



"""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


lowercase__					:						Tuple												=			logging.get_logger(__name__)




class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Dict	      =	["""input_features"""]

 def __init__(  self	: Optional[int]						,  lowercase_	: Any=80						,  lowercase_	: List[Any]=16000						,  lowercase_	: Optional[int]=160						,  lowercase_	: Dict=30						,  lowercase_	: str=400						,  lowercase_	: List[str]=0.0						,  lowercase_	: List[Any]=False						,  **lowercase_	: Tuple						,  ):
     super().__init__(
         feature_size=lowercase_						,  sampling_rate=lowercase_						,  padding_value=lowercase_						,  return_attention_mask=lowercase_						,  **lowercase_						,  )
     snake_case_				:  List[Any]       =			n_fft
     snake_case_				:  str       =			hop_length
     snake_case_				:  Dict       =			chunk_length
     snake_case_				:  int       =			chunk_length * sampling_rate
     snake_case_				:  Optional[Any]       =			self.n_samples // hop_length
     snake_case_				:  List[str]       =			sampling_rate
     snake_case_				:  Optional[int]       =			mel_filter_bank(
         num_frequency_bins=1 + n_fft // 2						,  num_mel_filters=lowercase_						,  min_frequency=0.0						,  max_frequency=80_00.0						,  sampling_rate=lowercase_						,  norm='''slaney'''						,  mel_scale='''slaney'''						,  )

 def 		_snake_case      (  self	: Dict						,  lowercase_	: np.array       ):
     snake_case_				:  Optional[int]       =			spectrogram(
         lowercase_						,  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'''						,  )
     snake_case_				:  Union[str, Any]       =			log_spec[:, :-1]
     snake_case_				:  int       =			np.maximum(lowercase_						,  log_spec.max() - 8.0       )
     snake_case_				:  Any       =			(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 		_snake_case      (  lowercase_	: List[np.ndarray]						,  lowercase_	: List[np.ndarray]						,  lowercase_	: float = 0.0       ):
     if attention_mask is not None:
         snake_case_				:  Union[str, Any]       =			np.array(lowercase_						,  np.intaa       )
         snake_case_				:  int       =			[]

         for vector, length in zip(lowercase_						,  attention_mask.sum(-1       )       ):
             snake_case_				:  str       =			(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7       )
             if length < normed_slice.shape[0]:
                 snake_case_				:  Dict       =			padding_value

             normed_input_values.append(lowercase_       )
     else:
         snake_case_				:  str       =			[(x - x.mean()) / np.sqrt(x.var() + 1E-7       ) for x in input_values]

     return normed_input_values

 def __call__(  self	: Tuple						,  lowercase_	: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]						,  lowercase_	: bool = True						,  lowercase_	: Optional[int] = None						,  lowercase_	: Optional[Union[str, TensorType]] = None						,  lowercase_	: Optional[bool] = None						,  lowercase_	: Optional[str] = "max_length"						,  lowercase_	: Optional[int] = None						,  lowercase_	: Optional[int] = None						,  lowercase_	: Optional[bool] = None						,  **lowercase_	: List[str]						,  ):

     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.'''       )

     snake_case_				:  List[Any]       =			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}"       )
     snake_case_				:  Any       =			is_batched_numpy or (
         isinstance(lowercase_						,  (list, tuple)       ) and (isinstance(raw_speech[0]						,  (np.ndarray, tuple, list)       ))
     )

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

     # always return batch
     if not is_batched:
         snake_case_				:  int       =			[np.asarray([raw_speech]       ).T]

     snake_case_				:  Union[str, Any]       =			BatchFeature({'''input_features''': raw_speech}       )

     # convert into correct format for padding

     snake_case_				:  List[Any]       =			self.pad(
         lowercase_						,  padding=lowercase_						,  max_length=max_length if max_length else self.n_samples						,  truncation=lowercase_						,  pad_to_multiple_of=lowercase_						,  return_attention_mask=return_attention_mask or do_normalize						,  )

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

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

     snake_case_				:  Optional[int]       =			[self._np_extract_fbank_features(lowercase_       ) for waveform in input_features[0]]

     if isinstance(input_features[0]						,  lowercase_       ):
         snake_case_				:  int       =			[np.asarray(lowercase_						,  dtype=np.floataa       ) for feature in input_features]
     else:
         snake_case_				:  List[Any]       =			input_features

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

     if return_tensors is not None:
         snake_case_				:  int       =			padded_inputs.convert_to_tensors(lowercase_       )

     return padded_inputs






 def 		_snake_case      (  self	: Optional[Any]       ):
     snake_case_				:  Optional[Any]       =			copy.deepcopy(self.__dict__       )
     snake_case_				:  List[Any]       =			self.__class__.__name__
     if "mel_filters" in output:
         del output["mel_filters"]
     return output










"""simple docstring"""

def __lowercase    (							_a	,    _a					):
    snake_case_				:  str       =			[0 for i in range(r + 1					)]
    # nc0 = 1
    snake_case_				:  int       =			1
    for i in range(1	,    n + 1					):
        # to compute current row from previous row.
        snake_case_				:  Any       =			min(_a	,    _a					)
        while j > 0:
            c[j] += c[j - 1]
            j -= 1
    return c[r]






print(binomial_coefficient(n=10, r=5))





