





import warnings

from ..trainer import Trainer
from ..utils import logging


__lowercase  :   Union[str, Any]						      =logging.get_logger(__name__)

class      A       (	__lowercase						):



						def __init__(	self:						int		,  _lowerCAmelCase:						Any=None		,  **_lowerCAmelCase:						Optional[Any]       )						->    str:
									'''simple docstring'''

									warnings.warn(
									    "`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` "
									    "instead."		,  __A		,  )
									super().__init__(args=__A		,  **__A       )











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  :   Union[str, Any]						      =logging.get_logger(__name__)

class      A       (	__lowercase						):
      _snake_case							     =['''input_features''', '''attention_mask''']



      def __init__(	self:						Dict		,  _lowerCAmelCase:						List[str]=80		,  _lowerCAmelCase:						Optional[Any]=1_6000		,  _lowerCAmelCase:						Any=0.0		,  _lowerCAmelCase:						Optional[Any]=10		,  _lowerCAmelCase:						Optional[Any]=25		,  _lowerCAmelCase:						Union[str, Any]="hamming_window"		,  _lowerCAmelCase:						Optional[int]=3_27_68.0		,  _lowerCAmelCase:						Optional[Any]=0.97		,  _lowerCAmelCase:						Any=1.0		,  _lowerCAmelCase:						List[str]=True		,  _lowerCAmelCase:						Dict=True		,  _lowerCAmelCase:						Any=False		,  **_lowerCAmelCase:						Any		,  )						->    Dict:
         '''simple docstring'''

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

         UpperCAmelCase_											=feature_size
         UpperCAmelCase_											=sampling_rate
         UpperCAmelCase_											=padding_value
         UpperCAmelCase_											=hop_length
         UpperCAmelCase_											=win_length
         UpperCAmelCase_											=frame_signal_scale
         UpperCAmelCase_											=preemphasis_coeff
         UpperCAmelCase_											=mel_floor
         UpperCAmelCase_											=normalize_means
         UpperCAmelCase_											=normalize_vars
         UpperCAmelCase_											=win_function
         UpperCAmelCase_											=return_attention_mask

         UpperCAmelCase_											=win_length * sampling_rate // 1000
         UpperCAmelCase_											=hop_length * sampling_rate // 1000

         UpperCAmelCase_											=optimal_fft_length(self.sample_size       )
         UpperCAmelCase_											=(self.n_fft // 2) + 1



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						np.array       )						->    np.ndarray:
         '''simple docstring'''

         if self.win_function == "hamming_window":
            UpperCAmelCase_											=window_function(window_length=self.sample_size		,  name=self.win_function		,  periodic=_lowerCAmelCase       )
         else:
            UpperCAmelCase_											=window_function(window_length=self.sample_size		,  name=self.win_function       )

         UpperCAmelCase_											=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		,  )

         UpperCAmelCase_											=spectrogram(
             one_waveform * self.frame_signal_scale		,  window=_lowerCAmelCase		,  frame_length=self.sample_size		,  hop_length=self.sample_stride		,  fft_length=self.n_fft		,  center=_lowerCAmelCase		,  preemphasis=self.preemphasis_coeff		,  mel_filters=_lowerCAmelCase		,  mel_floor=self.mel_floor		,  log_mel="log"		,  )
         return msfc_features.T



      def 	lowerCAmelCase__    (	self:						Dict		,  _lowerCAmelCase:						List[Any]		,  _lowerCAmelCase:						Dict		,  _lowerCAmelCase:						Any       )						->    Any:
         '''simple docstring'''

         if self.normalize_means:
            UpperCAmelCase_											=x[:input_length].mean(axis=0       )
            UpperCAmelCase_											=np.subtract(_lowerCAmelCase		,  _lowerCAmelCase       )
         if self.normalize_vars:
            UpperCAmelCase_											=x[:input_length].std(axis=0       )
            UpperCAmelCase_											=np.divide(_lowerCAmelCase		,  _lowerCAmelCase       )

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

         # make sure array is in float32
         UpperCAmelCase_											=x.astype(np.floataa       )

         return x



      def 	lowerCAmelCase__    (	self:						List[str]		,  _lowerCAmelCase:						List[np.ndarray]		,  _lowerCAmelCase:						Optional[np.ndarray] = None       )						->    List[np.ndarray]:
         '''simple docstring'''

         UpperCAmelCase_											=attention_mask.sum(-1       ) if attention_mask is not None else [x.shape[0] for x in input_features]
         return [self._normalize_one(_lowerCAmelCase		,  _lowerCAmelCase		,  self.padding_value       ) for x, n in zip(_lowerCAmelCase		,  _lowerCAmelCase       )]






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

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

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

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

         # extract fbank features
         UpperCAmelCase_											=[self._extract_mfsc_features(_lowerCAmelCase       ) for one_waveform in raw_speech]

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

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

         UpperCAmelCase_											=padded_inputs.get("attention_mask"       )
         if attention_mask is not None:
            UpperCAmelCase_											=[np.asarray(_lowerCAmelCase		,  dtype=np.intaa       ) for array in attention_mask]

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

         if return_tensors is not None:
            UpperCAmelCase_											=padded_inputs.convert_to_tensors(_lowerCAmelCase       )

         return padded_inputs




