



import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class    __lowerCAmelCase					(	lowerCamelCase__			):
				__lowerCamelCase					=							'''AutoFeatureExtractor'''
				__lowerCamelCase					=							'''Speech2Text2Tokenizer'''




				def __init__(				self		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									super().__init__(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_						)
									_lowerCAmelCase         =       self.feature_extractor
									_lowerCAmelCase         =       False




				def __call__(				self		,							*_snake_case		,							**_snake_case						):



									"""simple docstring"""





									if self._in_target_context_manager:
														return self.current_processor(*SCREAMING_SNAKE_CASE_		,							**SCREAMING_SNAKE_CASE_						)

									if "raw_speech" in kwargs:
														warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""						)
														_lowerCAmelCase         =       kwargs.pop("""raw_speech"""						)
									else:
														_lowerCAmelCase         =       kwargs.pop("""audio"""		,							SCREAMING_SNAKE_CASE_						)
									_lowerCAmelCase         =       kwargs.pop("""sampling_rate"""		,							SCREAMING_SNAKE_CASE_						)
									_lowerCAmelCase         =       kwargs.pop("""text"""		,							SCREAMING_SNAKE_CASE_						)
									if len(SCREAMING_SNAKE_CASE_						) > 0:
														_lowerCAmelCase         =       args[0]
														_lowerCAmelCase         =       args[1:]

									if audio is None and text is None:
														raise ValueError("""You need to specify either an `audio` or `text` input to process."""						)

									if audio is not None:
														_lowerCAmelCase         =       self.feature_extractor(SCREAMING_SNAKE_CASE_		,							*SCREAMING_SNAKE_CASE_		,							sampling_rate=SCREAMING_SNAKE_CASE_		,							**SCREAMING_SNAKE_CASE_						)
									if text is not None:
														_lowerCAmelCase         =       self.tokenizer(SCREAMING_SNAKE_CASE_		,							**SCREAMING_SNAKE_CASE_						)

									if text is None:
														return inputs
									elif audio is None:
														return encodings
									else:
														_lowerCAmelCase         =       encodings['''input_ids''']
														return inputs




				def 				snake_case  (				self		,							*_snake_case		,							**_snake_case						):



									"""simple docstring"""





									return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_		,							**SCREAMING_SNAKE_CASE_						)




				def 				snake_case  (				self		,							*_snake_case		,							**_snake_case						):



									"""simple docstring"""





									return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_		,							**SCREAMING_SNAKE_CASE_						)




				@contextmanager
				def 				snake_case  (				self						):



									"""simple docstring"""





									warnings.warn(
									    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
									    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
									    """your audio inputs, or in a separate call."""						)
									_lowerCAmelCase         =       True
									_lowerCAmelCase         =       self.tokenizer
									yield
									_lowerCAmelCase         =       self.feature_extractor
									_lowerCAmelCase         =       False





'''simple docstring'''





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

def   A			(							A_				:       float = np.pi / 2     ,				A_				:       float = np.pi / 2     ,				A_				:       float = np.pi / 2							):
						snake_case							:  List[Any]			=     QuantumRegister(3     ,				'''qr'''							)  # Define the number of quantum bits
						snake_case							:  List[Any]			=     ClassicalRegister(1     ,				'''cr'''							)  # Define the number of classical bits

						snake_case							:  Dict			=     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
						snake_case							:  str			=     Aer.get_backend('''aer_simulator'''							)
						snake_case							:  List[str]			=     execute(A_     ,				A_     ,				shots=1000							)

						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)}'''
						)

