



import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:Union[str, Any] =       "Speech2TextFeatureExtractor"
							A				:List[str] =       "Speech2TextTokenizer"



							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											super().__init__(__UpperCAmelCase ,					__UpperCAmelCase				)
											a__	:							int      						= self.feature_extractor
											a__	:							Tuple      						= False



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







											"""simple docstring"""



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

											if "raw_speech" in kwargs:
															warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."				)
															a__	:							Union[str, Any]      						= kwargs.pop("raw_speech"				)
											else:
															a__	:							Optional[Any]      						= kwargs.pop("audio" ,					__UpperCAmelCase				)
											a__	:							List[str]      						= kwargs.pop("sampling_rate" ,					__UpperCAmelCase				)
											a__	:							Any      						= kwargs.pop("text" ,					__UpperCAmelCase				)
											if len(__UpperCAmelCase				) > 0:
															a__	:							Tuple      						= args[0]
															a__	:							Optional[Any]      						= 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:
															a__	:							List[Any]      						= self.feature_extractor(__UpperCAmelCase ,					*__UpperCAmelCase ,					sampling_rate=__UpperCAmelCase ,					**__UpperCAmelCase				)
											if text is not None:
															a__	:							Optional[Any]      						= self.tokenizer(__UpperCAmelCase ,					**__UpperCAmelCase				)

											if text is None:
															return inputs
											elif audio is None:
															return encodings
											else:
															a__	:							Union[str, Any]      						= encodings["input_ids"]
															return inputs



							def 		_A	(							self ,					*__UpperCAmelCase ,					**__UpperCAmelCase				):







											"""simple docstring"""



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



							def 		_A	(							self ,					*__UpperCAmelCase ,					**__UpperCAmelCase				):







											"""simple docstring"""



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




							@contextmanager
							def 		_A	(							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."				)
											a__	:							int      						= True
											a__	:							Any      						= self.tokenizer
											yield
											a__	:							Dict      						= self.feature_extractor
											a__	:							Tuple      						= False







import qiskit






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase = 2 )	->						qiskit.result.counts.Counts:
				a__	:							Any      						= qubits

				# Using Aer's simulator
				a__	:							Dict      						= qiskit.Aer.get_backend("aer_simulator" )

				# Creating a Quantum Circuit acting on the q register
				a__	:							Dict      						= qiskit.QuantumCircuit(__UpperCamelCase	,				__UpperCamelCase )

				# Adding a H gate on qubit 0 (now q0 in superposition)
				circuit.h(0 )

				for i in range(1	,				__UpperCamelCase ):
								# Adding CX (CNOT) gate
								circuit.cx(i - 1	,				__UpperCamelCase )

				# Mapping the quantum measurement to the classical bits
				circuit.measure(list(range(__UpperCamelCase ) )	,				list(range(__UpperCamelCase ) ) )

				# Now measuring any one qubit would affect other qubits to collapse
				# their super position and have same state as the measured one.

				# Executing the circuit on the simulator
				a__	:							Any      						= qiskit.execute(__UpperCamelCase	,				__UpperCamelCase	,				shots=10_00 )

				return job.result().get_counts(__UpperCamelCase )


if __name__ == "__main__":
						print(F'Total count for various states are: {quantum_entanglement(3)}')


