






'''simple docstring'''

import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


UpperCAmelCase__						:List[Any]          =			logging.get_logger(__name__)







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):
       snake_case__    :     int   		=			['input_features']




       def __init__(					self      : List[str]	,    A__      : Any=80	,    A__      : int=16000	,    A__      : Dict=160	,    A__      : Optional[int]=30	,    A__      : List[str]=400	,    A__      : Tuple=0.0	,    A__      : int=False	,    **A__      : Optional[Any]	,    ):


            """simple docstring"""







            super().__init__(
                feature_size=A__	,    sampling_rate=A__	,    padding_value=A__	,    return_attention_mask=A__	,    **A__	,    )
            __lowerCamelCase						:       Optional[Any]				   =				n_fft
            __lowerCamelCase						:       Tuple				   =				hop_length
            __lowerCamelCase						:       Optional[int]				   =				chunk_length
            __lowerCamelCase						:       Optional[Any]				   =				chunk_length * sampling_rate
            __lowerCamelCase						:       Optional[int]				   =				self.n_samples // hop_length
            __lowerCamelCase						:       List[str]				   =				sampling_rate
            __lowerCamelCase						:       Optional[int]				   =				mel_filter_bank(
                num_frequency_bins=1 + n_fft // 2	,    num_mel_filters=A__	,    min_frequency=0.0	,    max_frequency=8000.0	,    sampling_rate=A__	,    norm="""slaney"""	,    mel_scale="""slaney"""	,    )




       def 					a_					(					self      : Optional[int]	,    A__      : np.array					):


            """simple docstring"""







            __lowerCamelCase						:       str				   =				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	,    log_mel="""log10"""	,    )
            __lowerCamelCase						:       Optional[Any]				   =				log_spec[:, :-1]
            __lowerCamelCase						:       Tuple				   =				np.maximum(A__	,    log_spec.max() - 8.0					)
            __lowerCamelCase						:       Tuple				   =				(log_spec + 4.0) / 4.0
            return log_spec




       @staticmethod
       # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
       def 					a_					(					A__      : List[np.ndarray]	,    A__      : List[np.ndarray]	,    A__      : float = 0.0					):


            """simple docstring"""







            if attention_mask is not None:
                 __lowerCamelCase						:       List[Any]				   =				np.array(A__	,    np.intaa					)
                 __lowerCamelCase						:       List[Any]				   =				[]

                 for vector, length in zip(A__	,    attention_mask.sum(-1					)					):
                      __lowerCamelCase						:       Union[str, Any]				   =				(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7					)
                      if length < normed_slice.shape[0]:
                           __lowerCamelCase						:       List[Any]				   =				padding_value

                      normed_input_values.append(A__					)
            else:
                 __lowerCamelCase						:       Optional[int]				   =				[(x - x.mean()) / np.sqrt(x.var() + 1e-7					) for x in input_values]

            return normed_input_values




       def __call__(					self      : str	,    A__      : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,    A__      : bool = True	,    A__      : Optional[int] = None	,    A__      : Optional[Union[str, TensorType]] = None	,    A__      : Optional[bool] = None	,    A__      : Optional[str] = "max_length"	,    A__      : Optional[int] = None	,    A__      : Optional[int] = None	,    A__      : Optional[bool] = None	,    **A__      : str	,    ):


            """simple docstring"""







            if sampling_rate is not None:
                 if sampling_rate != self.sampling_rate:
                      raise ValueError(
                          f"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
                          f" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
                          f" was sampled 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						:       Optional[Any]				   =				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}"					)
            __lowerCamelCase						:       Any				   =				is_batched_numpy or (
                isinstance(A__	,    (list, tuple)					) and (isinstance(raw_speech[0]	,    (np.ndarray, tuple, list)					))
            )

            if is_batched:
                 __lowerCamelCase						:       List[Any]				   =				[np.asarray([speech]	,    dtype=np.floataa					).T for speech in raw_speech]
            elif not is_batched and not isinstance(A__	,    np.ndarray					):
                 __lowerCamelCase						:       Tuple				   =				np.asarray(A__	,    dtype=np.floataa					)
            elif isinstance(A__	,    np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
                 __lowerCamelCase						:       Any				   =				raw_speech.astype(np.floataa					)

            # always return batch
            if not is_batched:
                 __lowerCamelCase						:       Any				   =				[np.asarray([raw_speech]					).T]

            __lowerCamelCase						:       List[Any]				   =				BatchFeature({"""input_features""": raw_speech}					)

            # convert into correct format for padding

            __lowerCamelCase						:       int				   =				self.pad(
                A__	,    padding=A__	,    max_length=max_length if max_length else self.n_samples	,    truncation=A__	,    pad_to_multiple_of=A__	,    return_attention_mask=return_attention_mask or do_normalize	,    )

            # zero-mean and unit-variance normalization
            if do_normalize:
                 __lowerCamelCase						:       Any				   =				self.zero_mean_unit_var_norm(
                     padded_inputs["""input_features"""]	,    attention_mask=padded_inputs["""attention_mask"""]	,    padding_value=self.padding_value	,    )
                 __lowerCamelCase						:       int				   =				np.stack(padded_inputs["""input_features"""]	,    axis=0					)

            # make sure list is in array format
            __lowerCamelCase						:       Tuple				   =				padded_inputs.get("""input_features"""					).transpose(2	,    0	,    1					)

            __lowerCamelCase						:       Optional[Any]				   =				[self._np_extract_fbank_features(A__					) for waveform in input_features[0]]

            if isinstance(input_features[0]	,    A__					):
                 __lowerCamelCase						:       str				   =				[np.asarray(A__	,    dtype=np.floataa					) for feature in input_features]
            else:
                 __lowerCamelCase						:       Optional[Any]				   =				input_features

            if return_attention_mask:
                 # rescale from sample (48000) to feature (3000)
                 __lowerCamelCase						:       Tuple				   =				padded_inputs["""attention_mask"""][:, :: self.hop_length]

            if return_tensors is not None:
                 __lowerCamelCase						:       List[Any]				   =				padded_inputs.convert_to_tensors(A__					)

            return padded_inputs




       def 					a_					(					self      : int					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				copy.deepcopy(self.__dict__					)
            __lowerCamelCase						:       int				   =				self.__class__.__name__
            if "mel_filters" in output:
                 del output["mel_filters"]
            return output











'''simple docstring'''

def     __lowercase  (_lowercase     )   ->     list:


     """simple docstring"""

     # bit count represents no. of bits in the gray code
     if bit_count < 0:
          raise ValueError("""The given input must be positive"""     )

     # get the generated string sequence
     __lowerCamelCase						:       Optional[Any]				   =				gray_code_sequence_string(_lowercase     )
     #
     # convert them to integers
     for i in range(len(_lowercase     )     ):
          __lowerCamelCase						:       List[str]				   =				int(sequence[i],						2     )

     return sequence
def     __lowercase  (_lowercase     )   ->     list:


     """simple docstring"""

     # The approach is a recursive one
     # Base case achieved when either n = 0 or n=1
     if bit_count == 0:
          return ["0"]

     if bit_count == 1:
          return ["0", "1"]

     __lowerCamelCase						:       str				   =				1 << bit_count  # defines the length of the sequence
     # 1<< n is equivalent to 2^n

     # recursive answer will generate answer for n-1 bits
     __lowerCamelCase						:       Union[str, Any]				   =				gray_code_sequence_string(bit_count - 1     )

     __lowerCamelCase						:       Optional[int]				   =				[]

     # append 0 to first half of the smaller sequence generated
     for i in range(seq_len // 2     ):
          __lowerCamelCase						:       Union[str, Any]				   =				"""0""" + smaller_sequence[i]
          sequence.append(_lowercase     )

     # append 1 to second half ... start from the end of the list
     for i in reversed(range(seq_len // 2     )     ):
          __lowerCamelCase						:       Optional[Any]				   =				"""1""" + smaller_sequence[i]
          sequence.append(_lowercase     )

     return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()



