


'''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


lowerCAmelCase     :  str											=    logging.get_logger(__name__)
class 						UpperCamelCase__       (			SCREAMING_SNAKE_CASE_						):



     """simple docstring"""






     __magic_name__           =     ["input_features", "attention_mask"]







     def __init__(			self   ,						snake_case__=80   ,						snake_case__=1_6000   ,						snake_case__=80   ,						snake_case__=0.0   ,						snake_case__=True   ,						snake_case__=True   ,						snake_case__=True   ,						**snake_case__   ,						):

            '''simple docstring'''

            super().__init__(feature_size=snake_case__   ,						sampling_rate=snake_case__   ,						padding_value=snake_case__   ,						**snake_case__ )
            _lowerCAmelCase     :				Any       =							num_mel_bins
            _lowerCAmelCase     :				Optional[Any]       =							do_ceptral_normalize
            _lowerCAmelCase     :				Any       =							normalize_means
            _lowerCAmelCase     :				Optional[int]       =							normalize_vars
            _lowerCAmelCase     :				List[str]       =							True







     def a						(			self   ,						snake_case__   ,						):

            '''simple docstring'''

            _lowerCAmelCase     :				Optional[int]       =							waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
            _lowerCAmelCase     :				str       =							torch.from_numpy(snake_case__ ).unsqueeze(0 )
            _lowerCAmelCase     :				Union[str, Any]       =							ta_kaldi.fbank(snake_case__   ,						num_mel_bins=self.num_mel_bins   ,						sample_frequency=self.sampling_rate )
            return features.numpy()







     @staticmethod
     def a						(			snake_case__   ,						snake_case__   ,						snake_case__ = True   ,						snake_case__ = True   ,						snake_case__ = 0.0   ,						):

            '''simple docstring'''

            if normalize_means:
                   _lowerCAmelCase     :				Tuple       =							x[:input_length].mean(axis=0 )
                   _lowerCAmelCase     :				Dict       =							np.subtract(snake_case__   ,						snake_case__ )
            if normalize_vars:
                   _lowerCAmelCase     :				Dict       =							x[:input_length].std(axis=0 )
                   _lowerCAmelCase     :				int       =							np.divide(snake_case__   ,						snake_case__ )

            if input_length < x.shape[0]:
                   _lowerCAmelCase     :				Union[str, Any]       =							padding_value

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

            return x







     def a						(			self   ,						snake_case__   ,						snake_case__ = None ):

            '''simple docstring'''

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







     def __call__(			self   ,						snake_case__   ,						snake_case__ = False   ,						snake_case__ = None   ,						snake_case__ = False   ,						snake_case__ = None   ,						snake_case__ = None   ,						snake_case__ = None   ,						snake_case__ = None   ,						**snake_case__   ,						):

            '''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 `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(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}' )
            _lowerCAmelCase     :				Dict       =							is_batched_numpy or (
                isinstance(snake_case__   ,						(list, tuple) ) and (isinstance(raw_speech[0]   ,						(np.ndarray, tuple, list) ))
            )

            if is_batched:
                   _lowerCAmelCase     :				Optional[Any]       =							[np.asarray(snake_case__   ,						dtype=np.floataa ) for speech in raw_speech]
            elif not is_batched and not isinstance(snake_case__   ,						np.ndarray ):
                   _lowerCAmelCase     :				Dict       =							np.asarray(snake_case__   ,						dtype=np.floataa )
            elif isinstance(snake_case__   ,						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     :				Union[str, Any]       =							[raw_speech]

            # extract fbank features
            _lowerCAmelCase     :				Dict       =							[self._extract_fbank_features(snake_case__ ) for waveform in raw_speech]

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

            _lowerCAmelCase     :				str       =							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
            _lowerCAmelCase     :				List[str]       =							padded_inputs.get('input_features' )
            if isinstance(input_features[0]   ,						snake_case__ ):
                   _lowerCAmelCase     :				int       =							[np.asarray(snake_case__   ,						dtype=np.floataa ) for feature in input_features]

            _lowerCAmelCase     :				Optional[int]       =							padded_inputs.get('attention_mask' )
            if attention_mask is not None:
                   _lowerCAmelCase     :				Union[str, Any]       =							[np.asarray(snake_case__   ,						dtype=np.intaa ) for array in attention_mask]

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

            if return_tensors is not None:
                   _lowerCAmelCase     :				str       =							padded_inputs.convert_to_tensors(snake_case__ )

            return padded_inputs





'''simple docstring'''







def       lowercase					(_A       ):







       """simple docstring"""







       if len(_A       ) <= 1:
              return [tuple(_A       )]

       _lowerCAmelCase     :				List[str]       =							[]

       def generate(_A ,    _A       ):
              _lowerCAmelCase     :				Any       =							[0] * n
              res.append(tuple(_A       )       )

              _lowerCAmelCase     :				Any       =							0
              while i < n:
                     if c[i] < i:
                            if i % 2 == 0:
                                   _lowerCAmelCase      ,  _lowerCAmelCase     :				Tuple       =							arr[i], arr[0]
                            else:
                                   _lowerCAmelCase      ,  _lowerCAmelCase     :				List[str]       =							arr[i], arr[c[i]]
                            res.append(tuple(_A       )       )
                            c[i] += 1
                            _lowerCAmelCase     :				Optional[int]       =							0
                     else:
                            _lowerCAmelCase     :				Any       =							0
                            i += 1

       generate(len(_A       ) ,    _A       )
       return res


if __name__ == "__main__":
       lowerCAmelCase     :  int											=    input("""Enter numbers separated by a comma:\n""").strip()
       lowerCAmelCase     :  Optional[int]											=    [int(item) for item in user_input.split(""",""")]
       print(heaps(arr))

