






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        lowerCAmelCase_				(      lowercase	):







				"""simple docstring"""




				_snake_case				:   Tuple        =			["""input_features""", """attention_mask"""]







				def __init__( self     :int  ,     lowerCamelCase__     :Optional[int]=80  ,     lowerCamelCase__     :Any=1_60_00  ,     lowerCamelCase__     :Union[str, Any]=80  ,     lowerCamelCase__     :List[Any]=0.0  ,     lowerCamelCase__     :List[Any]=True  ,     lowerCamelCase__     :List[Any]=True  ,     lowerCamelCase__     :str=True  ,     **lowerCamelCase__     :str  ,     ):
					super().__init__(feature_size=lowerCamelCase__  ,     sampling_rate=lowerCamelCase__  ,     padding_value=lowerCamelCase__  ,     **lowerCamelCase__     )
					UpperCamelCase__ :Optional[Any]						=      num_mel_bins
					UpperCamelCase__ :Dict						=      do_ceptral_normalize
					UpperCamelCase__ :Optional[Any]						=      normalize_means
					UpperCamelCase__ :Optional[int]						=      normalize_vars
					UpperCamelCase__ :List[Any]						=      True







				def 				__a   ( self     :Any  ,     lowerCamelCase__     :np.ndarray  ,     ):
					UpperCamelCase__ :Union[str, Any]						=      waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
					UpperCamelCase__ :str						=      torch.from_numpy(lowerCamelCase__     ).unsqueeze(0     )
					UpperCamelCase__ :Tuple						=      ta_kaldi.fbank(lowerCamelCase__  ,     num_mel_bins=self.num_mel_bins  ,     sample_frequency=self.sampling_rate     )
					return features.numpy()







				@staticmethod
				def 				__a   ( lowerCamelCase__     :np.ndarray  ,     lowerCamelCase__     :int  ,     lowerCamelCase__     :Optional[bool] = True  ,     lowerCamelCase__     :Optional[bool] = True  ,     lowerCamelCase__     :float = 0.0  ,     ):
					# make sure we normalize float32 arrays
					if normalize_means:
						UpperCamelCase__ :Any						=      x[:input_length].mean(axis=0     )
						UpperCamelCase__ :int						=      np.subtract(lowerCamelCase__  ,     lowerCamelCase__     )
					if normalize_vars:
						UpperCamelCase__ :str						=      x[:input_length].std(axis=0     )
						UpperCamelCase__ :str						=      np.divide(lowerCamelCase__  ,     lowerCamelCase__     )

					if input_length < x.shape[0]:
						UpperCamelCase__ :List[str]						=      padding_value

					# make sure array is in float32
					UpperCamelCase__ :Optional[int]						=      x.astype(np.floataa     )

					return x







				def 				__a   ( self     :Optional[Any]  ,     lowerCamelCase__     :List[np.ndarray]  ,     lowerCamelCase__     :Optional[np.ndarray] = None     ):
					UpperCamelCase__ :List[Any]						=      attention_mask.sum(-1     ) if attention_mask is not None else [x.shape[0] for x in input_features]
					return [
					    self.utterance_cmvn(lowerCamelCase__  ,     lowerCamelCase__  ,     self.normalize_means  ,     self.normalize_vars  ,     self.padding_value     )
					    for x, n in zip(lowerCamelCase__  ,     lowerCamelCase__     )
					]







				def __call__( self     :List[Any]  ,     lowerCamelCase__     :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]  ,     lowerCamelCase__     :Union[bool, str, PaddingStrategy] = False  ,     lowerCamelCase__     :Optional[int] = None  ,     lowerCamelCase__     :bool = False  ,     lowerCamelCase__     :Optional[int] = None  ,     lowerCamelCase__     :Optional[Union[str, TensorType]] = None  ,     lowerCamelCase__     :Optional[int] = None  ,     lowerCamelCase__     :Optional[bool] = None  ,     **lowerCamelCase__     :Dict  ,     ):

					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__ :Union[str, Any]						=      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}"""     )
					UpperCamelCase__ :Union[str, Any]						=      is_batched_numpy or (
					    isinstance(lowerCamelCase__  ,     (list, tuple)     ) and (isinstance(raw_speech[0]  ,     (np.ndarray, tuple, list)     ))
					)

					if is_batched:
						UpperCamelCase__ :List[str]						=      [np.asarray(lowerCamelCase__  ,     dtype=np.floataa     ) for speech in raw_speech]
					elif not is_batched and not isinstance(lowerCamelCase__  ,     np.ndarray     ):
						UpperCamelCase__ :str						=      np.asarray(lowerCamelCase__  ,     dtype=np.floataa     )
					elif isinstance(lowerCamelCase__  ,     np.ndarray     ) and raw_speech.dtype is np.dtype(np.floataa     ):
						UpperCamelCase__ :Dict						=      raw_speech.astype(np.floataa     )

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

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

					# convert into correct format for padding
					UpperCamelCase__ :Optional[Any]						=      BatchFeature({"""input_features""": features}     )

					UpperCamelCase__ :List[Any]						=      self.pad(
					    lowerCamelCase__  ,     padding=lowerCamelCase__  ,     max_length=lowerCamelCase__  ,     truncation=lowerCamelCase__  ,     pad_to_multiple_of=lowerCamelCase__  ,     return_attention_mask=lowerCamelCase__  ,     **lowerCamelCase__  ,     )

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

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

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

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

					return padded_inputs




from __future__ import annotations


def A    (							__UpperCamelCase = 4       )    ->   list[list[int]]:
			A__	  =     abs(__UpperCamelCase       ) or 4
			return [[1 + x + y * row_size for x in range(__UpperCamelCase       )] for y in range(__UpperCamelCase       )]


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			return reverse_row(transpose(__UpperCamelCase       )       )
			# OR.. transpose(reverse_column(matrix))


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			return reverse_row(reverse_column(__UpperCamelCase       )       )
			# OR.. reverse_column(reverse_row(matrix))


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			return reverse_column(transpose(__UpperCamelCase       )       )
			# OR.. transpose(reverse_row(matrix))


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			A__	  =     [list(__UpperCamelCase       ) for x in zip(*__UpperCamelCase       )]
			return matrix


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			A__	  =     matrix[::-1]
			return matrix


def A    (							__UpperCamelCase       )    ->   list[list[int]]:
			A__	  =     [x[::-1] for x in matrix]
			return matrix


def A    (							__UpperCamelCase       )    ->   None:
			for i in matrix:
						print(*__UpperCamelCase       )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   make_matrix()
							print('''\norigin:\n''')
							print_matrix(matrix)
							print('''\nrotate 90 counterclockwise:\n''')
							print_matrix(rotate_aa(matrix))

							SCREAMING_SNAKE_CASE__   			=   make_matrix()
							print('''\norigin:\n''')
							print_matrix(matrix)
							print('''\nrotate 180:\n''')
							print_matrix(rotate_aaa(matrix))

							SCREAMING_SNAKE_CASE__   			=   make_matrix()
							print('''\norigin:\n''')
							print_matrix(matrix)
							print('''\nrotate 270 counterclockwise:\n''')
							print_matrix(rotate_aaa(matrix))




