



"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

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


class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   ["input_features", "is_longer"]

    def __init__(				self			:				int ,  lowerCAmelCase_			:				int=6_4 ,  lowerCAmelCase_			:				List[Any]=4_8_0_0_0 ,  lowerCAmelCase_			:				Any=4_8_0 ,  lowerCAmelCase_			:				Optional[int]=1_0 ,  lowerCAmelCase_			:				Optional[int]=1_0_2_4 ,  lowerCAmelCase_			:				List[Any]=0.0 ,  lowerCAmelCase_			:				Any=False ,  lowerCAmelCase_			:				float = 0 ,  lowerCAmelCase_			:				float = 1_4_0_0_0 ,  lowerCAmelCase_			:				int = None ,  lowerCAmelCase_			:				str = "fusion" ,  lowerCAmelCase_			:				str = "repeatpad" ,  **lowerCAmelCase_			:				Dict ,  ):







      """simple docstring"""







      super().__init__(
          feature_size=lowerCAmelCase_ ,  sampling_rate=lowerCAmelCase_ ,  padding_value=lowerCAmelCase_ ,  return_attention_mask=lowerCAmelCase_ ,  **lowerCAmelCase_ ,  )
      lowercase_								= top_db
      lowercase_								= truncation
      lowercase_								= padding
      lowercase_								= fft_window_size
      lowercase_								= (fft_window_size >> 1) + 1
      lowercase_								= hop_length
      lowercase_								= max_length_s
      lowercase_								= max_length_s * sampling_rate
      lowercase_								= sampling_rate
      lowercase_								= frequency_min
      lowercase_								= frequency_max
      lowercase_								= mel_filter_bank(
          num_frequency_bins=self.nb_frequency_bins ,  num_mel_filters=lowerCAmelCase_ ,  min_frequency=lowerCAmelCase_ ,  max_frequency=lowerCAmelCase_ ,  sampling_rate=lowerCAmelCase_ ,  norm=lowerCAmelCase_ ,  mel_scale="""htk""" ,  )
      lowercase_								= mel_filter_bank(
          num_frequency_bins=self.nb_frequency_bins ,  num_mel_filters=lowerCAmelCase_ ,  min_frequency=lowerCAmelCase_ ,  max_frequency=lowerCAmelCase_ ,  sampling_rate=lowerCAmelCase_ ,  norm="""slaney""" ,  mel_scale="""slaney""" ,  )

    def 					_UpperCAmelCase	(				self			:				Optional[int]):







      """simple docstring"""







      lowercase_								= copy.deepcopy(self.__dict__)
      lowercase_								= self.__class__.__name__
      if "mel_filters" in output:
        del output["mel_filters"]
      if "mel_filters_slaney" in output:
        del output["mel_filters_slaney"]
      return output

    def 					_UpperCAmelCase	(				self			:				Optional[Any] ,  lowerCAmelCase_			:				np.array ,  lowerCAmelCase_			:				Optional[np.array] = None):







      """simple docstring"""







      lowercase_								= spectrogram(
          lowerCAmelCase_ ,  window_function(self.fft_window_size ,  """hann""") ,  frame_length=self.fft_window_size ,  hop_length=self.hop_length ,  power=2.0 ,  mel_filters=lowerCAmelCase_ ,  log_mel="""dB""" ,  )
      return log_mel_spectrogram.T

    def 					_UpperCAmelCase	(				self			:				List[str] ,  lowerCAmelCase_			:				List[str] ,  lowerCAmelCase_			:				Union[str, Any] ,  lowerCAmelCase_			:				Optional[Any]):







      """simple docstring"""







      lowercase_								= np.array_split(list(range(0 ,  total_frames - chunk_frames + 1)) ,  3)
      if len(ranges[1]) == 0:
        # if the audio is too short, we just use the first chunk
        lowercase_								= [0]
      if len(ranges[2]) == 0:
        # if the audio is too short, we just use the first chunk
        lowercase_								= [0]
      # randomly choose index for each part
      lowercase_								= np.random.choice(ranges[0])
      lowercase_								= np.random.choice(ranges[1])
      lowercase_								= np.random.choice(ranges[2])

      lowercase_								= mel[idx_front : idx_front + chunk_frames, :]
      lowercase_								= mel[idx_middle : idx_middle + chunk_frames, :]
      lowercase_								= mel[idx_back : idx_back + chunk_frames, :]

      lowercase_								= torch.tensor(mel[None, None, :])
      lowercase_								= torch.nn.functional.interpolate(
          lowerCAmelCase_ ,  size=[chunk_frames, 6_4] ,  mode="""bilinear""" ,  align_corners=lowerCAmelCase_)
      lowercase_								= mel_shrink[0][0].numpy()
      lowercase_								= np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,  axis=0)
      return mel_fusion

    def 					_UpperCAmelCase	(				self			:				Optional[int] ,  lowerCAmelCase_			:				np.array ,  lowerCAmelCase_			:				List[str] ,  lowerCAmelCase_			:				List[str] ,  lowerCAmelCase_			:				Union[str, Any]):







      """simple docstring"""







      if waveform.shape[0] > max_length:
        if truncation == "rand_trunc":
          lowercase_								= True
          # random crop to max_length (for compatibility) -> this should be handled by self.pad
          lowercase_								= len(lowerCAmelCase_) - max_length
          lowercase_								= np.random.randint(0 ,  overflow + 1)
          lowercase_								= waveform[idx : idx + max_length]
          lowercase_								= self._np_extract_fbank_features(lowerCAmelCase_ ,  self.mel_filters_slaney)[None, :]
        elif truncation == "fusion":
          lowercase_								= self._np_extract_fbank_features(lowerCAmelCase_ ,  self.mel_filters)
          lowercase_								= max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
          lowercase_								= mel.shape[0]
          if chunk_frames == total_frames:
            # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
            # In this case, we just use the whole audio.
            lowercase_								= np.stack([mel, mel, mel, mel] ,  axis=0)
            lowercase_								= False
          else:
            lowercase_								= self._random_mel_fusion(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_)
            lowercase_								= True
        else:
          raise NotImplementedError(F'''data_truncating {truncation} not implemented''')

      else:
        lowercase_								= False
        # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
        if waveform.shape[0] < max_length:
          if padding == "repeat":
            lowercase_								= int(max_length / len(lowerCAmelCase_))
            lowercase_								= np.stack(np.tile(lowerCAmelCase_ ,  n_repeat + 1))[:max_length]
          if padding == "repeatpad":
            lowercase_								= int(max_length / len(lowerCAmelCase_))
            lowercase_								= np.stack(np.tile(lowerCAmelCase_ ,  lowerCAmelCase_))
          lowercase_								= np.pad(lowerCAmelCase_ ,  (0, max_length - waveform.shape[0]) ,  mode="""constant""" ,  constant_values=0)

        if truncation == "fusion":
          lowercase_								= self._np_extract_fbank_features(lowerCAmelCase_ ,  self.mel_filters)
          lowercase_								= np.stack([input_mel, input_mel, input_mel, input_mel] ,  axis=0)
        else:
          lowercase_								= self._np_extract_fbank_features(lowerCAmelCase_ ,  self.mel_filters_slaney)[None, :]

      return input_mel, longer




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







      """simple docstring"""







      lowercase_								= truncation if truncation is not None else self.truncation
      lowercase_								= padding if padding else self.padding

      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.""")

      lowercase_								= 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}''')
      lowercase_								= is_batched_numpy or (
          isinstance(lowerCAmelCase_ ,  (list, tuple)) and (isinstance(raw_speech[0] ,  (np.ndarray, tuple, list)))
      )

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

      # always return batch
      if not is_batched:
        lowercase_								= [np.asarray(lowerCAmelCase_)]

      # convert to mel spectrogram, truncate and pad if needed.
      lowercase_								= [
          self._get_input_mel(lowerCAmelCase_ ,  max_length if max_length else self.nb_max_samples ,  lowerCAmelCase_ ,  lowerCAmelCase_)
          for waveform in raw_speech
      ]

      lowercase_								= []
      lowercase_								= []
      for mel, longer in padded_inputs:
        input_mel.append(lowerCAmelCase_)
        is_longer.append(lowerCAmelCase_)

      if truncation == "fusion" and sum(lowerCAmelCase_) == 0:
        # if no audio is longer than 10s, then randomly select one audio to be longer
        lowercase_								= np.random.randint(0 ,  len(lowerCAmelCase_))
        lowercase_								= True

      if isinstance(input_mel[0] ,  lowerCAmelCase_):
        lowercase_								= [np.asarray(lowerCAmelCase_ ,  dtype=np.floataa) for feature in input_mel]

      # is_longer is a list of bool
      lowercase_								= [[longer] for longer in is_longer]

      lowercase_								= {"""input_features""": input_mel, """is_longer""": is_longer}
      lowercase_								= BatchFeature(lowerCAmelCase_)

      if return_tensors is not None:
        lowercase_								= input_features.convert_to_tensors(lowerCAmelCase_)

      return input_features






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

import math
from collections import Counter
from string import ascii_lowercase


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						None:







  '''simple docstring'''


  lowercase_					,    lowercase_								= analyze_text(__lowerCAmelCase							)
  lowercase_								= list(""" """ + ascii_lowercase							)
  # what is our total sum of probabilities.
  lowercase_								= sum(single_char_strings.values()							)

  # one length string
  lowercase_								= 0
  # for each alpha we go in our dict and if it is in it we calculate entropy
  for ch in my_alphas:
    if ch in single_char_strings:
      lowercase_								= single_char_strings[ch]
      lowercase_								= my_str / all_sum
      my_fir_sum += prob * math.loga(__lowerCAmelCase							)  # entropy formula.

    # print entropy
  print(F'''{round(-1 * my_fir_sum							):.1f}'''							)

  # two len string
  lowercase_								= sum(two_char_strings.values()							)
  lowercase_								= 0
  # for each alpha (two in size) calculate entropy.
  for cha in my_alphas:
    for cha in my_alphas:
      lowercase_								= cha + cha
      if sequence in two_char_strings:
        lowercase_								= two_char_strings[sequence]
        lowercase_								= int(__lowerCAmelCase							) / all_sum
        my_sec_sum += prob * math.loga(__lowerCAmelCase							)

    # print second entropy
  print(F'''{round(-1 * my_sec_sum							):.1f}'''							)

  # print the difference between them
  print(F'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum)							):.1f}'''							)


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						tuple[dict, dict]:







  '''simple docstring'''


  lowercase_								= Counter()  # type: ignore
  lowercase_								= Counter()  # type: ignore
  single_char_strings[text[-1]] += 1

  # first case when we have space at start.
  two_char_strings[" " + text[0]] += 1
  for i in range(0 ,  len(__lowerCAmelCase							) - 1							):
    single_char_strings[text[i]] += 1
    two_char_strings[text[i : i + 2]] += 1
  return single_char_strings, two_char_strings


def 			_SCREAMING_SNAKE_CASE						() ->						str:







  '''simple docstring'''


  import doctest

  doctest.testmod()
  # text = (
  #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
  #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
  #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
  #     "beloved evident through no service elderly is. Blind there if every no so "
  #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
  #     "of message cottage windows do besides against uncivil.  Delightful "
  #     "unreserved impossible few estimating men favourable see entreaties. She "
  #     "propriety immediate was improving. He or entrance humoured likewise "
  #     "moderate. Much nor game son say feel. Fat make met can must form into "
  #     "gate. Me we offending prevailed discovery. "
  # )

  # calculate_prob(text)


if __name__ == "__main__":
       main()

