'''simple docstring'''







from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


UpperCamelCase_      	=	logging.get_logger(__name__)




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							int     		= ['input_features', 'attention_mask']




						def __init__(    self :       List[Any]							,							UpperCamelCase_ :       Optional[int]=80							,							UpperCamelCase_ :       List[Any]=1_60_00							,							UpperCamelCase_ :       List[str]=0.0							,							UpperCamelCase_ :       List[str]=10							,							UpperCamelCase_ :       str=25							,							UpperCamelCase_ :       Dict="hamming_window"							,							UpperCamelCase_ :       Dict=3_2768.0							,							UpperCamelCase_ :       Union[str, Any]=0.97							,							UpperCamelCase_ :       List[Any]=1.0							,							UpperCamelCase_ :       int=True							,							UpperCamelCase_ :       List[str]=True							,							UpperCamelCase_ :       int=False							,							**UpperCamelCase_ :       int							,							)     ->				List[str]:
													super().__init__(feature_size=UpperCamelCase_							,							sampling_rate=UpperCamelCase_							,							padding_value=UpperCamelCase_							,							**UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :List[Any]						    = feature_size
													SCREAMING_SNAKE_CASE__     :Any						    = sampling_rate
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = padding_value
													SCREAMING_SNAKE_CASE__     :Tuple						    = hop_length
													SCREAMING_SNAKE_CASE__     :Any						    = win_length
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = frame_signal_scale
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = preemphasis_coeff
													SCREAMING_SNAKE_CASE__     :List[Any]						    = mel_floor
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = normalize_means
													SCREAMING_SNAKE_CASE__     :Dict						    = normalize_vars
													SCREAMING_SNAKE_CASE__     :str						    = win_function
													SCREAMING_SNAKE_CASE__     :List[Any]						    = return_attention_mask

													SCREAMING_SNAKE_CASE__     :str						    = win_length * sampling_rate // 10_00
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = hop_length * sampling_rate // 10_00

													SCREAMING_SNAKE_CASE__     :Any						    = optimal_fft_length(self.sample_size      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = (self.n_fft // 2) + 1




						def      __lowerCamelCase       (    self :       List[Any]							,							UpperCamelCase_ :       np.array      )     ->				np.ndarray:
													if self.win_function == "hamming_window":
																				SCREAMING_SNAKE_CASE__     :Dict						    = window_function(window_length=self.sample_size							,							name=self.win_function							,							periodic=UpperCamelCase_      )
													else:
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = window_function(window_length=self.sample_size							,							name=self.win_function      )

													SCREAMING_SNAKE_CASE__     :Dict						    = mel_filter_bank(
													    num_frequency_bins=self.n_freqs							,							num_mel_filters=self.feature_size							,							min_frequency=0.0							,							max_frequency=self.sampling_rate / 2.0							,							sampling_rate=self.sampling_rate							,							)

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = spectrogram(
													    one_waveform * self.frame_signal_scale							,							window=UpperCamelCase_							,							frame_length=self.sample_size							,							hop_length=self.sample_stride							,							fft_length=self.n_fft							,							center=UpperCamelCase_							,							preemphasis=self.preemphasis_coeff							,							mel_filters=UpperCamelCase_							,							mel_floor=self.mel_floor							,							log_mel='log'							,							)
													return msfc_features.T




						def      __lowerCamelCase       (    self :       Dict							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       int							,							UpperCamelCase_ :       List[Any]      )     ->				int:
													# make sure we normalize float32 arrays
													if self.normalize_means:
																				SCREAMING_SNAKE_CASE__     :List[str]						    = x[:input_length].mean(axis=0      )
																				SCREAMING_SNAKE_CASE__     :Tuple						    = np.subtract(UpperCamelCase_							,							UpperCamelCase_      )
													if self.normalize_vars:
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = x[:input_length].std(axis=0      )
																				SCREAMING_SNAKE_CASE__     :Any						    = np.divide(UpperCamelCase_							,							UpperCamelCase_      )

													if input_length < x.shape[0]:
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = padding_value

													# make sure array is in float32
													SCREAMING_SNAKE_CASE__     :List[Any]						    = x.astype(np.floataa      )

													return x




						def      __lowerCamelCase       (    self :       Tuple							,							UpperCamelCase_ :       List[np.ndarray]							,							UpperCamelCase_ :       Optional[np.ndarray] = None      )     ->				List[np.ndarray]:
													SCREAMING_SNAKE_CASE__     :Any						    = attention_mask.sum(-1      ) if attention_mask is not None else [x.shape[0] for x in input_features]
													return [self._normalize_one(UpperCamelCase_							,							UpperCamelCase_							,							self.padding_value      ) for x, n in zip(UpperCamelCase_							,							UpperCamelCase_      )]




						def __call__(    self :       Optional[Any]							,							UpperCamelCase_ :       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]							,							UpperCamelCase_ :       Union[bool, str, PaddingStrategy] = False							,							UpperCamelCase_ :       Optional[int] = None							,							UpperCamelCase_ :       bool = False							,							UpperCamelCase_ :       Optional[int] = None							,							UpperCamelCase_ :       Optional[bool] = None							,							UpperCamelCase_ :       Optional[Union[str, TensorType]] = None							,							UpperCamelCase_ :       Optional[int] = None							,							**UpperCamelCase_ :       List[Any]							,							)     ->				BatchFeature:

													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.'      )

													SCREAMING_SNAKE_CASE__     :int						    = isinstance(UpperCamelCase_							,							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}'''      )
													SCREAMING_SNAKE_CASE__     :Dict						    = is_batched_numpy or (
													    isinstance(UpperCamelCase_							,							(list, tuple)      ) and (isinstance(raw_speech[0]							,							(np.ndarray, tuple, list)      ))
													)

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

													# always return batch
													if not is_batched:
																				SCREAMING_SNAKE_CASE__     :List[str]						    = [raw_speech]

													# extract fbank features
													SCREAMING_SNAKE_CASE__     :List[Any]						    = [self._extract_mfsc_features(UpperCamelCase_      ) for one_waveform in raw_speech]

													# convert into correct format for padding
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = BatchFeature({'input_features': features}      )

													SCREAMING_SNAKE_CASE__     :List[Any]						    = self.pad(
													    UpperCamelCase_							,							padding=UpperCamelCase_							,							max_length=UpperCamelCase_							,							truncation=UpperCamelCase_							,							pad_to_multiple_of=UpperCamelCase_							,							return_attention_mask=UpperCamelCase_							,							**UpperCamelCase_							,							)
													# make sure list is in array format
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = padded_inputs.get('input_features'      )
													if isinstance(input_features[0]							,							UpperCamelCase_      ):
																				SCREAMING_SNAKE_CASE__     :List[str]						    = [np.asarray(UpperCamelCase_							,							dtype=np.floataa      ) for feature in input_features]

													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = padded_inputs.get('attention_mask'      )
													if attention_mask is not None:
																				SCREAMING_SNAKE_CASE__     :str						    = [np.asarray(UpperCamelCase_							,							dtype=np.intaa      ) for array in attention_mask]

													if self.normalize_means or self.normalize_vars:
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = (
																				    np.array(UpperCamelCase_							,							dtype=np.intaa      )
																				    if self._get_padding_strategies(UpperCamelCase_							,							max_length=UpperCamelCase_      ) is not PaddingStrategy.DO_NOT_PAD
																				    and padding
																				    else None
																				)
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = self.normalize(
																				    padded_inputs['input_features']							,							attention_mask=UpperCamelCase_      )

													if return_tensors is not None:
																				SCREAMING_SNAKE_CASE__     :Dict						    = padded_inputs.convert_to_tensors(UpperCamelCase_      )

													return padded_inputs







'''simple docstring'''







import numpy as np
import qiskit


def    lowerCamelCase       (			UpperCAmelCase__				:  int = 8				,						UpperCAmelCase__				:  int | None = None		)			->	str:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = np.random.default_rng(seed=UpperCAmelCase__		)

							# Roughly 25% of the qubits will contribute to the key.
							# So we take more than we need.
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = 6 * key_len
							# Measurement basis for Alice's qubits.
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = rng.integers(2				,						size=UpperCAmelCase__		)
							# The set of states Alice will prepare.
							SCREAMING_SNAKE_CASE__     :List[Any]						    = rng.integers(2				,						size=UpperCAmelCase__		)
							# Measurement basis for Bob's qubits.
							SCREAMING_SNAKE_CASE__     :str						    = rng.integers(2				,						size=UpperCAmelCase__		)

							# Quantum Circuit to simulate BB84
							SCREAMING_SNAKE_CASE__     :int						    = qiskit.QuantumCircuit(UpperCAmelCase__				,						name='BB84'		)

							# Alice prepares her qubits according to rules above.
							for index, _ in enumerate(UpperCAmelCase__		):
														if alice_state[index] == 1:
																					bbaa_circ.x(UpperCAmelCase__		)
														if alice_basis[index] == 1:
																					bbaa_circ.h(UpperCAmelCase__		)
							bbaa_circ.barrier()

							# Bob measures the received qubits according to rules above.
							for index, _ in enumerate(UpperCAmelCase__		):
														if bob_basis[index] == 1:
																					bbaa_circ.h(UpperCAmelCase__		)

							bbaa_circ.barrier()
							bbaa_circ.measure_all()

							# Simulate the quantum circuit.
							SCREAMING_SNAKE_CASE__     :str						    = qiskit.Aer.get_backend('aer_simulator'		)
							# We only need to run one shot because the key is unique.
							# Multiple shots will produce the same key.
							SCREAMING_SNAKE_CASE__     :int						    = qiskit.execute(UpperCAmelCase__				,						UpperCAmelCase__				,						shots=1				,						seed_simulator=UpperCAmelCase__		)
							# Returns the result of measurement.
							SCREAMING_SNAKE_CASE__     :List[Any]						    = job.result().get_counts(UpperCAmelCase__		).most_frequent()

							# Extracting the generated key from the simulation results.
							# Only keep measurement results where Alice and Bob chose the same basis.
							SCREAMING_SNAKE_CASE__     :Any						    = ''.join(
							    [
							        result_bit
							        for alice_basis_bit, bob_basis_bit, result_bit in zip(
							            UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__		)
							        if alice_basis_bit == bob_basis_bit
							    ]		)

							# Get final key. Pad with 0 if too short, otherwise truncate.
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = gen_key[:key_len] if len(UpperCAmelCase__		) >= key_len else gen_key.ljust(UpperCAmelCase__				,						'0'		)
							return key


if __name__ == "__main__":
		print(f"The generated key is : {bbaa(8, seed=0)}")
		from doctest import testmod

		testmod()






