






from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


UpperCAmelCase_					:		Optional[int]           = logging.get_logger(__name__)

class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	Optional[Any]	      =    ["""input_values""", """attention_mask"""]





	def __init__(						self							:   List[str]					,    __lowerCamelCase							:   Optional[Any]=1					,    __lowerCamelCase							:   Union[str, Any]=16_000					,    __lowerCamelCase							:   Any=128					,    __lowerCamelCase							:   Optional[Any]=1_024					,    __lowerCamelCase							:   List[Any]=0.0					,    __lowerCamelCase							:   Any=True					,    __lowerCamelCase							:   Union[str, Any]=-4.2677393					,    __lowerCamelCase							:   int=4.5689974					,    __lowerCamelCase							:   Any=False					,    **__lowerCamelCase							:   List[Any]					,    ):
		super().__init__(feature_size=__lowerCamelCase					,    sampling_rate=__lowerCamelCase					,    padding_value=__lowerCamelCase					,    **__lowerCamelCase      )
		UpperCamelCase	:Union[str, Any]		=      num_mel_bins
		UpperCamelCase	:Optional[int]		=      max_length
		UpperCamelCase	:Optional[int]		=      do_normalize
		UpperCamelCase	:List[Any]		=      mean
		UpperCamelCase	:List[str]		=      std
		UpperCamelCase	:Any		=      return_attention_mask





	def 		_A					(						self							:   int					,    __lowerCamelCase							:   np.ndarray					,    __lowerCamelCase							:   int					,    ):
		UpperCamelCase	:Dict		=      torch.from_numpy(__lowerCamelCase      ).unsqueeze(0      )
		UpperCamelCase	:str		=      ta_kaldi.fbank(
		    __lowerCamelCase					,    htk_compat=__lowerCamelCase					,    sample_frequency=self.sampling_rate					,    use_energy=__lowerCamelCase					,    window_type="""hanning"""					,    num_mel_bins=self.num_mel_bins					,    dither=0.0					,    frame_shift=10					,    )

		UpperCamelCase	:Optional[int]		=      fbank.shape[0]
		UpperCamelCase	:str		=      max_length - n_frames

		# pad or truncate, depending on difference
		if difference > 0:
			UpperCamelCase	:Dict		=      torch.nn.ZeroPadad((0, 0, 0, difference)      )
			UpperCamelCase	:Dict		=      pad_module(__lowerCamelCase      )
		elif difference < 0:
			UpperCamelCase	:str		=      fbank[0:max_length, :]

		UpperCamelCase	:str		=      fbank.numpy()

		return fbank





	def 		_A					(						self							:   Tuple					,    __lowerCamelCase							:   np.ndarray      ):
		return (input_values - (self.mean)) / (self.std * 2)





	def __call__(						self							:   Dict					,    __lowerCamelCase							:   Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,    __lowerCamelCase							:   Optional[int] = None					,    __lowerCamelCase							:   Optional[Union[str, TensorType]] = None					,    **__lowerCamelCase							:   Optional[Any]					,    ):

		if sampling_rate is not None:
			if sampling_rate != self.sampling_rate:
				raise ValueError(
				    F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
				    F""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
				    F""" {self.sampling_rate} and not {sampling_rate}."""      )
		else:
			logger.warning(
			    """It is strongly recommended to pass the `sampling_rate` argument to this function. """
			    """Failing to do so can result in silent errors that might be hard to debug."""      )

		UpperCamelCase	:List[str]		=      isinstance(__lowerCamelCase					,    np.ndarray      ) and len(raw_speech.shape      ) > 1
		if is_batched_numpy and len(raw_speech.shape      ) > 2:
			raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""      )
		UpperCamelCase	:Tuple		=      is_batched_numpy or (
		    isinstance(__lowerCamelCase					,    (list, tuple)      ) and (isinstance(raw_speech[0]					,    (np.ndarray, tuple, list)      ))
		)

		if is_batched:
			UpperCamelCase	:Optional[int]		=      [np.asarray(__lowerCamelCase					,    dtype=np.floataa      ) for speech in raw_speech]
		elif not is_batched and not isinstance(__lowerCamelCase					,    np.ndarray      ):
			UpperCamelCase	:Optional[int]		=      np.asarray(__lowerCamelCase					,    dtype=np.floataa      )
		elif isinstance(__lowerCamelCase					,    np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
			UpperCamelCase	:Union[str, Any]		=      raw_speech.astype(np.floataa      )

		# always return batch
		if not is_batched:
			UpperCamelCase	:Optional[int]		=      [raw_speech]

		# extract fbank features and pad/truncate to max_length
		UpperCamelCase	:Tuple		=      [self._extract_fbank_features(__lowerCamelCase					,    max_length=self.max_length      ) for waveform in raw_speech]

		# convert into BatchFeature
		UpperCamelCase	:str		=      BatchFeature({"""input_values""": features}      )

		# make sure list is in array format
		UpperCamelCase	:Optional[Any]		=      padded_inputs.get("""input_values"""      )
		if isinstance(input_values[0]					,    __lowerCamelCase      ):
			UpperCamelCase	:List[Any]		=      [np.asarray(__lowerCamelCase					,    dtype=np.floataa      ) for feature in input_values]

		# normalization
		if self.do_normalize:
			UpperCamelCase	:Union[str, Any]		=      [self.normalize(__lowerCamelCase      ) for feature in input_values]

		if return_tensors is not None:
			UpperCamelCase	:List[str]		=      padded_inputs.convert_to_tensors(__lowerCamelCase      )

		return padded_inputs








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

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






	"""simple docstring"""



	UpperCamelCase	:Optional[int]		=      QuantumRegister(3      ,     """qr"""   )  # Define the number of quantum bits
	UpperCamelCase	:Optional[Any]		=      ClassicalRegister(1      ,     """cr"""   )  # Define the number of classical bits

	UpperCamelCase	:List[Any]		=      QuantumCircuit(__magic_name__      ,     __magic_name__   )  # Define the quantum circuit.

	# Build the circuit
	quantum_circuit.u(__magic_name__      ,     __magic_name__      ,     __magic_name__      ,     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
	UpperCamelCase	:Union[str, Any]		=      Aer.get_backend("""aer_simulator"""   )
	UpperCamelCase	:Dict		=      execute(__magic_name__      ,     __magic_name__      ,     shots=1000   )

	return job.result().get_counts(__magic_name__   )


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