'''simple docstring'''







import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

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


UpperCamelCase_      	=	logging.get_logger(__name__)




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							Optional[Any]     		= ['input_features']




						def __init__(    self :       int							,							UpperCamelCase_ :       str=80							,							UpperCamelCase_ :       Union[str, Any]=1_60_00							,							UpperCamelCase_ :       List[Any]=1_60							,							UpperCamelCase_ :       Any=30							,							UpperCamelCase_ :       List[str]=4_00							,							UpperCamelCase_ :       str=0.0							,							UpperCamelCase_ :       List[Any]=False							,							**UpperCamelCase_ :       Optional[Any]							,							)     ->				Any:
													super().__init__(
													    feature_size=UpperCamelCase_							,							sampling_rate=UpperCamelCase_							,							padding_value=UpperCamelCase_							,							return_attention_mask=UpperCamelCase_							,							**UpperCamelCase_							,							)
													SCREAMING_SNAKE_CASE__     :List[str]						    = n_fft
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = hop_length
													SCREAMING_SNAKE_CASE__     :List[Any]						    = chunk_length
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = chunk_length * sampling_rate
													SCREAMING_SNAKE_CASE__     :Tuple						    = self.n_samples // hop_length
													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = sampling_rate
													SCREAMING_SNAKE_CASE__     :Tuple						    = mel_filter_bank(
													    num_frequency_bins=1 + n_fft // 2							,							num_mel_filters=UpperCamelCase_							,							min_frequency=0.0							,							max_frequency=8000.0							,							sampling_rate=UpperCamelCase_							,							norm='slaney'							,							mel_scale='slaney'							,							)




						def      __lowerCamelCase       (    self :       str							,							UpperCamelCase_ :       np.array      )     ->				np.ndarray:
													SCREAMING_SNAKE_CASE__     :int						    = spectrogram(
													    UpperCamelCase_							,							window_function(self.n_fft							,							'hann'      )							,							frame_length=self.n_fft							,							hop_length=self.hop_length							,							power=2.0							,							mel_filters=self.mel_filters							,							log_mel='log10'							,							)
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = log_spec[:, :-1]
													SCREAMING_SNAKE_CASE__     :Dict						    = np.maximum(UpperCamelCase_							,							log_spec.max() - 8.0      )
													SCREAMING_SNAKE_CASE__     :List[Any]						    = (log_spec + 4.0) / 4.0
													return log_spec




						@staticmethod
						# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
						def      __lowerCamelCase       (    UpperCamelCase_ :       List[np.ndarray]							,							UpperCamelCase_ :       List[np.ndarray]							,							UpperCamelCase_ :       float = 0.0      )     ->				List[np.ndarray]:
													if attention_mask is not None:
																				SCREAMING_SNAKE_CASE__     :Optional[int]						    = np.array(UpperCamelCase_							,							np.intaa      )
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = []

																				for vector, length in zip(UpperCamelCase_							,							attention_mask.sum(-1      )      ):
																											SCREAMING_SNAKE_CASE__     :Any						    = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7      )
																											if length < normed_slice.shape[0]:
																																		SCREAMING_SNAKE_CASE__     :Dict						    = padding_value

																											normed_input_values.append(UpperCamelCase_      )
													else:
																				SCREAMING_SNAKE_CASE__     :int						    = [(x - x.mean()) / np.sqrt(x.var() + 1e-7      ) for x in input_values]

													return normed_input_values




						def __call__(    self :       Optional[int]							,							UpperCamelCase_ :       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]							,							UpperCamelCase_ :       bool = True							,							UpperCamelCase_ :       Optional[int] = None							,							UpperCamelCase_ :       Optional[Union[str, TensorType]] = None							,							UpperCamelCase_ :       Optional[bool] = None							,							UpperCamelCase_ :       Optional[str] = "max_length"							,							UpperCamelCase_ :       Optional[int] = None							,							UpperCamelCase_ :       Optional[int] = None							,							UpperCamelCase_ :       Optional[bool] = 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.__class__.__name__} was trained using a'''
																											    f''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
																											    f''' was sampled with {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__     :List[str]						    = 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__     :List[Any]						    = is_batched_numpy or (
													    isinstance(UpperCamelCase_							,							(list, tuple)      ) and (isinstance(raw_speech[0]							,							(np.ndarray, tuple, list)      ))
													)

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

													# always return batch
													if not is_batched:
																				SCREAMING_SNAKE_CASE__     :int						    = [np.asarray([raw_speech]      ).T]

													SCREAMING_SNAKE_CASE__     :Tuple						    = BatchFeature({'input_features': raw_speech}      )

													# convert into correct format for padding

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = self.pad(
													    UpperCamelCase_							,							padding=UpperCamelCase_							,							max_length=max_length if max_length else self.n_samples							,							truncation=UpperCamelCase_							,							pad_to_multiple_of=UpperCamelCase_							,							return_attention_mask=return_attention_mask or do_normalize							,							)

													# zero-mean and unit-variance normalization
													if do_normalize:
																				SCREAMING_SNAKE_CASE__     :List[str]						    = self.zero_mean_unit_var_norm(
																				    padded_inputs['input_features']							,							attention_mask=padded_inputs['attention_mask']							,							padding_value=self.padding_value							,							)
																				SCREAMING_SNAKE_CASE__     :int						    = np.stack(padded_inputs['input_features']							,							axis=0      )

													# make sure list is in array format
													SCREAMING_SNAKE_CASE__     :Tuple						    = padded_inputs.get('input_features'      ).transpose(2							,							0							,							1      )

													SCREAMING_SNAKE_CASE__     :Dict						    = [self._np_extract_fbank_features(UpperCamelCase_      ) for waveform in input_features[0]]

													if isinstance(input_features[0]							,							UpperCamelCase_      ):
																				SCREAMING_SNAKE_CASE__     :List[Any]						    = [np.asarray(UpperCamelCase_							,							dtype=np.floataa      ) for feature in input_features]
													else:
																				SCREAMING_SNAKE_CASE__     :Any						    = input_features

													if return_attention_mask:
																				# rescale from sample (48000) to feature (3000)
																				SCREAMING_SNAKE_CASE__     :int						    = padded_inputs['attention_mask'][:, :: self.hop_length]

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

													return padded_inputs




						def      __lowerCamelCase       (    self :       List[Any]      )     ->				Dict[str, Any]:
													SCREAMING_SNAKE_CASE__     :Tuple						    = copy.deepcopy(self.__dict__      )
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = self.__class__.__name__
													if "mel_filters" in output:
																				del output["mel_filters"]
													return output







'''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()






