






'''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       =      logging.get_logger(__name__)





class      A							(    __UpperCAmelCase			):
			lowerCamelCase							:							List[Any]    						=					["""input_values""", """attention_mask"""]



			def __init__(   self    ,       lowerCamelCase__=1    ,       lowerCamelCase__=16_000    ,       lowerCamelCase__=0.0    ,       lowerCamelCase__=False    ,       lowerCamelCase__=True    ,       **lowerCamelCase__    ,       )     ->						Union[str, Any]:




									'''simple docstring'''



									super().__init__(feature_size=lowerCamelCase__    ,       sampling_rate=lowerCamelCase__    ,       padding_value=lowerCamelCase__    ,       **lowerCamelCase__    )
									lowercase__								=    return_attention_mask
									lowercase__								=    do_normalize



			@staticmethod
			def A__	(   lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__ = 0.0    )     ->						List[np.ndarray]:




									'''simple docstring'''



									if attention_mask is not None:
															lowercase__								=    np.array(lowerCamelCase__    ,       np.intaa    )
															lowercase__								=    []

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

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

									return normed_input_values



			def __call__(   self    ,       lowerCamelCase__    ,       lowerCamelCase__ = False    ,       lowerCamelCase__ = None    ,       lowerCamelCase__ = False    ,       lowerCamelCase__ = None    ,       lowerCamelCase__ = None    ,       lowerCamelCase__ = None    ,       lowerCamelCase__ = None    ,       **lowerCamelCase__    ,       )     ->						BatchFeature:




									'''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."""    )

									lowercase__								=    isinstance(lowerCamelCase__    ,       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}'''    )
									lowercase__								=    is_batched_numpy or (
									    isinstance(lowerCamelCase__    ,       (list, tuple)    ) and (isinstance(raw_speech[0]    ,       (np.ndarray, tuple, list)    ))
									)

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

									# convert into correct format for padding
									lowercase__								=    BatchFeature({"""input_values""": raw_speech}    )

									lowercase__								=    self.pad(
									    lowerCamelCase__    ,       padding=lowerCamelCase__    ,       max_length=lowerCamelCase__    ,       truncation=lowerCamelCase__    ,       pad_to_multiple_of=lowerCamelCase__    ,       return_attention_mask=lowerCamelCase__    ,       )

									# convert input values to correct format
									lowercase__								=    padded_inputs["""input_values"""]
									if not isinstance(input_values[0]    ,       np.ndarray    ):
															lowercase__								=    [np.asarray(lowerCamelCase__    ,       dtype=np.floataa    ) for array in input_values]
									elif (
									    not isinstance(lowerCamelCase__    ,       np.ndarray    )
									    and isinstance(input_values[0]    ,       np.ndarray    )
									    and input_values[0].dtype is np.dtype(np.floataa    )
									):
															lowercase__								=    [array.astype(np.floataa    ) for array in input_values]
									elif isinstance(lowerCamelCase__    ,       np.ndarray    ) and input_values.dtype is np.dtype(np.floataa    ):
															lowercase__								=    input_values.astype(np.floataa    )

									# convert attention_mask to correct format
									lowercase__								=    padded_inputs.get("""attention_mask"""    )
									if attention_mask is not None:
															lowercase__								=    [np.asarray(lowerCamelCase__    ,       dtype=np.intaa    ) for array in attention_mask]

									# zero-mean and unit-variance normalization
									if self.do_normalize:
															lowercase__								=    (
															    attention_mask
															    if self._get_padding_strategies(lowerCamelCase__    ,       max_length=lowerCamelCase__    ) is not PaddingStrategy.DO_NOT_PAD
															    else None
															)
															lowercase__								=    self.zero_mean_unit_var_norm(
															    padded_inputs["""input_values"""]    ,       attention_mask=lowerCamelCase__    ,       padding_value=self.padding_value    )

									if return_tensors is not None:
															lowercase__								=    padded_inputs.convert_to_tensors(lowerCamelCase__    )

									return padded_inputs








'''simple docstring'''



def 	_A						(							lowercase__		):
						lowercase__								=    1.3
						lowercase__								=    len(lowercase__		)
						lowercase__								=    False

						while not completed:
												# Update the gap value for a next comb
												lowercase__								=    int(gap / shrink_factor		)
												if gap <= 1:
																		lowercase__								=    True

												lowercase__								=    0
												while index + gap < len(lowercase__		):
																		if data[index] > data[index + gap]:
																								# Swap values
																								lowercase__	,  lowercase__								=    data[index + gap], data[index]
																								lowercase__								=    False
																		index += 1

						return data


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				__A       =      input("Enter numbers separated by a comma:\n").strip()
				__A       =      [int(item) for item in user_input.split(",")]
				print(comb_sort(unsorted))






