





'''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       		=      logging.get_logger(__name__)
class 		lowerCamelCase  (      lowercase__       ):
 snake_case_      =   ["audio_values", "audio_mask"]



 def __init__(   self		,      a_=2_048		,      a_=1		,      a_=[16, 16]		,      a_=128		,      a_=44_100		,      a_=86		,      a_=2_048		,      a_=0.0		,      **a_		,      ):
   super().__init__(
       feature_size=__lowerCamelCase		,      sampling_rate=__lowerCamelCase		,      padding_value=__lowerCamelCase		,      **__lowerCamelCase		,      )

   lowerCAmelCase     :    Dict						=   spectrogram_length
   lowerCAmelCase     :    List[str]						=   num_channels
   lowerCAmelCase     :    str						=   patch_size
   lowerCAmelCase     :    Dict						=   feature_size // self.patch_size[1]
   lowerCAmelCase     :    List[Any]						=   n_fft
   lowerCAmelCase     :    str						=   sampling_rate // hop_length_to_sampling_rate
   lowerCAmelCase     :    Optional[int]						=   sampling_rate
   lowerCAmelCase     :    Tuple						=   padding_value
   lowerCAmelCase     :    Dict						=   mel_filter_bank(
       num_frequency_bins=1 + n_fft // 2		,      num_mel_filters=__lowerCamelCase		,      min_frequency=0.0		,      max_frequency=22_050.0		,      sampling_rate=__lowerCamelCase		,      norm="slaney"		,      mel_scale="slaney"		,      ).T



 def   _lowerCamelCase    (   self		,      a_	):
   lowerCAmelCase     :    Optional[int]						=   spectrogram(
       __lowerCamelCase		,      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     :    str						=   log_spec[:, :-1]
   lowerCAmelCase     :    str						=   log_spec - 20.0
   lowerCAmelCase     :    int						=   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."	)

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

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

   # Create audio attention mask
   lowerCAmelCase     :    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:
     lowerCAmelCase     :    Optional[Any]						=   [
         (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     :    Tuple						=   np.array(__lowerCamelCase	).astype(np.floataa	)

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

   # return as BatchFeature
   if return_attention_mask:
     lowerCAmelCase     :    Any						=   {"audio_values": padded_audio_features, "audio_mask": audio_mask}
   else:
     lowerCAmelCase     :    List[str]						=   {"audio_values": padded_audio_features}

   lowerCAmelCase     :    Any						=   BatchFeature(data=__lowerCamelCase		,      tensor_type=__lowerCamelCase	)
   return encoded_inputs










'''simple docstring'''






def      __A						(							a_						:  int      ,a_						:  int	):
		return abs(a_	) if a == 0 else greatest_common_divisor(b % a      ,a_	)






def      __A						(							a_						:  int      ,a_						:  int	):
		while y:  # --> when y=0 then loop will terminate and return x as final GCD.
				lowerCAmelCase	,		lowerCAmelCase     :    Tuple						=   y, x % y
		return abs(a_	)






def      __A						(							):
		try:
				lowerCAmelCase     :    Tuple						=   input("Enter two integers separated by comma (,): "	).split(","	)
				lowerCAmelCase     :    Tuple						=   int(nums[0]	)
				lowerCAmelCase     :    Tuple						=   int(nums[1]	)
				print(
				    f'''greatest_common_divisor({num_a}, {num_a}) = '''
				    f'''{greatest_common_divisor(a_      ,a_	)}'''	)
				print(f'''By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(a_      ,a_	)}'''	)
		except (IndexError, UnboundLocalError, ValueError):
				print("Wrong input"	)


if __name__ == "__main__":
					main()



