


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


_A     =  logging.get_logger(__name__)





class 			_lowerCAmelCase      (			_UpperCAmelCase ):
				_lowercase    	=['''input_features''', '''attention_mask''']




				def __init__(      self       ,       _UpperCamelCase=80       ,       _UpperCamelCase=16_000       ,       _UpperCamelCase=80       ,       _UpperCamelCase=0.0       ,       _UpperCamelCase=True       ,       _UpperCamelCase=True       ,       _UpperCamelCase=True       ,       **_UpperCamelCase       ,       )       ->   Tuple:
							super().__init__(feature_size=lowerCamelCase_       ,       sampling_rate=lowerCamelCase_       ,       padding_value=lowerCamelCase_       ,       **lowerCamelCase_  )
							lowerCAmelCase_      			=			num_mel_bins
							lowerCAmelCase_      			=			do_ceptral_normalize
							lowerCAmelCase_      			=			normalize_means
							lowerCAmelCase_      			=			normalize_vars
							lowerCAmelCase_      			=			True




				def   __a		(      self       ,       _UpperCamelCase       ,       )       ->   np.ndarray:
							lowerCAmelCase_      			=			waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
							lowerCAmelCase_      			=			torch.from_numpy(lowerCamelCase_  ).unsqueeze(0  )
							lowerCAmelCase_      			=			ta_kaldi.fbank(lowerCamelCase_       ,       num_mel_bins=self.num_mel_bins       ,       sample_frequency=self.sampling_rate  )
							return features.numpy()




				@staticmethod
				def   __a		(      _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase = True       ,       _UpperCamelCase = True       ,       _UpperCamelCase = 0.0       ,       )       ->   np.ndarray:




							if normalize_means:
										lowerCAmelCase_      			=			x[:input_length].mean(axis=0  )
										lowerCAmelCase_      			=			np.subtract(lowerCamelCase_       ,       lowerCamelCase_  )
							if normalize_vars:
										lowerCAmelCase_      			=			x[:input_length].std(axis=0  )
										lowerCAmelCase_      			=			np.divide(lowerCamelCase_       ,       lowerCamelCase_  )

							if input_length < x.shape[0]:
										lowerCAmelCase_      			=			padding_value

							# make sure array is in float32
							lowerCAmelCase_      			=			x.astype(np.floataa  )

							return x




				def   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = None  )       ->   List[np.ndarray]:
							lowerCAmelCase_      			=			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       ,       _UpperCamelCase       ,       _UpperCamelCase = False       ,       _UpperCamelCase = None       ,       _UpperCamelCase = False       ,       _UpperCamelCase = None       ,       _UpperCamelCase = None       ,       _UpperCamelCase = None       ,       _UpperCamelCase = None       ,       **_UpperCamelCase       ,       )       ->   BatchFeature:




							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_      			=			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}"""  )
							lowerCAmelCase_      			=			is_batched_numpy or (
							    isinstance(lowerCamelCase_       ,       (list, tuple)  ) and (isinstance(raw_speech[0]       ,       (np.ndarray, tuple, list)  ))
							)

							if is_batched:
										lowerCAmelCase_      			=			[np.asarray(lowerCamelCase_       ,       dtype=np.floataa  ) for speech in raw_speech]
							elif not is_batched and not isinstance(lowerCamelCase_       ,       np.ndarray  ):
										lowerCAmelCase_      			=			np.asarray(lowerCamelCase_       ,       dtype=np.floataa  )
							elif isinstance(lowerCamelCase_       ,       np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
										lowerCAmelCase_      			=			raw_speech.astype(np.floataa  )

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

							# extract fbank features
							lowerCAmelCase_      			=			[self._extract_fbank_features(lowerCamelCase_  ) for waveform in raw_speech]

							# convert into correct format for padding
							lowerCAmelCase_      			=			BatchFeature({"input_features": features}  )

							lowerCAmelCase_      			=			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
							lowerCAmelCase_      			=			padded_inputs.get("input_features"  )
							if isinstance(input_features[0]       ,       lowerCamelCase_  ):
										lowerCAmelCase_      			=			[np.asarray(lowerCamelCase_       ,       dtype=np.floataa  ) for feature in input_features]

							lowerCAmelCase_      			=			padded_inputs.get("attention_mask"  )
							if attention_mask is not None:
										lowerCAmelCase_      			=			[np.asarray(lowerCamelCase_       ,       dtype=np.intaa  ) for array in attention_mask]

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

							if return_tensors is not None:
										lowerCAmelCase_      			=			padded_inputs.convert_to_tensors(lowerCamelCase_  )

							return padded_inputs





from string import ascii_lowercase, ascii_uppercase






def      lowerCamelCase__       (       __lowerCAmelCase						:						str       ):




			"""simple docstring"""


			if not sentence:
						return ""
			lowerCAmelCase_      			=			dict(zip(__lowerCAmelCase					,							__lowerCAmelCase       )       )
			return lower_to_upper.get(sentence[0]					,							sentence[0]       ) + sentence[1:]


if __name__ == "__main__":
			from doctest import testmod

			testmod()



