



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







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__ = np.pi / 2			,SCREAMING_SNAKE_CASE__ = np.pi / 2			,SCREAMING_SNAKE_CASE__ = np.pi / 2  ):


						'''simple docstring'''




						lowerCAmelCase	:   Dict					     =					QuantumRegister(3			,"""qr"""  )  # Define the number of quantum bits
						lowerCAmelCase	:   List[str]					     =					ClassicalRegister(1			,"""cr"""  )  # Define the number of classical bits

						lowerCAmelCase	:   List[str]					     =					QuantumCircuit(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  )  # Define the quantum circuit.

						# Build the circuit
						quantum_circuit.u(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,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
						lowerCAmelCase	:   Optional[int]					     =					Aer.get_backend("""aer_simulator"""  )
						lowerCAmelCase	:   List[str]					     =					execute(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,shots=1_0_0_0  )

						return job.result().get_counts(SCREAMING_SNAKE_CASE__  )


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_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


lowerCAmelCase					:				Optional[int]   	={
    'configuration_speech_to_text_2': ['SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2Text2Config'],
    'processing_speech_to_text_2': ['Speech2Text2Processor'],
    'tokenization_speech_to_text_2': ['Speech2Text2Tokenizer'],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase					:				int   	=[
		    'SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'Speech2Text2ForCausalLM',
		    'Speech2Text2PreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
		from .processing_speech_to_text_a import SpeechaTextaProcessor
		from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_speech_to_text_a import (
				    SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SpeechaTextaForCausalLM,
				    SpeechaTextaPreTrainedModel,
				)

else:
		import sys

		lowerCAmelCase					:				Union[str, Any]   	=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

