
'''simple docstring'''
def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       None:





  """simple docstring"""

  __A		:     Tuple												=		generate_pascal_triangle(__SCREAMING_SNAKE_CASE )
  for row_idx in range(__SCREAMING_SNAKE_CASE ):
    # 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_     (						__SCREAMING_SNAKE_CASE						:  int )				->       list[list[int]]:





  """simple docstring"""

  if not isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ):
    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		:     list[list[int]]												=		[]
  for current_row_idx in range(__SCREAMING_SNAKE_CASE ):
    __A		:     Tuple												=		populate_current_row(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
    triangle.append(__SCREAMING_SNAKE_CASE )
  return triangle







def        A_     (						__SCREAMING_SNAKE_CASE						:  list[list[int]]			,     __SCREAMING_SNAKE_CASE						:  int )				->       list[int]:





  """simple docstring"""

  __A		:     Union[str, Any]												=		[-1] * (current_row_idx + 1)
  # first and last elements of current row are equal to 1
  __A							,					__A		:     Tuple												=		1, 1
  for current_col_idx in range(1			,     __SCREAMING_SNAKE_CASE ):
    calculate_current_element(
        __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
  return current_row







def        A_     (						__SCREAMING_SNAKE_CASE						:  list[list[int]]			,     __SCREAMING_SNAKE_CASE						:  list[int]			,     __SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  int			,     )				->       None:





  """simple docstring"""

  __A		:     str												=		triangle[current_row_idx - 1][current_col_idx - 1]
  __A		:     Union[str, Any]												=		triangle[current_row_idx - 1][current_col_idx]
  __A		:     Any												=		above_to_left_elt + above_to_right_elt







def        A_     (						__SCREAMING_SNAKE_CASE						:  int )				->       list[list[int]]:





  """simple docstring"""

  if not isinstance(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE ):
    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		:     list[list[int]]												=		[[1]]

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

  return result







def        A_     (						)				->       None:





  """simple docstring"""

  from collections.abc import Callable
  from timeit import timeit

  def benchmark_a_function(__SCREAMING_SNAKE_CASE						:  Callable			,     __SCREAMING_SNAKE_CASE						:  int ) -> None:
    __A		:     List[Any]												=		F"{func.__name__}({value})"
    __A		:     Dict												=		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(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
    print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()





'''simple docstring'''
from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


A__   :		Dict 		=logging.get_logger(__name__)







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       =['''input_values''', '''padding_mask''']
 def __init__(   self			:    Optional[int]				,   lowerCamelCase			:    int = 1				,   lowerCamelCase			:    int = 2_40_00				,   lowerCamelCase			:    float = 0.0				,   lowerCamelCase			:    float = None				,   lowerCamelCase			:    float = None				,   **lowerCamelCase			:    Dict				,   ):



   """simple docstring"""





   super().__init__(feature_size=lowerCamelCase				,   sampling_rate=lowerCamelCase				,   padding_value=lowerCamelCase				,   **lowerCamelCase       )
   __A		:     str												=		chunk_length_s
   __A		:     str												=		overlap
 @property
 def 		lowercase_(   self			:    List[str]       ):



   """simple docstring"""





   if self.chunk_length_s is None:
     return None
   else:
     return int(self.chunk_length_s * self.sampling_rate       )
 @property
 def 		lowercase_(   self			:    List[str]       ):



   """simple docstring"""





   if self.chunk_length_s is None or self.overlap is None:
     return None
   else:
     return max(1				,   int((1.0 - self.overlap) * self.chunk_length       )       )






 def __call__(   self			:    List[str]				,   lowerCamelCase			:    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,   lowerCamelCase			:    Optional[Union[bool, str, PaddingStrategy]] = None				,   lowerCamelCase			:    Optional[bool] = False				,   lowerCamelCase			:    Optional[int] = None				,   lowerCamelCase			:    Optional[Union[str, TensorType]] = None				,   lowerCamelCase			:    Optional[int] = None				,   ):



   """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} 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 padding and truncation:
     raise ValueError("""Both padding and truncation were set. Make sure you only set one."""       )
   elif padding is None:
     # by default let's pad the inputs
     __A		:     List[Any]												=		True

   __A		:     List[str]												=		bool(
       isinstance(lowerCamelCase				,   (list, tuple)       ) and (isinstance(raw_audio[0]				,   (np.ndarray, tuple, list)       ))       )

   if is_batched:
     __A		:     Optional[Any]												=		[np.asarray(lowerCamelCase				,   dtype=np.floataa       ).T for audio in raw_audio]
   elif not is_batched and not isinstance(lowerCamelCase				,   np.ndarray       ):
     __A		:     Union[str, Any]												=		np.asarray(lowerCamelCase				,   dtype=np.floataa       )
   elif isinstance(lowerCamelCase				,   np.ndarray       ) and raw_audio.dtype is np.dtype(np.floataa       ):
     __A		:     Optional[Any]												=		raw_audio.astype(np.floataa       )

   # always return batch
   if not is_batched:
     __A		:     Tuple												=		[np.asarray(lowerCamelCase       ).T]

   # verify inputs are valid
   for idx, example in enumerate(lowerCamelCase       ):
     if example.ndim > 2:
       raise ValueError(f"Expected input shape (channels, length) but got shape {example.shape}"       )
     if self.feature_size == 1 and example.ndim != 1:
       raise ValueError(f"Expected mono audio but example has {example.shape[-1]} channels"       )
     if self.feature_size == 2 and example.shape[-1] != 2:
       raise ValueError(f"Expected stereo audio but example has {example.shape[-1]} channels"       )

   __A		:     Optional[Any]												=		None
   __A		:     Dict												=		BatchFeature({"""input_values""": raw_audio}       )
   if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
     if truncation:
       __A		:     Optional[Any]												=		min(array.shape[0] for array in raw_audio       )
       __A		:     Dict												=		int(np.floor(max_length / self.chunk_stride       )       )
       __A		:     Union[str, Any]												=		(nb_step - 1) * self.chunk_stride + self.chunk_length
     elif padding:
       __A		:     int												=		max(array.shape[0] for array in raw_audio       )
       __A		:     Optional[Any]												=		int(np.ceil(max_length / self.chunk_stride       )       )
       __A		:     Any												=		(nb_step - 1) * self.chunk_stride + self.chunk_length
       __A		:     Optional[int]												=		"""max_length"""
     else:
       __A		:     Optional[Any]												=		input_values

        # normal padding on batch
   if padded_inputs is None:
     __A		:     Tuple												=		self.pad(
         lowerCamelCase				,   max_length=lowerCamelCase				,   truncation=lowerCamelCase				,   padding=lowerCamelCase				,   return_attention_mask=lowerCamelCase				,   )
     if padding:
       __A		:     List[Any]												=		padded_inputs.pop("""attention_mask"""       )

   __A		:     List[Any]												=		[]
   for example in padded_inputs.pop("""input_values"""       ):
     if self.feature_size == 1:
       __A		:     Optional[Any]												=		example[..., None]
     input_values.append(example.T       )

   __A		:     str												=		input_values
   if return_tensors is not None:
     __A		:     Any												=		padded_inputs.convert_to_tensors(lowerCamelCase       )

   return padded_inputs



