

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


_lowerCamelCase       :     str           =							logging.get_logger(__name__)





class       __UpperCAmelCase  ( lowerCamelCase__		):
   UpperCamelCase       =  ["""input_values""", """attention_mask"""]



   def __init__(							self							:  Union[str, Any],	__A							:  Dict=1,	__A							:  Any=1_6_0_0_0,	__A							:  List[str]=1_2_8,	__A							:  str=1_0_2_4,	__A							:  int=0.0,	__A							:  str=True,	__A							:  Any=-4.2_6_7_7_3_9_3,	__A							:  Optional[int]=4.5_6_8_9_9_7_4,	__A							:  Optional[int]=False,	**__A							:  str,	):
     super().__init__(feature_size=__A,	sampling_rate=__A,	padding_value=__A,	**__A		)
     UpperCAmelCase :							int								=				num_mel_bins
     UpperCAmelCase :							Tuple								=				max_length
     UpperCAmelCase :							str								=				do_normalize
     UpperCAmelCase :							List[Any]								=				mean
     UpperCAmelCase :							Union[str, Any]								=				std
     UpperCAmelCase :							Any								=				return_attention_mask



   def  __magic_name__					(							self							:  Dict,	__A							:  np.ndarray,	__A							:  int,	):
     UpperCAmelCase :							Union[str, Any]								=				torch.from_numpy(__A		).unsqueeze(0		)
     UpperCAmelCase :							List[str]								=				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 :							int								=				fbank.shape[0]
     UpperCAmelCase :							int								=				max_length - n_frames

     # pad or truncate, depending on difference
     if difference > 0:
       UpperCAmelCase :							List[Any]								=				torch.nn.ZeroPadad((0, 0, 0, difference)		)
       UpperCAmelCase :							int								=				pad_module(__A		)
     elif difference < 0:
       UpperCAmelCase :							Dict								=				fbank[0:max_length, :]

     UpperCAmelCase :							int								=				fbank.numpy()

     return fbank



   def  __magic_name__					(							self							:  Optional[Any],	__A							:  np.ndarray		):
     return (input_values - (self.mean)) / (self.std * 2)





   def __call__(							self							:  Union[str, Any],	__A							:  Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],	__A							:  Optional[int] = None,	__A							:  Optional[Union[str, TensorType]] = None,	**__A							:  Optional[Any],	):

     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 :							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 :							Dict								=				is_batched_numpy or (
         isinstance(__A,	(list, tuple)		) and (isinstance(raw_speech[0],	(np.ndarray, tuple, list)		))
     )

     if is_batched:
       UpperCAmelCase :							List[Any]								=				[np.asarray(__A,	dtype=np.floataa		) for speech in raw_speech]
     elif not is_batched and not isinstance(__A,	np.ndarray		):
       UpperCAmelCase :							Union[str, Any]								=				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 :							str								=				[raw_speech]

     # extract fbank features and pad/truncate to max_length
     UpperCAmelCase :							Union[str, Any]								=				[self._extract_fbank_features(__A,	max_length=self.max_length		) for waveform in raw_speech]

     # convert into BatchFeature
     UpperCAmelCase :							Optional[Any]								=				BatchFeature({'''input_values''': features}		)

     # make sure list is in array format
     UpperCAmelCase :							Optional[int]								=				padded_inputs.get('''input_values'''		)
     if isinstance(input_values[0],	__A		):
       UpperCAmelCase :							Dict								=				[np.asarray(__A,	dtype=np.floataa		) for feature in input_values]

     # normalization
     if self.do_normalize:
       UpperCAmelCase :							str								=				[self.normalize(__A		) for feature in input_values]

     if return_tensors is not None:
       UpperCAmelCase :							Tuple								=				padded_inputs.convert_to_tensors(__A		)

     return padded_inputs








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


_lowerCamelCase       :     int           =							{}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCamelCase       :     Tuple           =							["MBart50Tokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCamelCase       :     List[Any]           =							["MBart50TokenizerFast"]


if TYPE_CHECKING:
     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbartaa import MBartaaTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
     import sys

     _lowerCamelCase       :     Optional[int]           =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





