
"""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


lowercase_       :						Dict     					=	logging.get_logger(__name__)
class 			UpperCamelCase      (							__lowercase  ):
     A__      =			['''input_features''', '''attention_mask''']
     def __init__(     self  ,     snake_case__=80  ,     snake_case__=16000  ,     snake_case__=0.0  ,     snake_case__=10  ,     snake_case__=25  ,     snake_case__="hamming_window"  ,     snake_case__=32768.0  ,     snake_case__=0.97  ,     snake_case__=1.0  ,     snake_case__=True  ,     snake_case__=True  ,     snake_case__=False  ,     **snake_case__  ,     ):




       """simple docstring"""


       super().__init__(feature_size=__a  ,     sampling_rate=__a  ,     padding_value=__a  ,     **__a				)

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

       _SCREAMING_SNAKE_CASE   :				Any							    = win_length * sampling_rate // 1000
       _SCREAMING_SNAKE_CASE   :				Any							    = hop_length * sampling_rate // 1000

       _SCREAMING_SNAKE_CASE   :				Dict							    = optimal_fft_length(self.sample_size				)
       _SCREAMING_SNAKE_CASE   :				Any							    = (self.n_fft // 2) + 1
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       if self.win_function == "hamming_window":
         _SCREAMING_SNAKE_CASE   :				str							    = window_function(window_length=self.sample_size  ,     name=self.win_function  ,     periodic=__a				)
       else:
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = window_function(window_length=self.sample_size  ,     name=self.win_function				)

       _SCREAMING_SNAKE_CASE   :				List[str]							    = 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   :				Dict							    = spectrogram(
           one_waveform * self.frame_signal_scale  ,     window=__a  ,     frame_length=self.sample_size  ,     hop_length=self.sample_stride  ,     fft_length=self.n_fft  ,     center=__a  ,     preemphasis=self.preemphasis_coeff  ,     mel_filters=__a  ,     mel_floor=self.mel_floor  ,     log_mel="log"  ,     )
       return msfc_features.T
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       if self.normalize_means:
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = x[:input_length].mean(axis=0				)
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = np.subtract(__a  ,     __a				)
       if self.normalize_vars:
         _SCREAMING_SNAKE_CASE   :				int							    = x[:input_length].std(axis=0				)
         _SCREAMING_SNAKE_CASE   :				List[str]							    = np.divide(__a  ,     __a				)

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

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

       return x
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = None				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = attention_mask.sum(-1				) if attention_mask is not None else [x.shape[0] for x in input_features]
       return [self._normalize_one(__a  ,     __a  ,     self.padding_value				) for x, n in zip(__a  ,     __a				)]




     def __call__(     self  ,     snake_case__  ,     snake_case__ = False  ,     snake_case__ = None  ,     snake_case__ = False  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     **snake_case__  ,     ):




       """simple docstring"""


       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   :				str							    = isinstance(__a  ,     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   :				int							    = is_batched_numpy or (
           isinstance(__a  ,     (list, tuple)				) and (isinstance(raw_speech[0]  ,     (np.ndarray, tuple, list)				))
       )

       if is_batched:
         _SCREAMING_SNAKE_CASE   :				str							    = [np.asarray(__a  ,     dtype=np.floataa				) for speech in raw_speech]
       elif not is_batched and not isinstance(__a  ,     np.ndarray				):
         _SCREAMING_SNAKE_CASE   :				str							    = np.asarray(__a  ,     dtype=np.floataa				)
       elif isinstance(__a  ,     np.ndarray				) and raw_speech.dtype is np.dtype(np.floataa				):
         _SCREAMING_SNAKE_CASE   :				List[str]							    = raw_speech.astype(np.floataa				)

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

       # extract fbank features
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = [self._extract_mfsc_features(__a				) for one_waveform in raw_speech]

       # convert into correct format for padding
       _SCREAMING_SNAKE_CASE   :				Dict							    = BatchFeature({"input_features": features}				)

       _SCREAMING_SNAKE_CASE   :				Dict							    = self.pad(
           __a  ,     padding=__a  ,     max_length=__a  ,     truncation=__a  ,     pad_to_multiple_of=__a  ,     return_attention_mask=__a  ,     **__a  ,     )
       # make sure list is in array format
       _SCREAMING_SNAKE_CASE   :				int							    = padded_inputs.get("input_features"				)
       if isinstance(input_features[0]  ,     __a				):
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = [np.asarray(__a  ,     dtype=np.floataa				) for feature in input_features]

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = padded_inputs.get("attention_mask"				)
       if attention_mask is not None:
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = [np.asarray(__a  ,     dtype=np.intaa				) for array in attention_mask]

       if self.normalize_means or self.normalize_vars:
         _SCREAMING_SNAKE_CASE   :				Tuple							    = (
             np.array(__a  ,     dtype=np.intaa				)
             if self._get_padding_strategies(__a  ,     max_length=__a				) is not PaddingStrategy.DO_NOT_PAD
             and padding
             else None
         )
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.normalize(
             padded_inputs["input_features"]  ,     attention_mask=__a				)

       if return_tensors is not None:
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = padded_inputs.convert_to_tensors(__a				)

       return padded_inputs





"""simple docstring"""
from __future__ import annotations

lowercase_       :						List[str]     					=	'''#'''
class 			UpperCamelCase      :
     def __init__(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				dict							    = {}
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = self._trie
       for char in text:
         if char not in trie:
           _SCREAMING_SNAKE_CASE   :				List[str]							    = {}
         _SCREAMING_SNAKE_CASE   :				List[str]							    = trie[char]
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = True
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self._trie
       for char in prefix:
         if char in trie:
           _SCREAMING_SNAKE_CASE   :				str							    = trie[char]
         else:
           return []
       return self._elements(snake_case__				)




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = []
       for c, v in d.items():
         _SCREAMING_SNAKE_CASE   :				int							    = [" "] if c == END else [(c + s) for s in self._elements(snake_case__				)]
         result.extend(snake_case__				)
       return tuple(snake_case__				)
lowercase_       :						Union[str, Any]     					=	Trie()
lowercase_       :						Optional[Any]     					=	('''depart''', '''detergent''', '''daring''', '''dog''', '''deer''', '''deal''')
for word in words:
      trie.insert_word(word)





def    _lowerCAmelCase    (     lowerCamelCase__							:							str				)  ->    tuple:
  _SCREAMING_SNAKE_CASE   :				Dict							    = trie.find_word(lowerCamelCase__				)
  return tuple(string + word for word in suffixes				)





def    _lowerCAmelCase    (     )  ->    None:
  print(autocomplete_using_trie("de"				)				)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()



