''
		lowercase__				:			Tuple            =		QuantumRegister(3      ,						'qr' )  # Define the number of quantum bits
		lowercase__				:			Dict            =		ClassicalRegister(1      ,						'cr' )  # Define the number of classical bits

		lowercase__				:			Tuple            =		QuantumCircuit(a_      ,						a_ )  # Define the quantum circuit.

		# Build the circuit
		quantum_circuit.u(a_      ,						a_      ,						a_      ,						0 )  # Quantum State to teleport
		quantum_circuit.h(1 )  # add hadamard gate
		quantum_circuit.cx(
		    1      ,						2 )  # add control gate with qubit 1 as control and 2 as target.
		quantum_circuit.cx(0      ,						1 )
		quantum_circuit.h(0 )
		quantum_circuit.cz(0      ,						2 )  # add control z gate.
		quantum_circuit.cx(1      ,						2 )

		quantum_circuit.measure([2]      ,						[0] )  # measure the qubit.

		# Simulate the circuit using qasm simulator
		lowercase__				:			int            =		Aer.get_backend('aer_simulator' )
		lowercase__				:			Tuple            =		execute(a_      ,						a_      ,						shots=1_000 )

		return job.result().get_counts(a_ )


if __name__ == "__main__":
			print(
			    """Total count for teleported state is: """
			    F'{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}'
			)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase:  Dict   =   {
    """configuration_deberta_v2""": ["""DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaV2Config""", """DebertaV2OnnxConfig"""],
    """tokenization_deberta_v2""": ["""DebertaV2Tokenizer"""],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  List[Any]   =   ["""DebertaV2TokenizerFast"""]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  str   =   [
			    """TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """TFDebertaV2ForMaskedLM""",
			    """TFDebertaV2ForQuestionAnswering""",
			    """TFDebertaV2ForSequenceClassification""",
			    """TFDebertaV2ForTokenClassification""",
			    """TFDebertaV2Model""",
			    """TFDebertaV2PreTrainedModel""",
			]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__UpperCamelCase:  Tuple   =   [
			    """DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """DebertaV2ForMaskedLM""",
			    """DebertaV2ForMultipleChoice""",
			    """DebertaV2ForQuestionAnswering""",
			    """DebertaV2ForSequenceClassification""",
			    """DebertaV2ForTokenClassification""",
			    """DebertaV2Model""",
			    """DebertaV2PreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_deberta_va import (
			    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    DebertaVaConfig,
			    DebertaVaOnnxConfig,
			)
			from .tokenization_deberta_va import DebertaVaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_deberta_va import (
						    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFDebertaVaForMaskedLM,
						    TFDebertaVaForQuestionAnswering,
						    TFDebertaVaForSequenceClassification,
						    TFDebertaVaForTokenClassification,
						    TFDebertaVaModel,
						    TFDebertaVaPreTrainedModel,
						)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_deberta_va import (
						    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    DebertaVaForMaskedLM,
						    DebertaVaForMultipleChoice,
						    DebertaVaForQuestionAnswering,
						    DebertaVaForSequenceClassification,
						    DebertaVaForTokenClassification,
						    DebertaVaModel,
						    DebertaVaPreTrainedModel,
						)

else:
			import sys

			__UpperCamelCase:  Optional[Any]   =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


