"""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


__lowerCAmelCase					: Tuple      =logging.get_logger(__name__)





class 				_A		( _UpperCAmelCase			):
      snake_case__    :     Tuple													=			['audio_values', 'audio_mask']

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







            """simple docstring"""



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

            lowercase														=			spectrogram_length
            lowercase														=			num_channels
            lowercase														=			patch_size
            lowercase														=			feature_size // self.patch_size[1]
            lowercase														=			n_fft
            lowercase														=			sampling_rate // hop_length_to_sampling_rate
            lowercase														=			sampling_rate
            lowercase														=			padding_value
            lowercase														=			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        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			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	, )
            lowercase														=			log_spec[:, :-1]
            lowercase														=			log_spec - 20.0
            lowercase														=			np.clip(log_spec / 40.0	, -2.0	, 0.0		) + 1.0
            return log_spec







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







            """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."""		)

            lowercase														=			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}'		)
            lowercase														=			is_batched_numpy or (
                isinstance(__UpperCamelCase	, (list, tuple)		) and (isinstance(raw_speech[0]	, (np.ndarray, tuple, list)		))
            )
            if is_batched:
                  lowercase														=			[np.asarray([speech]	, dtype=np.floataa		).T for speech in raw_speech]
            elif not is_batched and not isinstance(__UpperCamelCase	, np.ndarray		):
                  lowercase														=			np.asarray(__UpperCamelCase	, dtype=np.floataa		)
            elif isinstance(__UpperCamelCase	, 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([raw_speech]		).T]

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

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

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

            # return as BatchFeature
            if return_attention_mask:
                  lowercase														=			{"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
            else:
                  lowercase														=			{"""audio_values""": padded_audio_features}

            lowercase														=			BatchFeature(data=__UpperCamelCase	, tensor_type=__UpperCamelCase		)
            return encoded_inputs




"""simple docstring"""






# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


__lowerCAmelCase					: Union[str, Any]        =subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
__lowerCAmelCase					: List[str]        =subprocess.check_output(F"""git diff --name-only {fork_point_sha}""".split()).decode("""utf-8""").split()

__lowerCAmelCase					: List[str]        ="""|""".join(sys.argv[1:])
__lowerCAmelCase					: Dict        =re.compile(RF"""^({joined_dirs}).*?\.py$""")

__lowerCAmelCase					: int        =[x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")



