





"""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 TensorType, logging


__magic_name__					:				Tuple	= logging.get_logger(__name__)






class        lowercase__ (      __SCREAMING_SNAKE_CASE  ):

			"""simple docstring"""
			__lowerCAmelCase    :							List[str]								=     ["""input_values""", """attention_mask"""]







			def __init__(			self	,      _A=1	,      _A=1_6_0_0_0	,      _A=1_2_8	,      _A=1_0_2_4	,      _A=0.0	,      _A=True	,      _A=-4.2_67_73_93	,      _A=4.5_68_99_74	,      _A=False	,      **_A	,      ):






									'''simple docstring'''







									super().__init__(feature_size=_A	,      sampling_rate=_A	,      padding_value=_A	,      **_A						)
									UpperCamelCase							:		Any							      = num_mel_bins
									UpperCamelCase							:		Any							      = max_length
									UpperCamelCase							:		List[Any]							      = do_normalize
									UpperCamelCase							:		Tuple							      = mean
									UpperCamelCase							:		int							      = std
									UpperCamelCase							:		Tuple							      = return_attention_mask







			def      _a			(			self	,      _A	,      _A	,      ):






									'''simple docstring'''







									UpperCamelCase							:		List[str]							      = torch.from_numpy(_A						).unsqueeze(0						)
									UpperCamelCase							:		Tuple							      = ta_kaldi.fbank(
									    _A	,      htk_compat=_A	,      sample_frequency=self.sampling_rate	,      use_energy=_A	,      window_type="""hanning"""	,      num_mel_bins=self.num_mel_bins	,      dither=0.0	,      frame_shift=1_0	,      )

									UpperCamelCase							:		Dict							      = fbank.shape[0]
									UpperCamelCase							:		Optional[int]							      = max_length - n_frames

									# pad or truncate, depending on difference
									if difference > 0:
															UpperCamelCase							:		Union[str, Any]							      = torch.nn.ZeroPadad((0, 0, 0, difference)						)
															UpperCamelCase							:		Optional[int]							      = pad_module(_A						)
									elif difference < 0:
															UpperCamelCase							:		str							      = fbank[0:max_length, :]

									UpperCamelCase							:		int							      = fbank.numpy()

									return fbank







			def      _a			(			self	,      _A						):






									'''simple docstring'''







									return (input_values - (self.mean)) / (self.std * 2)







			def __call__(			self	,      _A	,      _A = None	,      _A = None	,      **_A	,      ):






									'''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."""						)

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

									if is_batched:
															UpperCamelCase							:		Optional[Any]							      = [np.asarray(_A	,      dtype=np.floataa						) for speech in raw_speech]
									elif not is_batched and not isinstance(_A	,      np.ndarray						):
															UpperCamelCase							:		Dict							      = np.asarray(_A	,      dtype=np.floataa						)
									elif isinstance(_A	,      np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
															UpperCamelCase							:		int							      = raw_speech.astype(np.floataa						)

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

									# extract fbank features and pad/truncate to max_length
									UpperCamelCase							:		List[Any]							      = [self._extract_fbank_features(_A	,      max_length=self.max_length						) for waveform in raw_speech]

									# convert into BatchFeature
									UpperCamelCase							:		Any							      = BatchFeature({"""input_values""": features}						)

									# make sure list is in array format
									UpperCamelCase							:		Any							      = padded_inputs.get("""input_values"""						)
									if isinstance(input_values[0]	,      _A						):
															UpperCamelCase							:		Union[str, Any]							      = [np.asarray(_A	,      dtype=np.floataa						) for feature in input_values]

									# normalization
									if self.do_normalize:
															UpperCamelCase							:		Dict							      = [self.normalize(_A						) for feature in input_values]

									if return_tensors is not None:
															UpperCamelCase							:		List[str]							      = padded_inputs.convert_to_tensors(_A						)

									return padded_inputs










"""simple docstring"""


def     UpperCamelCase       (SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE    ):
						UpperCamelCase							:		int							      = len(SCREAMING_SNAKE_CASE    )
						UpperCamelCase							:		int							      = len(SCREAMING_SNAKE_CASE    )
						UpperCamelCase							:		int							      = (
						    first_input_list_length
						    if first_input_list_length > second_input_list_length
						    else second_input_list_length
						)
						UpperCamelCase							:		list							      = []
						for char_count in range(SCREAMING_SNAKE_CASE    ):
												if char_count < first_input_list_length:
																		output_result_list.append(first_input_list[char_count]    )
												if char_count < second_input_list_length:
																		output_result_list.append(second_input_list[char_count]    )

						return output_result_list


if __name__ == "__main__":
		print(alternative_list_arrange(["""A""", """B""", """C"""], [1, 2, 3, 4, 5]), end=""" """)



