


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


snake_case							: int      =   logging.get_logger(__name__)







class  snake_case_     (lowercase_   ):
     UpperCAmelCase__      :					Optional[int]					   =    ['''input_features''', '''attention_mask''']
     def __init__(   self     :Optional[int]    ,__snake_case     :Tuple=80    ,__snake_case     :Tuple=1_60_00    ,__snake_case     :List[Any]=0.0    ,__snake_case     :Union[str, Any]=10    ,__snake_case     :List[str]=25    ,__snake_case     :Dict="hamming_window"    ,__snake_case     :Any=3_27_68.0    ,__snake_case     :Union[str, Any]=0.97    ,__snake_case     :int=1.0    ,__snake_case     :List[str]=True    ,__snake_case     :List[Any]=True    ,__snake_case     :Optional[int]=False    ,**__snake_case     :Union[str, Any]    ,)      ->					Optional[int]:
            super().__init__(feature_size=__snake_case    ,sampling_rate=__snake_case    ,padding_value=__snake_case    ,**__snake_case   )

            a__    =  feature_size
            a__    =  sampling_rate
            a__    =  padding_value
            a__    =  hop_length
            a__    =  win_length
            a__    =  frame_signal_scale
            a__    =  preemphasis_coeff
            a__    =  mel_floor
            a__    =  normalize_means
            a__    =  normalize_vars
            a__    =  win_function
            a__    =  return_attention_mask

            a__    =  win_length * sampling_rate // 10_00
            a__    =  hop_length * sampling_rate // 10_00

            a__    =  optimal_fft_length(self.sample_size   )
            a__    =  (self.n_fft // 2) + 1
     def 				lowerCamelCase__(   self     :List[Any]    ,__snake_case     :Tuple   )      ->					Dict:
            if self.win_function == "hamming_window":
                   a__    =  window_function(window_length=self.sample_size    ,name=self.win_function    ,periodic=__snake_case   )
            else:
                   a__    =  window_function(window_length=self.sample_size    ,name=self.win_function   )

            a__    =  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    ,)

            a__    =  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     :int    ,__snake_case     :int    ,__snake_case     :Any    ,__snake_case     :Any   )      ->					str:
            # make sure we normalize float32 arrays
            if self.normalize_means:
                   a__    =  x[:input_length].mean(axis=0   )
                   a__    =  np.subtract(__snake_case    ,__snake_case   )
            if self.normalize_vars:
                   a__    =  x[:input_length].std(axis=0   )
                   a__    =  np.divide(__snake_case    ,__snake_case   )

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

            # make sure array is in float32
            a__    =  x.astype(np.floataa   )

            return x
     def 				lowerCamelCase__(   self     :Optional[Any]    ,__snake_case     :Tuple    ,__snake_case     :Optional[Any] = None   )      ->					int:
            a__    =  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     :List[Any]    ,__snake_case     :Dict    ,__snake_case     :Tuple = False    ,__snake_case     :Union[str, Any] = None    ,__snake_case     :Union[str, Any] = False    ,__snake_case     :List[str] = None    ,__snake_case     :Optional[int] = None    ,__snake_case     :List[str] = None    ,__snake_case     :Any = None    ,**__snake_case     :Tuple    ,)      ->					int:

            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.'   )

            a__    =  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}'   )
            a__    =  is_batched_numpy or (
                isinstance(__snake_case    ,(list, tuple)   ) and (isinstance(raw_speech[0]    ,(np.ndarray, tuple, list)   ))
            )

            if is_batched:
                   a__    =  [np.asarray(__snake_case    ,dtype=np.floataa   ) for speech in raw_speech]
            elif not is_batched and not isinstance(__snake_case    ,np.ndarray   ):
                   a__    =  np.asarray(__snake_case    ,dtype=np.floataa   )
            elif isinstance(__snake_case    ,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__    =  [raw_speech]

            # extract fbank features
            a__    =  [self._extract_mfsc_features(__snake_case   ) for one_waveform in raw_speech]

            # convert into correct format for padding
            a__    =  BatchFeature({'input_features': features}   )

            a__    =  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
            a__    =  padded_inputs.get('input_features'   )
            if isinstance(input_features[0]    ,__snake_case   ):
                   a__    =  [np.asarray(__snake_case    ,dtype=np.floataa   ) for feature in input_features]

            a__    =  padded_inputs.get('attention_mask'   )
            if attention_mask is not None:
                   a__    =  [np.asarray(__snake_case    ,dtype=np.intaa   ) for array in attention_mask]

            if self.normalize_means or self.normalize_vars:
                   a__    =  (
                       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
                   )
                   a__    =  self.normalize(
                       padded_inputs['input_features']    ,attention_mask=__snake_case   )

            if return_tensors is not None:
                   a__    =  padded_inputs.convert_to_tensors(__snake_case   )

            return padded_inputs








def 							__lowercase     (				__lowerCAmelCase							:       int     ):
							a__    =  generate_pascal_triangle(__lowerCAmelCase     )
							for row_idx in range(__lowerCAmelCase     ):
														# Print left spaces
														for _ in range(num_rows - row_idx - 1     ):
																					print(end=' '     )
														# Print row values
														for col_idx in range(row_idx + 1     ):
																					if col_idx != row_idx:
																												print(triangle[row_idx][col_idx]      ,					end=' '     )
																					else:
																												print(triangle[row_idx][col_idx]      ,					end=''     )
														print()






def 							__lowercase     (				__lowerCAmelCase							:       int     ):

							if not isinstance(__lowerCAmelCase      ,					__lowerCAmelCase     ):
														raise TypeError('The input value of \'num_rows\' should be \'int\''     )

							if num_rows == 0:
														return []
							elif num_rows < 0:
														raise ValueError(
														    'The input value of \'num_rows\' should be greater than or equal to 0'     )

							a__    =  []
							for current_row_idx in range(__lowerCAmelCase     ):
														a__    =  populate_current_row(__lowerCAmelCase      ,					__lowerCAmelCase     )
														triangle.append(__lowerCAmelCase     )
							return triangle






def 							__lowercase     (				__lowerCAmelCase							:       list[list[int]]      ,					__lowerCAmelCase							:       int     ):
							a__    =  [-1] * (current_row_idx + 1)
							# first and last elements of current row are equal to 1
							a__   ,      a__    =  1, 1
							for current_col_idx in range(1      ,					__lowerCAmelCase     ):
														calculate_current_element(
														    __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase     )
							return current_row






def 							__lowercase     (				__lowerCAmelCase							:       list[list[int]]      ,					__lowerCAmelCase							:       list[int]      ,					__lowerCAmelCase							:       int      ,					__lowerCAmelCase							:       int      ,					):
							a__    =  triangle[current_row_idx - 1][current_col_idx - 1]
							a__    =  triangle[current_row_idx - 1][current_col_idx]
							a__    =  above_to_left_elt + above_to_right_elt






def 							__lowercase     (				__lowerCAmelCase							:       int     ):

							if not isinstance(__lowerCAmelCase      ,					__lowerCAmelCase     ):
														raise TypeError('The input value of \'num_rows\' should be \'int\''     )

							if num_rows == 0:
														return []
							elif num_rows < 0:
														raise ValueError(
														    'The input value of \'num_rows\' should be greater than or equal to 0'     )

							a__    =  [[1]]

							for row_index in range(1      ,					__lowerCAmelCase     ):
														a__    =  [0] + result[-1] + [0]
														a__    =  row_index + 1
														# Calculate the number of distinct elements in a row
														a__    =  sum(divmod(__lowerCAmelCase      ,					2     )     )
														a__    =  [
														    temp_row[i - 1] + temp_row[i] for i in range(1      ,					distinct_elements + 1     )
														]
														a__    =  row_first_half[: (row_index + 1) // 2]
														row_second_half.reverse()
														a__    =  row_first_half + row_second_half
														result.append(__lowerCAmelCase     )

							return result






def 							__lowercase     (				):
							from collections.abc import Callable
							from timeit import timeit

							def benchmark_a_function(__lowerCAmelCase							:       Callable      ,					__lowerCAmelCase							:       int     ) -> None:
														a__    =  F'{func.__name__}({value})'
														a__    =  timeit(F'__main__.{call}'      ,					setup='import __main__'     )
														# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
														print(F'{call:38} -- {timing:.4f} seconds'     )

							for value in range(1_5     ):  # (1, 7, 14):
														for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
																					benchmark_a_function(__lowerCAmelCase      ,					__lowerCAmelCase     )
														print()


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						benchmark()




