


'''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 PaddingStrategy, TensorType, logging


lowercase				=logging.get_logger(__name__)






class    __magic_name__				(						lowerCAmelCase							):
       UpperCAmelCase		 =["input_features", "attention_mask"]






       def __init__(     self    ,				snake_case=8_0    ,				snake_case=1_6_0_0_0    ,				snake_case=8_0    ,				snake_case=0.0    ,				snake_case=True    ,				snake_case=True    ,				snake_case=True    ,				**snake_case    ,				)		->    str:




          '''simple docstring'''
          super().__init__(feature_size=snake_case    ,				sampling_rate=snake_case    ,				padding_value=snake_case    ,				**snake_case)
          _UpperCAmelCase  :  Optional[Any]				       =num_mel_bins
          _UpperCAmelCase  :  Optional[int]				       =do_ceptral_normalize
          _UpperCAmelCase  :  Optional[Any]				       =normalize_means
          _UpperCAmelCase  :  Optional[Any]				       =normalize_vars
          _UpperCAmelCase  :  Tuple				       =True






       def      lowerCAmelCase						(     self    ,				snake_case    ,				)		->    np.ndarray:




          '''simple docstring'''
          _UpperCAmelCase  :  List[Any]				       =waveform * (2**1_5)  # Kaldi compliance: 16-bit signed integers
          _UpperCAmelCase  :  Dict				       =torch.from_numpy(snake_case).unsqueeze(0)
          _UpperCAmelCase  :  Union[str, Any]				       =ta_kaldi.fbank(snake_case    ,				num_mel_bins=self.num_mel_bins    ,				sample_frequency=self.sampling_rate)
          return features.numpy()






       @staticmethod
       def      lowerCAmelCase						(     snake_case    ,				snake_case    ,				snake_case = True    ,				snake_case = True    ,				snake_case = 0.0    ,				)		->    np.ndarray:




          '''simple docstring'''
          # make sure we normalize float32 arrays
          if normalize_means:
             _UpperCAmelCase  :  int				       =x[:input_length].mean(axis=0)
             _UpperCAmelCase  :  List[Any]				       =np.subtract(snake_case    ,				snake_case)
          if normalize_vars:
             _UpperCAmelCase  :  List[Any]				       =x[:input_length].std(axis=0)
             _UpperCAmelCase  :  Optional[int]				       =np.divide(snake_case    ,				snake_case)

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

          # make sure array is in float32
          _UpperCAmelCase  :  str				       =x.astype(np.floataa)

          return x






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case = None)		->    List[np.ndarray]:




          '''simple docstring'''
          _UpperCAmelCase  :  str				       =attention_mask.sum(-1) if attention_mask is not None else [x.shape[0] for x in input_features]
          return [
              self.utterance_cmvn(snake_case    ,				snake_case    ,				self.normalize_means    ,				self.normalize_vars    ,				self.padding_value)
              for x, n in zip(snake_case    ,				snake_case)
          ]






       def __call__(     self    ,				snake_case    ,				snake_case = False    ,				snake_case = None    ,				snake_case = False    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				**snake_case    ,				)		->    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.')

          _UpperCAmelCase  :  Optional[int]				       =isinstance(snake_case    ,				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  :  List[Any]				       =is_batched_numpy or (
              isinstance(snake_case    ,				(list, tuple)) and (isinstance(raw_speech[0]    ,				(np.ndarray, tuple, list)))
          )

          if is_batched:
             _UpperCAmelCase  :  int				       =[np.asarray(snake_case    ,				dtype=np.floataa) for speech in raw_speech]
          elif not is_batched and not isinstance(snake_case    ,				np.ndarray):
             _UpperCAmelCase  :  Tuple				       =np.asarray(snake_case    ,				dtype=np.floataa)
          elif isinstance(snake_case    ,				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  :  Dict				       =[raw_speech]

          # extract fbank features
          _UpperCAmelCase  :  Optional[Any]				       =[self._extract_fbank_features(snake_case) for waveform in raw_speech]

          # convert into correct format for padding
          _UpperCAmelCase  :  List[str]				       =BatchFeature({'input_features': features})

          _UpperCAmelCase  :  Any				       =self.pad(
              snake_case    ,				padding=snake_case    ,				max_length=snake_case    ,				truncation=snake_case    ,				pad_to_multiple_of=snake_case    ,				return_attention_mask=snake_case    ,				**snake_case    ,				)

          # make sure list is in array format
          _UpperCAmelCase  :  Dict				       =padded_inputs.get('input_features')
          if isinstance(input_features[0]    ,				snake_case):
             _UpperCAmelCase  :  Any				       =[np.asarray(snake_case    ,				dtype=np.floataa) for feature in input_features]

          _UpperCAmelCase  :  int				       =padded_inputs.get('attention_mask')
          if attention_mask is not None:
             _UpperCAmelCase  :  Tuple				       =[np.asarray(snake_case    ,				dtype=np.intaa) for array in attention_mask]

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

          if return_tensors is not None:
             _UpperCAmelCase  :  List[Any]				       =padded_inputs.convert_to_tensors(snake_case)

          return padded_inputs





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase				={
    'configuration_table_transformer': [
        'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TableTransformerConfig',
        'TableTransformerOnnxConfig',
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase				=[
     'TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TableTransformerForObjectDetection',
     'TableTransformerModel',
     'TableTransformerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_table_transformer import (
     TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     TableTransformerConfig,
     TableTransformerOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_table_transformer import (
      TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TableTransformerForObjectDetection,
      TableTransformerModel,
      TableTransformerPreTrainedModel,
  )

else:
 import sys

 lowercase				=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

