


'''simple docstring'''






def 		_A			(lowerCAmelCase__					:int						)				->					None:




     '''simple docstring'''



     _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 		_A			(lowerCAmelCase__					:int						)				->					list[list[int]]:




     '''simple docstring'''



     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 		_A			(lowerCAmelCase__					:list[list[int]]			,  lowerCAmelCase__					:int						)				->					list[int]:




     '''simple docstring'''



     _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 		_A			(lowerCAmelCase__					:list[list[int]]			,  lowerCAmelCase__					:list[int]			,  lowerCAmelCase__					:int			,  lowerCAmelCase__					:int			,  )				->					None:




     '''simple docstring'''



     _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 		_A			(lowerCAmelCase__					:int						)				->					list[list[int]]:




     '''simple docstring'''



     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 		_A			()				->					None:




     '''simple docstring'''



     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(15						):  # (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()





'''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


a_   :      Tuple				    =      logging.get_logger(__name__)




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		["""input_features"""]
 def __init__(	self ,			__magic_name__=80 ,			__magic_name__=1_60_00 ,			__magic_name__=1_60 ,			__magic_name__=30 ,			__magic_name__=4_00 ,			__magic_name__=0.0 ,			__magic_name__=False ,			**__magic_name__ ,			)   ->							Optional[int]:
      super().__init__(
          feature_size=__magic_name__ ,			sampling_rate=__magic_name__ ,			padding_value=__magic_name__ ,			return_attention_mask=__magic_name__ ,			**__magic_name__ ,			)
      _a      =							n_fft
      _a      =							hop_length
      _a      =							chunk_length
      _a      =							chunk_length * sampling_rate
      _a      =							self.n_samples // hop_length
      _a      =							sampling_rate
      _a      =							mel_filter_bank(
          num_frequency_bins=1 + n_fft // 2 ,			num_mel_filters=__magic_name__ ,			min_frequency=0.0 ,			max_frequency=8_0_0_0.0 ,			sampling_rate=__magic_name__ ,			norm='slaney' ,			mel_scale='slaney' ,			)
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							np.ndarray:
      _a      =							spectrogram(
          __magic_name__ ,			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' ,			)
      _a      =							log_spec[:, :-1]
      _a      =							np.maximum(__magic_name__ ,			log_spec.max() - 8.0  )
      _a      =							(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 							__UpperCAmelCase						(	__magic_name__ ,			__magic_name__ ,			__magic_name__ = 0.0  )   ->							List[np.ndarray]:
      if attention_mask is not None:
           _a      =							np.array(__magic_name__ ,			np.intaa  )
           _a      =							[]

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

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

      return normed_input_values
 def __call__(	self ,			__magic_name__ ,			__magic_name__ = True ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = "max_length" ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			**__magic_name__ ,			)   ->							BatchFeature:

      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.'  )

      _a      =							isinstance(__magic_name__ ,			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(__magic_name__ ,			(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(__magic_name__ ,			np.ndarray  ):
           _a      =							np.asarray(__magic_name__ ,			dtype=np.floataa  )
      elif isinstance(__magic_name__ ,			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]

      _a      =							BatchFeature({'input_features': raw_speech}  )

      # convert into correct format for padding

      _a      =							self.pad(
          __magic_name__ ,			padding=__magic_name__ ,			max_length=max_length if max_length else self.n_samples ,			truncation=__magic_name__ ,			pad_to_multiple_of=__magic_name__ ,			return_attention_mask=return_attention_mask or do_normalize ,			)

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

      # make sure list is in array format
      _a      =							padded_inputs.get('input_features'  ).transpose(2 ,			0 ,			1  )

      _a      =							[self._np_extract_fbank_features(__magic_name__  ) for waveform in input_features[0]]

      if isinstance(input_features[0] ,			__magic_name__  ):
           _a      =							[np.asarray(__magic_name__ ,			dtype=np.floataa  ) for feature in input_features]
      else:
           _a      =							input_features

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

      if return_tensors is not None:
           _a      =							padded_inputs.convert_to_tensors(__magic_name__  )

      return padded_inputs




 def 							__UpperCAmelCase						(	self  )   ->							Dict[str, Any]:
      _a      =							copy.deepcopy(self.__dict__  )
      _a      =							self.__class__.__name__
      if "mel_filters" in output:
           del output["mel_filters"]
      return output

