


'''simple docstring'''



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


__UpperCAmelCase      :Tuple			    =       logging.get_logger(__name__)



class      a    (		_a	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			List[Any]							       =       ["input_features", "attention_mask"]

     def __init__(    self					:		List[Any]							,			snake_case					:		Any=80							,			snake_case					:		Optional[int]=1_6000							,			snake_case					:		Any=0.0							,			snake_case					:		Optional[Any]=10							,			snake_case					:		int=25							,			snake_case					:		Union[str, Any]="hamming_window"							,			snake_case					:		str=32_768.0							,			snake_case					:		List[str]=0.97							,			snake_case					:		Dict=1.0							,			snake_case					:		Tuple=True							,			snake_case					:		Any=True							,			snake_case					:		Optional[int]=False							,			**snake_case					:		List[str]							,			)						->							List[str]:
          super().__init__(feature_size=snake_case							,			sampling_rate=snake_case							,			padding_value=snake_case							,			**snake_case      )

          __UpperCAmelCase     :			Dict  							=      feature_size
          __UpperCAmelCase     :			Any  							=      sampling_rate
          __UpperCAmelCase     :			Tuple  							=      padding_value
          __UpperCAmelCase     :			Optional[int]  							=      hop_length
          __UpperCAmelCase     :			str  							=      win_length
          __UpperCAmelCase     :			Tuple  							=      frame_signal_scale
          __UpperCAmelCase     :			Tuple  							=      preemphasis_coeff
          __UpperCAmelCase     :			Any  							=      mel_floor
          __UpperCAmelCase     :			Dict  							=      normalize_means
          __UpperCAmelCase     :			Union[str, Any]  							=      normalize_vars
          __UpperCAmelCase     :			Optional[int]  							=      win_function
          __UpperCAmelCase     :			Tuple  							=      return_attention_mask

          __UpperCAmelCase     :			int  							=      win_length * sampling_rate // 1000
          __UpperCAmelCase     :			List[Any]  							=      hop_length * sampling_rate // 1000

          __UpperCAmelCase     :			List[Any]  							=      optimal_fft_length(self.sample_size      )
          __UpperCAmelCase     :			int  							=      (self.n_fft // 2) + 1

     def 			lowerCamelCase__			(    self					:		Tuple							,			snake_case					:		np.array      )						->							np.ndarray:
          if self.win_function == "hamming_window":
               __UpperCAmelCase     :			Union[str, Any]  							=      window_function(window_length=self.sample_size							,			name=self.win_function							,			periodic=snake_case      )
          else:
               __UpperCAmelCase     :			Optional[int]  							=      window_function(window_length=self.sample_size							,			name=self.win_function      )

          __UpperCAmelCase     :			Tuple  							=      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     :			int  							=      spectrogram(
              one_waveform * self.frame_signal_scale							,			window=snake_case							,			frame_length=self.sample_size							,			hop_length=self.sample_stride							,			fft_length=self.n_fft							,			center=snake_case							,			preemphasis=self.preemphasis_coeff							,			mel_filters=snake_case							,			mel_floor=self.mel_floor							,			log_mel='''log'''							,			)
          return msfc_features.T

     def 			lowerCamelCase__			(    self					:		str							,			snake_case					:		Optional[int]							,			snake_case					:		Any							,			snake_case					:		int      )						->							Dict:
          # make sure we normalize float32 arrays
          if self.normalize_means:
               __UpperCAmelCase     :			int  							=      x[:input_length].mean(axis=0      )
               __UpperCAmelCase     :			List[str]  							=      np.subtract(snake_case							,			snake_case      )
          if self.normalize_vars:
               __UpperCAmelCase     :			List[str]  							=      x[:input_length].std(axis=0      )
               __UpperCAmelCase     :			Dict  							=      np.divide(snake_case							,			snake_case      )

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

          # make sure array is in float32
          __UpperCAmelCase     :			Any  							=      x.astype(np.floataa      )

          return x

     def 			lowerCamelCase__			(    self					:		Union[str, Any]							,			snake_case					:		List[np.ndarray]							,			snake_case					:		Optional[np.ndarray] = None      )						->							List[np.ndarray]:
          __UpperCAmelCase     :			Union[str, Any]  							=      attention_mask.sum(-1      ) if attention_mask is not None else [x.shape[0] for x in input_features]
          return [self._normalize_one(snake_case							,			snake_case							,			self.padding_value      ) for x, n in zip(snake_case							,			snake_case      )]






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

          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     :			List[Any]  							=      isinstance(snake_case							,			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     :			List[Any]  							=      is_batched_numpy or (
              isinstance(snake_case							,			(list, tuple)      ) and (isinstance(raw_speech[0]							,			(np.ndarray, tuple, list)      ))
          )

          if is_batched:
               __UpperCAmelCase     :			Union[str, Any]  							=      [np.asarray(snake_case							,			dtype=np.floataa      ) for speech in raw_speech]
          elif not is_batched and not isinstance(snake_case							,			np.ndarray      ):
               __UpperCAmelCase     :			str  							=      np.asarray(snake_case							,			dtype=np.floataa      )
          elif isinstance(snake_case							,			np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
               __UpperCAmelCase     :			Tuple  							=      raw_speech.astype(np.floataa      )

          # always return batch
          if not is_batched:
               __UpperCAmelCase     :			List[str]  							=      [raw_speech]

          # extract fbank features
          __UpperCAmelCase     :			Any  							=      [self._extract_mfsc_features(snake_case      ) for one_waveform in raw_speech]

          # convert into correct format for padding
          __UpperCAmelCase     :			Union[str, Any]  							=      BatchFeature({'''input_features''': features}      )

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

          __UpperCAmelCase     :			Union[str, Any]  							=      padded_inputs.get('''attention_mask'''      )
          if attention_mask is not None:
               __UpperCAmelCase     :			Any  							=      [np.asarray(snake_case							,			dtype=np.intaa      ) for array in attention_mask]

          if self.normalize_means or self.normalize_vars:
               __UpperCAmelCase     :			Optional[Any]  							=      (
                   np.array(snake_case							,			dtype=np.intaa      )
                   if self._get_padding_strategies(snake_case							,			max_length=snake_case      ) is not PaddingStrategy.DO_NOT_PAD
                   and padding
                   else None
               )
               __UpperCAmelCase     :			Tuple  							=      self.normalize(
                   padded_inputs['''input_features''']							,			attention_mask=snake_case      )

          if return_tensors is not None:
               __UpperCAmelCase     :			List[str]  							=      padded_inputs.convert_to_tensors(snake_case      )

          return padded_inputs



'''simple docstring'''



from argparse import ArgumentParser

from .env import EnvironmentCommand




def      _a     (						):





     '''simple docstring'''






     __UpperCAmelCase     :			Optional[Any]  							=      ArgumentParser('''Diffusers CLI tool'''      , usage='''diffusers-cli <command> [<args>]'''  )
     __UpperCAmelCase     :			Union[str, Any]  							=      parser.add_subparsers(help='''diffusers-cli command helpers'''  )

     # Register commands
     EnvironmentCommand.register_subcommand(_lowercase  )

     # Let's go
     __UpperCAmelCase     :			Union[str, Any]  							=      parser.parse_args()

     if not hasattr(_lowercase      , '''func'''  ):
          parser.print_help()
          exit(1  )

     # Run
     __UpperCAmelCase     :			Any  							=      args.func(_lowercase  )
     service.run()


if __name__ == "__main__":
      main()