


"""simple docstring"""





from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

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 			lowercase_	(			a_    ):
      __magic_name__  :				Union[str, Any] 							=					['input_features', 'attention_mask']







      def __init__(   self			:				List[str]		,	_lowercase			:				List[str]=8_0		,	_lowercase			:				Tuple=1_6_0_0_0		,	_lowercase			:				Optional[int]=8_0		,	_lowercase			:				Dict=0.0		,	_lowercase			:				List[Any]=True		,	_lowercase			:				Optional[int]=True		,	_lowercase			:				int=True		,	**_lowercase			:				Optional[int]		,	):
        super().__init__(feature_size=_lowercase		,	sampling_rate=_lowercase		,	padding_value=_lowercase		,	**_lowercase     )
        lowerCAmelCase__     :     Tuple										=       num_mel_bins
        lowerCAmelCase__     :     List[Any]										=       do_ceptral_normalize
        lowerCAmelCase__     :     Dict										=       normalize_means
        lowerCAmelCase__     :     str										=       normalize_vars
        lowerCAmelCase__     :     Tuple										=       True







      def 					_lowerCAmelCase							(   self			:				str		,	_lowercase			:				Union[str, Any]		,	):
        lowerCAmelCase__     :     Optional[Any]										=       waveform * (2**1_5)  # Kaldi compliance: 16-bit signed integers
        lowerCAmelCase__     :     List[Any]										=       torch.from_numpy(_lowercase     ).unsqueeze(0     )
        lowerCAmelCase__     :     str										=       ta_kaldi.fbank(_lowercase		,	num_mel_bins=self.num_mel_bins		,	sample_frequency=self.sampling_rate     )
        return features.numpy()







      @staticmethod
      def 					_lowerCAmelCase							(   _lowercase			:				str		,	_lowercase			:				Optional[int]		,	_lowercase			:				Union[str, Any] = True		,	_lowercase			:				List[Any] = True		,	_lowercase			:				Optional[Any] = 0.0		,	):
        # make sure we normalize float32 arrays
        if normalize_means:
          lowerCAmelCase__     :     Tuple										=       x[:input_length].mean(axis=0     )
          lowerCAmelCase__     :     str										=       np.subtract(_lowercase		,	_lowercase     )
        if normalize_vars:
          lowerCAmelCase__     :     Optional[Any]										=       x[:input_length].std(axis=0     )
          lowerCAmelCase__     :     List[str]										=       np.divide(_lowercase		,	_lowercase     )

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

        # make sure array is in float32
        lowerCAmelCase__     :     Tuple										=       x.astype(np.floataa     )

        return x







      def 					_lowerCAmelCase							(   self			:				Tuple		,	_lowercase			:				Optional[int]		,	_lowercase			:				Optional[int] = None     ):
        lowerCAmelCase__     :     List[str]										=       attention_mask.sum(-1     ) if attention_mask is not None else [x.shape[0] for x in input_features]
        return [
            self.utterance_cmvn(_lowercase		,	_lowercase		,	self.normalize_means		,	self.normalize_vars		,	self.padding_value     )
            for x, n in zip(_lowercase		,	_lowercase     )
        ]







      def __call__(   self			:				Optional[int]		,	_lowercase			:				Any		,	_lowercase			:				Dict = False		,	_lowercase			:				Optional[int] = None		,	_lowercase			:				str = False		,	_lowercase			:				List[Any] = None		,	_lowercase			:				Union[str, Any] = None		,	_lowercase			:				Tuple = None		,	_lowercase			:				Optional[Any] = None		,	**_lowercase			:				Optional[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."     )

        lowerCAmelCase__     :     List[str]										=       isinstance(_lowercase		,	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}"     )
        lowerCAmelCase__     :     Dict										=       is_batched_numpy or (
            isinstance(_lowercase		,	(list, tuple)     ) and (isinstance(raw_speech[0]		,	(np.ndarray, tuple, list)     ))
        )

        if is_batched:
          lowerCAmelCase__     :     int										=       [np.asarray(_lowercase		,	dtype=np.floataa     ) for speech in raw_speech]
        elif not is_batched and not isinstance(_lowercase		,	np.ndarray     ):
          lowerCAmelCase__     :     List[Any]										=       np.asarray(_lowercase		,	dtype=np.floataa     )
        elif isinstance(_lowercase		,	np.ndarray     ) and raw_speech.dtype is np.dtype(np.floataa     ):
          lowerCAmelCase__     :     Tuple										=       raw_speech.astype(np.floataa     )

        # always return batch
        if not is_batched:
          lowerCAmelCase__     :     Dict										=       [raw_speech]

        # extract fbank features
        lowerCAmelCase__     :     Optional[int]										=       [self._extract_fbank_features(_lowercase     ) for waveform in raw_speech]

        # convert into correct format for padding
        lowerCAmelCase__     :     Any										=       BatchFeature({"input_features": features}     )

        lowerCAmelCase__     :     Any										=       self.pad(
            _lowercase		,	padding=_lowercase		,	max_length=_lowercase		,	truncation=_lowercase		,	pad_to_multiple_of=_lowercase		,	return_attention_mask=_lowercase		,	**_lowercase		,	)

        # make sure list is in array format
        lowerCAmelCase__     :     int										=       padded_inputs.get("input_features"     )
        if isinstance(input_features[0]		,	_lowercase     ):
          lowerCAmelCase__     :     List[Any]										=       [np.asarray(_lowercase		,	dtype=np.floataa     ) for feature in input_features]

        lowerCAmelCase__     :     List[str]										=       padded_inputs.get("attention_mask"     )
        if attention_mask is not None:
          lowerCAmelCase__     :     List[Any]										=       [np.asarray(_lowercase		,	dtype=np.intaa     ) for array in attention_mask]

        # Utterance-level cepstral mean and variance normalization
        if self.do_ceptral_normalize:
          lowerCAmelCase__     :     List[str]										=       (
              np.array(_lowercase		,	dtype=np.intaa     )
              if self._get_padding_strategies(_lowercase		,	max_length=_lowercase     ) is not PaddingStrategy.DO_NOT_PAD
              else None
          )
          lowerCAmelCase__     :     Union[str, Any]										=       self.normalize(
              padded_inputs["input_features"]		,	attention_mask=_lowercase     )

        if return_tensors is not None:
          lowerCAmelCase__     :     Union[str, Any]										=       padded_inputs.convert_to_tensors(_lowercase     )

        return padded_inputs


"""simple docstring"""


def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  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__						):

			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'''						)

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






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__						):
			lowerCamelCase_					=  [-1] * (current_row_idx + 1)
			# first and last elements of current row are equal to 1
			lowerCamelCase_		, lowerCamelCase_					=  1, 1
			for current_col_idx in range(1	,lowerCAmelCase__						):
						calculate_current_element(
						    lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__						)
			return current_row






def  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__	,):
			lowerCamelCase_					=  triangle[current_row_idx - 1][current_col_idx - 1]
			lowerCamelCase_					=  triangle[current_row_idx - 1][current_col_idx]
			lowerCamelCase_					=  above_to_left_elt + above_to_right_elt






def  lowercase						(	lowerCAmelCase__						):

			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'''						)

			lowerCamelCase_					=  [[1]]

			for row_index in range(1	,lowerCAmelCase__						):
						lowerCamelCase_					=  [0] + result[-1] + [0]
						lowerCamelCase_					=  row_index + 1
						# Calculate the number of distinct elements in a row
						lowerCamelCase_					=  sum(divmod(lowerCAmelCase__	,2						)						)
						lowerCamelCase_					=  [
						    temp_row[i - 1] + temp_row[i] for i in range(1	,distinct_elements + 1						)
						]
						lowerCamelCase_					=  row_first_half[: (row_index + 1) // 2]
						row_second_half.reverse()
						lowerCamelCase_					=  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__	,lowerCAmelCase__						) -> None:
						lowerCamelCase_					=  f"{func.__name__}({value})"
						lowerCamelCase_					=  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()



