




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


_snake_case										=       logging.get_logger(__name__)







class 						lowercase							(   UpperCamelCase__		):
     _a							 =						["audio_values", "audio_mask"]

     def __init__(  self		,     _a=2048		,     _a=1		,     _a=[16, 16]		,     _a=128		,     _a=4_4100		,     _a=86		,     _a=2048		,     _a=0.0		,     **_a		,     )						->							List[Any]:
           super().__init__(
               feature_size=_a		,     sampling_rate=_a		,     padding_value=_a		,     **_a		,     )

           _A							:     Any			=						spectrogram_length
           _A							:     Dict			=						num_channels
           _A							:     Optional[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[str]			=						sampling_rate
           _A							:     Union[str, Any]			=						padding_value
           _A							:     List[str]			=						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 					a__	(  self		,     _a				)						->							np.ndarray:
           _A							:     Optional[int]			=						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							:     Tuple			=						log_spec[:, :-1]
           _A							:     int			=						log_spec - 20.0
           _A							:     Tuple			=						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		,     )						->							BatchFeature:

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

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

           # Create audio attention mask
           _A							:     Any			=						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							:     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[str]			=						np.array(_a				).astype(np.floataa				)

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

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

           _A							:     int			=						BatchFeature(data=_a		,     tensor_type=_a				)
           return encoded_inputs








'''simple docstring'''

def 						UpperCamelCase				(							_lowerCamelCase					:					int | float | str       ):
    try:
        A__				=			float(_lowerCamelCase       )
    except ValueError:
        raise ValueError("Please enter a valid number"       )
    A__				=			decimal - int(_lowerCamelCase       )
    if fractional_part == 0:
        return int(_lowerCamelCase       ), 1
    else:
        A__				=			len(str(_lowerCamelCase       ).split("."       )[1]       )
        A__				=			int(decimal * (10**number_of_frac_digits)       )
        A__				=			10**number_of_frac_digits
        A__,	A__				=			denominator, numerator
        while True:
            A__				=			dividend % divisor
            if remainder == 0:
                break
            A__,	A__				=			divisor, remainder
        A__,	A__				=			numerator / divisor, denominator / divisor
        return int(_lowerCamelCase       ), int(_lowerCamelCase       )


if __name__ == "__main__":
  print(f"""{decimal_to_fraction(2) = }""")
  print(f"""{decimal_to_fraction(89.0) = }""")
  print(f"""{decimal_to_fraction("67") = }""")
  print(f"""{decimal_to_fraction("45.0") = }""")
  print(f"""{decimal_to_fraction(1.5) = }""")
  print(f"""{decimal_to_fraction("6.25") = }""")
  print(f"""{decimal_to_fraction("78td") = }""")



