



from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload



class 		_lowercase		:

   def __init__(      self			:       Union[str, Any]							,   __lowerCAmelCase			:       Collection[float] | None = None							)       ->				None:


    """simple docstring"""






    if components is None:
     a						=		[]
    a						=		list(__lowerCAmelCase							)

   def __len__(      self			:       str							)       ->				int:


    """simple docstring"""






    return len(self.__components							)

   def __str__(      self			:       str							)       ->				str:


    """simple docstring"""






    return "(" + ",".join(map(__lowerCAmelCase							,   self.__components							)							) + ")"

   def __add__(      self			:       Any							,   __lowerCAmelCase			:       Vector							)       ->				Vector:


    """simple docstring"""






    a						=		len(self							)
    if size == len(__lowerCAmelCase							):
     a						=		[self.__components[i] + other.component(__lowerCAmelCase							) for i in range(__lowerCAmelCase							)]
     return Vector(__lowerCAmelCase							)
    else:
     raise Exception("must have the same size"							)

   def __sub__(      self			:       Dict							,   __lowerCAmelCase			:       Vector							)       ->				Vector:


    """simple docstring"""






    a						=		len(self							)
    if size == len(__lowerCAmelCase							):
     a						=		[self.__components[i] - other.component(__lowerCAmelCase							) for i in range(__lowerCAmelCase							)]
     return Vector(__lowerCAmelCase							)
    else:  # error case
     raise Exception("must have the same size"							)

   @overload
   def __mul__(      self			:       List[str]							,   __lowerCAmelCase			:       float							)       ->				Vector:


    """simple docstring"""






    ...

   @overload
   def __mul__(      self			:       Union[str, Any]							,   __lowerCAmelCase			:       Vector							)       ->				float:


    """simple docstring"""






    ...

   def __mul__(      self			:       Dict							,   __lowerCAmelCase			:       float | Vector							)       ->				float | Vector:


    """simple docstring"""






    if isinstance(__lowerCAmelCase							,   (float, int)							):
     a						=		[c * other for c in self.__components]
     return Vector(__lowerCAmelCase							)
    elif isinstance(__lowerCAmelCase							,   __lowerCAmelCase							) and len(self							) == len(__lowerCAmelCase							):
     a						=		len(self							)
     a						=		[self.__components[i] * other.component(__lowerCAmelCase							) for i in range(__lowerCAmelCase							)]
     return sum(__lowerCAmelCase							)
    else:  # error case
     raise Exception("invalid operand!"							)

   def 		A							(      self			:       Optional[int]							)       ->				Vector:


    """simple docstring"""






    return Vector(self.__components							)

   def 		A							(      self			:       Any							,   __lowerCAmelCase			:       int							)       ->				float:


    """simple docstring"""






    if isinstance(__lowerCAmelCase							,   __lowerCAmelCase							) and -len(self.__components							) <= i < len(self.__components							):
     return self.__components[i]
    else:
     raise Exception("index out of range"							)

   def 		A							(      self			:       Optional[Any]							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       float							)       ->				None:


    """simple docstring"""






    assert -len(self.__components							) <= pos < len(self.__components							)
    a						=		value

   def 		A							(      self			:       Any							)       ->				float:


    """simple docstring"""






    if len(self.__components							) == 0:
     raise Exception("Vector is empty"							)
    a						=		[c**2 for c in self.__components]
    return math.sqrt(sum(__lowerCAmelCase							)							)




   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       Vector							,   __lowerCAmelCase			:       bool = False							)       ->				float:


    """simple docstring"""






    a						=		self * other
    a						=		self.euclidean_length() * other.euclidean_length()
    if deg:
     return math.degrees(math.acos(num / den							)							)
    else:
     return math.acos(num / den							)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :int					):






 '''simple docstring'''

 assert isinstance(UpperCAmelCase__ ,					UpperCAmelCase__					)
 return Vector([0] * dimension					)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :int ,					UpperCAmelCase__ :int					):






 '''simple docstring'''

 assert isinstance(UpperCAmelCase__ ,					UpperCAmelCase__					) and (isinstance(UpperCAmelCase__ ,					UpperCAmelCase__					))
 a						=		[0] * dimension
 a						=		1
 return Vector(UpperCAmelCase__					)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :float ,					UpperCAmelCase__ :Vector ,					UpperCAmelCase__ :Vector					):






 '''simple docstring'''

 assert (
     isinstance(UpperCAmelCase__ ,					UpperCAmelCase__					)
     and isinstance(UpperCAmelCase__ ,					UpperCAmelCase__					)
     and (isinstance(UpperCAmelCase__ ,					(int, float)					))
 )
 return x * scalar + y


def 	UpperCAmelCase__      (      UpperCAmelCase__ :int ,					UpperCAmelCase__ :int ,					UpperCAmelCase__ :int					):






 '''simple docstring'''

 random.seed(UpperCAmelCase__					)
 a						=		[random.randint(UpperCAmelCase__ ,					UpperCAmelCase__					) for _ in range(UpperCAmelCase__					)]
 return Vector(UpperCAmelCase__					)



class 		_lowercase		:

   def __init__(      self			:       Optional[Any]							,   __lowerCAmelCase			:       list[list[float]]							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int							)       ->				None:


    """simple docstring"""






    a						=		matrix
    a						=		w
    a						=		h

   def __str__(      self			:       List[Any]							)       ->				str:


    """simple docstring"""






    a						=		""
    for i in range(self.__height							):
     ans += "|"
     for j in range(self.__width							):
      if j < self.__width - 1:
       ans += str(self.__matrix[i][j]							) + ","
      else:
       ans += str(self.__matrix[i][j]							) + "|\n"
    return ans

   def __add__(      self			:       int							,   __lowerCAmelCase			:       Matrix							)       ->				Matrix:


    """simple docstring"""






    if self.__width == other.width() and self.__height == other.height():
     a						=		[]
     for i in range(self.__height							):
      a						=		[
          self.__matrix[i][j] + other.component(__lowerCAmelCase							,   __lowerCAmelCase							)
          for j in range(self.__width							)
      ]
      matrix.append(__lowerCAmelCase							)
     return Matrix(__lowerCAmelCase							,   self.__width							,   self.__height							)
    else:
     raise Exception("matrix must have the same dimension!"							)

   def __sub__(      self			:       List[str]							,   __lowerCAmelCase			:       Matrix							)       ->				Matrix:


    """simple docstring"""






    if self.__width == other.width() and self.__height == other.height():
     a						=		[]
     for i in range(self.__height							):
      a						=		[
          self.__matrix[i][j] - other.component(__lowerCAmelCase							,   __lowerCAmelCase							)
          for j in range(self.__width							)
      ]
      matrix.append(__lowerCAmelCase							)
     return Matrix(__lowerCAmelCase							,   self.__width							,   self.__height							)
    else:
     raise Exception("matrices must have the same dimension!"							)

   @overload
   def __mul__(      self			:       List[Any]							,   __lowerCAmelCase			:       float							)       ->				Matrix:


    """simple docstring"""






    ...

   @overload
   def __mul__(      self			:       Dict							,   __lowerCAmelCase			:       Vector							)       ->				Vector:


    """simple docstring"""






    ...

   def __mul__(      self			:       List[Any]							,   __lowerCAmelCase			:       float | Vector							)       ->				Vector | Matrix:


    """simple docstring"""






    if isinstance(__lowerCAmelCase							,   __lowerCAmelCase							):  # matrix-vector
     if len(__lowerCAmelCase							) == self.__width:
      a						=		zero_vector(self.__height							)
      for i in range(self.__height							):
       a						=		[
           self.__matrix[i][j] * other.component(__lowerCAmelCase							)
           for j in range(self.__width							)
       ]
       ans.change_component(__lowerCAmelCase							,   sum(__lowerCAmelCase							)							)
      return ans
     else:
      raise Exception(
          "vector must have the same size as the "
          "number of columns of the matrix!"							)
    elif isinstance(__lowerCAmelCase							,   (int, float)							):  # matrix-scalar
     a						=		[
         [self.__matrix[i][j] * other for j in range(self.__width							)]
         for i in range(self.__height							)
     ]
     return Matrix(__lowerCAmelCase							,   self.__width							,   self.__height							)
    return None

   def 		A							(      self			:       Union[str, Any]							)       ->				int:


    """simple docstring"""






    return self.__height

   def 		A							(      self			:       int							)       ->				int:


    """simple docstring"""






    return self.__width

   def 		A							(      self			:       Optional[Any]							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int							)       ->				float:


    """simple docstring"""






    if 0 <= x < self.__height and 0 <= y < self.__width:
     return self.__matrix[x][y]
    else:
     raise Exception("change_component: indices out of bounds"							)

   def 		A							(      self			:       Optional[int]							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       float							)       ->				None:


    """simple docstring"""






    if 0 <= x < self.__height and 0 <= y < self.__width:
     a						=		value
    else:
     raise Exception("change_component: indices out of bounds"							)

   def 		A							(      self			:       Union[str, Any]							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int							)       ->				float:


    """simple docstring"""






    if self.__height != self.__width:
     raise Exception("Matrix is not square"							)
    a						=		self.__matrix[:x] + self.__matrix[x + 1 :]
    for i in range(len(__lowerCAmelCase							)							):
     a						=		minor[i][:y] + minor[i][y + 1 :]
    return Matrix(__lowerCAmelCase							,   self.__width - 1							,   self.__height - 1							).determinant()

   def 		A							(      self			:       List[str]							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int							)       ->				float:


    """simple docstring"""






    if self.__height != self.__width:
     raise Exception("Matrix is not square"							)
    if 0 <= x < self.__height and 0 <= y < self.__width:
     return (-1) ** (x + y) * self.minor(__lowerCAmelCase							,   __lowerCAmelCase							)
    else:
     raise Exception("Indices out of bounds"							)




   def 		A							(      self			:       Any							)       ->				float:


    """simple docstring"""






    if self.__height != self.__width:
     raise Exception("Matrix is not square"							)
    if self.__height < 1:
     raise Exception("Matrix has no element"							)
    elif self.__height == 1:
     return self.__matrix[0][0]
    elif self.__height == 2:
     return (
         self.__matrix[0][0] * self.__matrix[1][1]
         - self.__matrix[0][1] * self.__matrix[1][0]
     )
    else:
     a						=		[
         self.__matrix[0][y] * self.cofactor(0							,   __lowerCAmelCase							) for y in range(self.__width							)
     ]
     return sum(__lowerCAmelCase							)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :int					):






 '''simple docstring'''

 a						=		[[0] * n for _ in range(UpperCAmelCase__					)]
 return Matrix(UpperCAmelCase__ ,					UpperCAmelCase__ ,					UpperCAmelCase__					)




def 	UpperCAmelCase__      (      UpperCAmelCase__ :int ,					UpperCAmelCase__ :int ,					UpperCAmelCase__ :int ,					UpperCAmelCase__ :int					):






 '''simple docstring'''

 random.seed(UpperCAmelCase__					)
 a						=		[
     [random.randint(UpperCAmelCase__ ,					UpperCAmelCase__					) for _ in range(UpperCAmelCase__					)] for _ in range(UpperCAmelCase__					)
 ]
 return Matrix(UpperCAmelCase__ ,					UpperCAmelCase__ ,					UpperCAmelCase__					)






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


A_		:     List[str]        =     logging.get_logger(__name__)



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			['''audio_values''', '''audio_mask''']

   def __init__(      self			:       List[Any]							,   __lowerCAmelCase			:       Dict=2048							,   __lowerCAmelCase			:       List[Any]=1							,   __lowerCAmelCase			:       Dict=[16, 16]							,   __lowerCAmelCase			:       str=128							,   __lowerCAmelCase			:       Optional[int]=4_4100							,   __lowerCAmelCase			:       int=86							,   __lowerCAmelCase			:       Optional[Any]=2048							,   __lowerCAmelCase			:       str=0.0							,   **__lowerCAmelCase			:       Optional[int]							,   )       ->				Union[str, Any]:


    """simple docstring"""






    super().__init__(
        feature_size=__lowerCAmelCase							,   sampling_rate=__lowerCAmelCase							,   padding_value=__lowerCAmelCase							,   **__lowerCAmelCase							,   )

    a						=		spectrogram_length
    a						=		num_channels
    a						=		patch_size
    a						=		feature_size // self.patch_size[1]
    a						=		n_fft
    a						=		sampling_rate // hop_length_to_sampling_rate
    a						=		sampling_rate
    a						=		padding_value
    a						=		mel_filter_bank(
        num_frequency_bins=1 + n_fft // 2							,   num_mel_filters=__lowerCAmelCase							,   min_frequency=0.0							,   max_frequency=2_2_0_5_0.0							,   sampling_rate=__lowerCAmelCase							,   norm="slaney"							,   mel_scale="slaney"							,   ).T

   def 		A							(      self			:       List[str]							,   __lowerCAmelCase			:       np.array							)       ->				np.ndarray:


    """simple docstring"""






    a						=		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=8_0.0							,   )
    a						=		log_spec[:, :-1]
    a						=		log_spec - 2_0.0
    a						=		np.clip(log_spec / 4_0.0							,   -2.0							,   0.0							) + 1.0
    return log_spec




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


    """simple docstring"""






    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						=		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}"""							)
    a						=		is_batched_numpy or (
        isinstance(__lowerCAmelCase							,   (list, tuple)							) and (isinstance(raw_speech[0]							,   (np.ndarray, tuple, list)							))
    )
    if is_batched:
     a						=		[np.asarray([speech]							,   dtype=np.floataa							).T for speech in raw_speech]
    elif not is_batched and not isinstance(__lowerCAmelCase							,   np.ndarray							):
     a						=		np.asarray(__lowerCAmelCase							,   dtype=np.floataa							)
    elif isinstance(__lowerCAmelCase							,   np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
     a						=		raw_speech.astype(np.floataa							)
    # always return batch
    if not is_batched:
     a						=		[np.asarray([raw_speech]							).T]

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

    # Create audio attention mask
    a						=		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						=		[
         (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						=		np.array(__lowerCAmelCase							).astype(np.floataa							)

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

    # return as BatchFeature
    if return_attention_mask:
     a						=		{"audio_values": padded_audio_features, "audio_mask": audio_mask}
    else:
     a						=		{"audio_values": padded_audio_features}

    a						=		BatchFeature(data=__lowerCAmelCase							,   tensor_type=__lowerCAmelCase							)
    return encoded_inputs

