




"""simple docstring"""


def 					UpperCAmelCase__       (snake_case__							:					Optional[int]		,   snake_case__							:					Dict    ):
  """simple docstring"""

  if not len(__A    ) == len(__A    ) == 3:
    raise ValueError("""Please enter a valid equation."""    )
  if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
    raise ValueError("""Both a & b of two equations can't be zero."""    )

  # Extract the coefficients
  _snake_case		,					_snake_case		,					_snake_case							:		Dict								=  equationa
  _snake_case		,					_snake_case		,					_snake_case							:		Dict								=  equationa

  # Calculate the determinants of the matrices
  _snake_case							:		List[Any]								=  aa * ba - aa * ba
  _snake_case							:		List[str]								=  ca * ba - ca * ba
  _snake_case							:		int								=  aa * ca - aa * ca

  # Check if the system of linear equations has a solution (using Cramer's rule)
  if determinant == 0:
    if determinant_x == determinant_y == 0:
      raise ValueError("""Infinite solutions. (Consistent system)"""    )
    else:
      raise ValueError("""No solution. (Inconsistent system)"""    )
  else:
    if determinant_x == determinant_y == 0:
      # Trivial solution (Inconsistent system)
      return (0.0, 0.0)
    else:
      _snake_case							:		Dict								=  determinant_x / determinant
      _snake_case							:		Any								=  determinant_y / determinant
      # Non-Trivial Solution (Consistent system)
      return (x, y)



import warnings
from typing import Any, Dict, 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 ...utils import PaddingStrategy, TensorType, logging


UpperCamelCase__											=					logging.get_logger(__name__)





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : int      =			['input_values', 'attention_mask']


   def __init__(self     :						Any       ,  __UpperCAmelCase     :						int = 1       ,  __UpperCAmelCase     :						int = 1_6_0_0_0       ,  __UpperCAmelCase     :						float = 0.0       ,  __UpperCAmelCase     :						bool = False       ,  __UpperCAmelCase     :						int = 8_0       ,  __UpperCAmelCase     :						int = 1_6       ,  __UpperCAmelCase     :						int = 6_4       ,  __UpperCAmelCase     :						str = "hann_window"       ,  __UpperCAmelCase     :						float = 1.0       ,  __UpperCAmelCase     :						float = 8_0       ,  __UpperCAmelCase     :						float = 7_6_0_0       ,  __UpperCAmelCase     :						float = 1E-10       ,  __UpperCAmelCase     :						int = 2       ,  __UpperCAmelCase     :						bool = True       ,  **__UpperCAmelCase     :						Any       ,  )						->				str:
         """simple docstring"""



         super().__init__(feature_size=__UpperCAmelCase       ,  sampling_rate=__UpperCAmelCase       ,  padding_value=__UpperCAmelCase       ,  **__UpperCAmelCase		)
         UpperCAmelCase__								=  do_normalize
         UpperCAmelCase__								=  return_attention_mask

         UpperCAmelCase__								=  num_mel_bins
         UpperCAmelCase__								=  hop_length
         UpperCAmelCase__								=  win_length
         UpperCAmelCase__								=  win_function
         UpperCAmelCase__								=  frame_signal_scale
         UpperCAmelCase__								=  fmin
         UpperCAmelCase__								=  fmax
         UpperCAmelCase__								=  mel_floor
         UpperCAmelCase__								=  reduction_factor

         UpperCAmelCase__								=  win_length * sampling_rate // 1_0_0_0
         UpperCAmelCase__								=  hop_length * sampling_rate // 1_0_0_0
         UpperCAmelCase__								=  optimal_fft_length(self.sample_size		)
         UpperCAmelCase__								=  (self.n_fft // 2) + 1

         UpperCAmelCase__								=  window_function(window_length=self.sample_size       ,  name=self.win_function       ,  periodic=__UpperCAmelCase		)

         UpperCAmelCase__								=  mel_filter_bank(
             num_frequency_bins=self.n_freqs       ,  num_mel_filters=self.num_mel_bins       ,  min_frequency=self.fmin       ,  max_frequency=self.fmax       ,  sampling_rate=self.sampling_rate       ,  norm="slaney"       ,  mel_scale="slaney"       ,  )

         if frame_signal_scale != 1.0:
               warnings.warn(
                   "The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"       ,  __UpperCAmelCase       ,  )
         if reduction_factor != 2.0:
               warnings.warn(
                   "The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"       ,  __UpperCAmelCase       ,  )


   @staticmethod
   # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
   def 			lowercase_      (__UpperCAmelCase     :						List[np.ndarray]       ,  __UpperCAmelCase     :						List[np.ndarray]       ,  __UpperCAmelCase     :						float = 0.0		)						->				List[np.ndarray]:
         """simple docstring"""



         if attention_mask is not None:
               UpperCAmelCase__								=  np.array(__UpperCAmelCase       ,  np.intaa		)
               UpperCAmelCase__								=  []

               for vector, length in zip(__UpperCAmelCase       ,  attention_mask.sum(-1		)		):
                     UpperCAmelCase__								=  (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7		)
                     if length < normed_slice.shape[0]:
                           UpperCAmelCase__								=  padding_value

                     normed_input_values.append(__UpperCAmelCase		)
         else:
               UpperCAmelCase__								=  [(x - x.mean()) / np.sqrt(x.var() + 1E-7		) for x in input_values]

         return normed_input_values


   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						np.ndarray       ,  )						->				np.ndarray:
         """simple docstring"""



         UpperCAmelCase__								=  spectrogram(
             __UpperCAmelCase       ,  window=self.window       ,  frame_length=self.sample_size       ,  hop_length=self.sample_stride       ,  fft_length=self.n_fft       ,  mel_filters=self.mel_filters       ,  mel_floor=self.mel_floor       ,  log_mel="log10"       ,  )
         return log_mel_spec.T


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



         if audio is None and audio_target is None:
               raise ValueError("You must provide either `audio` or `audio_target` values."		)

         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 audio 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."		)

         if audio is not None:
               UpperCAmelCase__								=  self._process_audio(
                   __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  **__UpperCAmelCase       ,  )
         else:
               UpperCAmelCase__								=  None

         if audio_target is not None:
               UpperCAmelCase__								=  self._process_audio(
                   __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  **__UpperCAmelCase       ,  )

               if inputs is None:
                     return inputs_target
               else:
                     UpperCAmelCase__								=  inputs_target["input_values"]
                     UpperCAmelCase__								=  inputs_target.get("attention_mask"		)
                     if decoder_attention_mask is not None:
                           UpperCAmelCase__								=  decoder_attention_mask

         return inputs


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



         UpperCAmelCase__								=  isinstance(__UpperCAmelCase       ,  np.ndarray		) and len(speech.shape		) > 1
         if is_batched_numpy and len(speech.shape		) > 2:
               raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""		)
         UpperCAmelCase__								=  is_batched_numpy or (
             isinstance(__UpperCAmelCase       ,  (list, tuple)		) and (isinstance(speech[0]       ,  (np.ndarray, tuple, list)		))
         )

         if is_batched:
               UpperCAmelCase__								=  [np.asarray(__UpperCAmelCase       ,  dtype=np.floataa		) for speech in speech]
         elif not is_batched and not isinstance(__UpperCAmelCase       ,  np.ndarray		):
               UpperCAmelCase__								=  np.asarray(__UpperCAmelCase       ,  dtype=np.floataa		)
         elif isinstance(__UpperCAmelCase       ,  np.ndarray		) and speech.dtype is np.dtype(np.floataa		):
               UpperCAmelCase__								=  speech.astype(np.floataa		)

         # always return batch
         if not is_batched:
               UpperCAmelCase__								=  [speech]

         # needed to make pad() work on spectrogram inputs
         UpperCAmelCase__								=  self.feature_size

         # convert into correct format for padding
         if is_target:
               UpperCAmelCase__								=  [self._extract_mel_features(__UpperCAmelCase		) for waveform in speech]
               UpperCAmelCase__								=  BatchFeature({"input_values": features}		)
               UpperCAmelCase__								=  self.num_mel_bins
         else:
               UpperCAmelCase__								=  BatchFeature({"input_values": speech}		)

         UpperCAmelCase__								=  self.pad(
             __UpperCAmelCase       ,  padding=__UpperCAmelCase       ,  max_length=__UpperCAmelCase       ,  truncation=__UpperCAmelCase       ,  pad_to_multiple_of=__UpperCAmelCase       ,  return_attention_mask=__UpperCAmelCase       ,  **__UpperCAmelCase       ,  )

         UpperCAmelCase__								=  feature_size_hack

         # convert input values to correct format
         UpperCAmelCase__								=  padded_inputs["input_values"]
         if not isinstance(input_values[0]       ,  np.ndarray		):
               UpperCAmelCase__								=  [np.asarray(__UpperCAmelCase       ,  dtype=np.floataa		) for array in input_values]
         elif (
             not isinstance(__UpperCAmelCase       ,  np.ndarray		)
             and isinstance(input_values[0]       ,  np.ndarray		)
             and input_values[0].dtype is np.dtype(np.floataa		)
         ):
               UpperCAmelCase__								=  [array.astype(np.floataa		) for array in input_values]
         elif isinstance(__UpperCAmelCase       ,  np.ndarray		) and input_values.dtype is np.dtype(np.floataa		):
               UpperCAmelCase__								=  input_values.astype(np.floataa		)

         # convert attention_mask to correct format
         UpperCAmelCase__								=  padded_inputs.get("attention_mask"		)
         if attention_mask is not None:
               UpperCAmelCase__								=  [np.asarray(__UpperCAmelCase       ,  dtype=np.intaa		) for array in attention_mask]

         # zero-mean and unit-variance normalization
         if not is_target and self.do_normalize:
               UpperCAmelCase__								=  (
                   attention_mask
                   if self._get_padding_strategies(__UpperCAmelCase       ,  max_length=__UpperCAmelCase		) is not PaddingStrategy.DO_NOT_PAD
                   else None
               )
               UpperCAmelCase__								=  self.zero_mean_unit_var_norm(
                   padded_inputs["input_values"]       ,  attention_mask=__UpperCAmelCase       ,  padding_value=self.padding_value		)

         if return_tensors is not None:
               UpperCAmelCase__								=  padded_inputs.convert_to_tensors(__UpperCAmelCase		)

         return padded_inputs





   def 			lowercase_      (self     :						Tuple		)						->				Dict[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  super().to_dict()

         # Don't serialize these as they are derived from the other properties.
         UpperCAmelCase__								=  ["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
         for name in names:
               if name in output:
                     del output[name]

         return output



