


'''simple docstring'''

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


a_    :							Dict         =					logging.get_logger(__name__)





class 	snake_case       (       SCREAMING_SNAKE_CASE_     ):





  """simple docstring"""




  _lowerCamelCase						       =    ["""audio_values""", """audio_mask"""]






  def __init__(       self     ,    UpperCamelCase=2048     ,    UpperCamelCase=1     ,    UpperCamelCase=[16, 16]     ,    UpperCamelCase=128     ,    UpperCamelCase=4_4100     ,    UpperCamelCase=86     ,    UpperCamelCase=2048     ,    UpperCamelCase=0.0     ,    **UpperCamelCase     ,    ):







   """simple docstring"""
   super().__init__(
       feature_size=UpperCamelCase     ,    sampling_rate=UpperCamelCase     ,    padding_value=UpperCamelCase     ,    **UpperCamelCase     ,    )

   lowerCamelCase_						=    spectrogram_length
   lowerCamelCase_						=    num_channels
   lowerCamelCase_						=    patch_size
   lowerCamelCase_						=    feature_size // self.patch_size[1]
   lowerCamelCase_						=    n_fft
   lowerCamelCase_						=    sampling_rate // hop_length_to_sampling_rate
   lowerCamelCase_						=    sampling_rate
   lowerCamelCase_						=    padding_value
   lowerCamelCase_						=    mel_filter_bank(
       num_frequency_bins=1 + n_fft // 2     ,    num_mel_filters=UpperCamelCase     ,    min_frequency=0.0     ,    max_frequency=2_2050.0     ,    sampling_rate=UpperCamelCase     ,    norm="slaney"     ,    mel_scale="slaney"     ,    ).T






  def     snake_case						(       self     ,    UpperCamelCase	):







   """simple docstring"""
   lowerCamelCase_						=    spectrogram(
       UpperCamelCase     ,    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     ,    )
   lowerCamelCase_						=    log_spec[:, :-1]
   lowerCamelCase_						=    log_spec - 20.0
   lowerCamelCase_						=    np.clip(log_spec / 40.0     ,    -2.0     ,    0.0	) + 1.0
   return log_spec







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







   """simple docstring"""

   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."	)

   lowerCamelCase_						=    isinstance(UpperCamelCase     ,    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}'''	)
   lowerCamelCase_						=    is_batched_numpy or (
       isinstance(UpperCamelCase     ,    (list, tuple)	) and (isinstance(raw_speech[0]     ,    (np.ndarray, tuple, list)	))
   )
   if is_batched:
    lowerCamelCase_						=    [np.asarray([speech]     ,    dtype=np.floataa	).T for speech in raw_speech]
   elif not is_batched and not isinstance(UpperCamelCase     ,    np.ndarray	):
    lowerCamelCase_						=    np.asarray(UpperCamelCase     ,    dtype=np.floataa	)
   elif isinstance(UpperCamelCase     ,    np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
    lowerCamelCase_						=    raw_speech.astype(np.floataa	)
   # always return batch
   if not is_batched:
    lowerCamelCase_						=    [np.asarray([raw_speech]	).T]

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

   # Create audio attention mask
   lowerCamelCase_						=    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:
    lowerCamelCase_						=    [
        (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
    ]
    lowerCamelCase_						=    np.array(UpperCamelCase	).astype(np.floataa	)

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

   # return as BatchFeature
   if return_attention_mask:
    lowerCamelCase_						=    {'audio_values': padded_audio_features, 'audio_mask': audio_mask}
   else:
    lowerCamelCase_						=    {'audio_values': padded_audio_features}

   lowerCamelCase_						=    BatchFeature(data=UpperCamelCase     ,    tensor_type=UpperCamelCase	)
   return encoded_inputs








"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


a_							=    logging.get_logger(__name__)







class 					A_(SCREAMING_SNAKE_CASE_      ):







			"""simple docstring"""



			def __init__(	self				,   *A				,   **A							):
						warnings.warn(
						    'The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
						    ' Please use SegformerImageProcessor instead.'				,   A				,   )
						super().__init__(*A				,   **A							)





