






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 PaddingStrategy, TensorType, logging


a		:       Dict			     = logging.get_logger(__name__)
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : Optional[Any]							       =	['input_features', 'attention_mask']
      def __init__(      self					:			int		,      __lowercase					:			int=80		,      __lowercase					:			str=16000		,      __lowercase					:			Optional[Any]=80		,      __lowercase					:			List[Any]=0.0		,      __lowercase					:			Union[str, Any]=True		,      __lowercase					:			Tuple=True		,      __lowercase					:			Union[str, Any]=True		,      **__lowercase					:			List[Any]		,      )							->      Tuple:
        super().__init__(feature_size=__lowercase		,      sampling_rate=__lowercase		,      padding_value=__lowercase		,      **__lowercase					)
        __UpperCAmelCase							:	Any           =  num_mel_bins
        __UpperCAmelCase							:	List[Any]           =  do_ceptral_normalize
        __UpperCAmelCase							:	List[str]           =  normalize_means
        __UpperCAmelCase							:	List[Any]           =  normalize_vars
        __UpperCAmelCase							:	List[str]           =  True
      def 	UpperCAmelCase      (      self					:			int		,      __lowercase					:			np.ndarray		,      )							->      np.ndarray:
        __UpperCAmelCase							:	Union[str, Any]           =  waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
        __UpperCAmelCase							:	Optional[Any]           =  torch.from_numpy(__lowercase					).unsqueeze(0					)
        __UpperCAmelCase							:	List[str]           =  ta_kaldi.fbank(__lowercase		,      num_mel_bins=self.num_mel_bins		,      sample_frequency=self.sampling_rate					)
        return features.numpy()
      @staticmethod
      def 	UpperCAmelCase      (      __lowercase					:			np.ndarray		,      __lowercase					:			int		,      __lowercase					:			Optional[bool] = True		,      __lowercase					:			Optional[bool] = True		,      __lowercase					:			float = 0.0		,      )							->      np.ndarray:
        # make sure we normalize float32 arrays
        if normalize_means:
          __UpperCAmelCase							:	str           =  x[:input_length].mean(axis=0					)
          __UpperCAmelCase							:	int           =  np.subtract(__lowercase		,      __lowercase					)
        if normalize_vars:
          __UpperCAmelCase							:	Union[str, Any]           =  x[:input_length].std(axis=0					)
          __UpperCAmelCase							:	int           =  np.divide(__lowercase		,      __lowercase					)

        if input_length < x.shape[0]:
          __UpperCAmelCase							:	Tuple           =  padding_value

        # make sure array is in float32
        __UpperCAmelCase							:	int           =  x.astype(np.floataa					)

        return x
      def 	UpperCAmelCase      (      self					:			Tuple		,      __lowercase					:			List[np.ndarray]		,      __lowercase					:			Optional[np.ndarray] = None					)							->      List[np.ndarray]:
        __UpperCAmelCase							:	List[Any]           =  attention_mask.sum(-1					) if attention_mask is not None else [x.shape[0] for x in input_features]
        return [
            self.utterance_cmvn(__lowercase		,      __lowercase		,      self.normalize_means		,      self.normalize_vars		,      self.padding_value					)
            for x, n in zip(__lowercase		,      __lowercase					)
        ]




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

        __UpperCAmelCase							:	List[str]           =  isinstance(__lowercase		,      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							:	Dict           =  is_batched_numpy or (
            isinstance(__lowercase		,      (list, tuple)					) and (isinstance(raw_speech[0]		,      (np.ndarray, tuple, list)					))
        )

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

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

        # extract fbank features
        __UpperCAmelCase							:	Dict           =  [self._extract_fbank_features(__lowercase					) for waveform in raw_speech]

        # convert into correct format for padding
        __UpperCAmelCase							:	List[Any]           =  BatchFeature({"""input_features""": features}					)

        __UpperCAmelCase							:	Optional[int]           =  self.pad(
            __lowercase		,      padding=__lowercase		,      max_length=__lowercase		,      truncation=__lowercase		,      pad_to_multiple_of=__lowercase		,      return_attention_mask=__lowercase		,      **__lowercase		,      )

        # make sure list is in array format
        __UpperCAmelCase							:	Optional[Any]           =  padded_inputs.get("""input_features"""					)
        if isinstance(input_features[0]		,      __lowercase					):
          __UpperCAmelCase							:	Tuple           =  [np.asarray(__lowercase		,      dtype=np.floataa					) for feature in input_features]

        __UpperCAmelCase							:	Tuple           =  padded_inputs.get("""attention_mask"""					)
        if attention_mask is not None:
          __UpperCAmelCase							:	List[Any]           =  [np.asarray(__lowercase		,      dtype=np.intaa					) for array in attention_mask]

        # Utterance-level cepstral mean and variance normalization
        if self.do_ceptral_normalize:
          __UpperCAmelCase							:	Tuple           =  (
              np.array(__lowercase		,      dtype=np.intaa					)
              if self._get_padding_strategies(__lowercase		,      max_length=__lowercase					) is not PaddingStrategy.DO_NOT_PAD
              else None
          )
          __UpperCAmelCase							:	str           =  self.normalize(
              padded_inputs["""input_features"""]		,      attention_mask=__lowercase					)

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

        return padded_inputs










from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

a		:       Dict			     = typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
a		:       Any			     = typing.Union[np.floataa, int, float]  # noqa: UP007






def 				lowerCamelCase__      (    __lowerCamelCase			:     Vector  ,		__lowerCamelCase			:     Vector		):
  return np.sqrt(np.sum((np.asarray(__lowerCamelCase		) - np.asarray(__lowerCamelCase		)) ** 2		)		)






def 				lowerCamelCase__      (    __lowerCamelCase			:     Vector  ,		__lowerCamelCase			:     Vector		):
  return sum((va - va) ** 2 for va, va in zip(__lowerCamelCase  ,		__lowerCamelCase		)		) ** (1 / 2)


if __name__ == "__main__":






       def 				lowerCamelCase__      (    ):
         from timeit import timeit

         print("""Without Numpy"""		)
         print(
             timeit(
                 """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"""  ,		number=10000  ,		globals=globals()  ,		)		)
         print("""With Numpy"""		)
         print(
             timeit(
                 """euclidean_distance([1, 2, 3], [4, 5, 6])"""  ,		number=10000  ,		globals=globals()  ,		)		)







       benchmark()


