





from math import ceil
from typing import List, Optional, Union

import numpy as np

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


_lowerCAmelCase 				=     logging.get_logger(__name__)

class    _UpperCAmelCase							(		_lowerCamelCase     ):

      '''simple docstring'''





      a						       =							['''audio_values''', '''audio_mask''']
      def __init__(		self      ,       a__=2048      ,       a__=1      ,       a__=[16, 16]      ,       a__=128      ,       a__=44100      ,       a__=86      ,       a__=2048      ,       a__=0.0      ,       **a__      ,       ):
         super().__init__(
             feature_size=a__      ,       sampling_rate=a__      ,       padding_value=a__      ,       **a__      ,       )

         A_     :				Dict			      =	spectrogram_length
         A_     :				Tuple			      =	num_channels
         A_     :				Any			      =	patch_size
         A_     :				str			      =	feature_size // self.patch_size[1]
         A_     :				List[Any]			      =	n_fft
         A_     :				Optional[Any]			      =	sampling_rate // hop_length_to_sampling_rate
         A_     :				List[Any]			      =	sampling_rate
         A_     :				Optional[Any]			      =	padding_value
         A_     :				Any			      =	mel_filter_bank(
             num_frequency_bins=1 + n_fft // 2      ,       num_mel_filters=a__      ,       min_frequency=0.0      ,       max_frequency=22050.0      ,       sampling_rate=a__      ,       norm="""slaney"""      ,       mel_scale="""slaney"""      ,       ).T
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				List[Any]			      =	spectrogram(
             a__      ,       window_function(self.n_fft      ,       """hann"""       )      ,       frame_length=self.n_fft      ,       hop_length=self.hop_length      ,       power=2.0      ,       mel_filters=self.mel_filters.T      ,       log_mel="""dB"""      ,       db_range=80.0      ,       )
         A_     :				Dict			      =	log_spec[:, :-1]
         A_     :				str			      =	log_spec - 20.0
         A_     :				List[Any]			      =	np.clip(log_spec / 40.0      ,       -2.0      ,       0.0       ) + 1.0
         return log_spec






      def __call__(		self      ,       a__      ,       a__ = None      ,       a__ = True      ,       a__ = None      ,       a__ = False      ,       a__ = False      ,       **a__      ,       ):

         if sampling_rate is not None:
            if sampling_rate != self.sampling_rate:
               raise ValueError(
                   """This feature extractor is set to support sampling rate"""
                   F""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
                   F""" 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."""       )

         A_     :				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}"""       )
         A_     :				List[str]			      =	is_batched_numpy or (
             isinstance(a__      ,       (list, tuple)       ) and (isinstance(raw_speech[0]      ,       (np.ndarray, tuple, list)       ))
         )
         if is_batched:
            A_     :				Any			      =	[np.asarray([speech]      ,       dtype=np.floataa       ).T for speech in raw_speech]
         elif not is_batched and not isinstance(a__      ,       np.ndarray       ):
            A_     :				Dict			      =	np.asarray(a__      ,       dtype=np.floataa       )
         elif isinstance(a__      ,       np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
            A_     :				int			      =	raw_speech.astype(np.floataa       )
         # always return batch
         if not is_batched:
            A_     :				Optional[int]			      =	[np.asarray([raw_speech]       ).T]

         # Convert audio signals to log mel spectrograms, truncate by time axis
         A_     :				List[str]			      =	[
             self._np_extract_fbank_features(waveform.squeeze()       ).T[: self.spectrogram_length] for waveform in raw_speech
         ]
         if isinstance(audio_features[0]      ,       a__       ):
            A_     :				Dict			      =	[np.asarray(a__      ,       dtype=np.floataa       ) for feature in audio_features]

         # Create audio attention mask
         A_     :				Dict			      =	max(
             [ceil(feature.shape[0] / self.patch_size[0]       ) * self.freq_len for feature in audio_features]       )  # The maximum number of audio patches in a batch
         if return_attention_mask:
            A_     :				List[str]			      =	[
                (ceil(feature.shape[0] / self.patch_size[0]       ) * self.freq_len) * [1]
                + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]       ) * self.freq_len) * [0]
                for feature in audio_features
            ]
            A_     :				List[Any]			      =	np.array(a__       ).astype(np.floataa       )

         # convert into correct format for padding
         A_     :				int			      =	max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
         A_     :				Optional[int]			      =	np.ones([len(a__       ), 1, max_time_len, self.feature_size]       ).astype(np.floataa       )
         A_     :				str			      =	padded_audio_features * self.padding_value
         for i in range(len(a__       )       ):
            A_     :				Union[str, Any]			      =	audio_features[i]
            A_     :				str			      =	feature

         # return as BatchFeature
         if return_attention_mask:
            A_     :				Any			      =	{"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
         else:
            A_     :				Optional[Any]			      =	{"""audio_values""": padded_audio_features}

         A_     :				List[Any]			      =	BatchFeature(data=a__      ,       tensor_type=a__       )
         return encoded_inputs













from typing import List

from .keymap import KEYMAP, get_character



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   def decorator(_lowerCAmelCase     ):
      A_     :				List[Any]			      =	getattr(_lowerCAmelCase  ,"""handle_key"""  ,[]     )
      handle += [key]
      setattr(_lowerCAmelCase  ,"""handle_key"""  ,_lowerCAmelCase     )
      return func

   return decorator



def     _lowerCAmelCase		(  *_lowerCAmelCase     ):
   '''simple docstring'''


   def decorator(_lowerCAmelCase     ):
      A_     :				Tuple			      =	getattr(_lowerCAmelCase  ,"""handle_key"""  ,[]     )
      handle += keys
      setattr(_lowerCAmelCase  ,"""handle_key"""  ,_lowerCAmelCase     )
      return func

   return decorator

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      def __new__(		cls      ,       a__      ,       a__      ,       a__       ):
         A_     :				Any			      =	super().__new__(cls      ,       a__      ,       a__      ,       a__       )
         if not hasattr(a__      ,       """key_handler"""       ):
            setattr(a__      ,       """key_handler"""      ,       {}       )
         setattr(a__      ,       """handle_input"""      ,       KeyHandler.handle_input       )

         for value in attrs.values():
            A_     :				Tuple			      =	getattr(a__      ,       """handle_key"""      ,       []       )
            for key in handled_keys:
               A_     :				Optional[Any]			      =	value
         return new_cls






      @staticmethod
      def 		_lowerCamelCase      (		cls       ):
         A_     :				List[str]			      =	get_character()
         if char != KEYMAP["undefined"]:
            A_     :				str			      =	ord(a__       )
         A_     :				List[str]			      =	cls.key_handler.get(a__       )
         if handler:
            A_     :				Optional[int]			      =	char
            return handler(cls       )
         else:
            return None






def     _lowerCAmelCase		(  cls     ):
   '''simple docstring'''


   return KeyHandler(cls.__name__  ,cls.__bases__  ,cls.__dict__.copy()     )






