




from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


lowerCamelCase				:		Optional[int]     =   logging.get_logger(__name__)







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						['''input_features''', '''attention_mask''']

 def __init__(				self					:  List[str]						,							A_					:  Optional[int]=80						,							A_					:  List[str]=16000						,							A_					:  str=0.0						,							A_					:  List[Any]=10						,							A_					:  List[str]=25						,							A_					:  Optional[Any]="hamming_window"						,							A_					:  Any=32768.0						,							A_					:  int=0.97						,							A_					:  Union[str, Any]=1.0						,							A_					:  str=True						,							A_					:  Union[str, Any]=True						,							A_					:  Optional[Any]=False						,							**A_					:  Optional[Any]						,							)   ->	Dict:

       """simple docstring"""






       super().__init__(feature_size=A_						,							sampling_rate=A_						,							padding_value=A_						,							**A_  )

       lowerCamelCase_		     =    feature_size
       lowerCamelCase_		     =    sampling_rate
       lowerCamelCase_		     =    padding_value
       lowerCamelCase_		     =    hop_length
       lowerCamelCase_		     =    win_length
       lowerCamelCase_		     =    frame_signal_scale
       lowerCamelCase_		     =    preemphasis_coeff
       lowerCamelCase_		     =    mel_floor
       lowerCamelCase_		     =    normalize_means
       lowerCamelCase_		     =    normalize_vars
       lowerCamelCase_		     =    win_function
       lowerCamelCase_		     =    return_attention_mask

       lowerCamelCase_		     =    win_length * sampling_rate // 1000
       lowerCamelCase_		     =    hop_length * sampling_rate // 1000

       lowerCamelCase_		     =    optimal_fft_length(self.sample_size  )
       lowerCamelCase_		     =    (self.n_fft // 2) + 1

 def 				a__ (				self					:  str						,							A_					:  np.array  )   ->	np.ndarray:

       """simple docstring"""






       if self.win_function == "hamming_window":
             lowerCamelCase_		     =    window_function(window_length=self.sample_size						,							name=self.win_function						,							periodic=A_  )
       else:
             lowerCamelCase_		     =    window_function(window_length=self.sample_size						,							name=self.win_function  )

       lowerCamelCase_		     =    mel_filter_bank(
           num_frequency_bins=self.n_freqs						,							num_mel_filters=self.feature_size						,							min_frequency=0.0						,							max_frequency=self.sampling_rate / 2.0						,							sampling_rate=self.sampling_rate						,							)

       lowerCamelCase_		     =    spectrogram(
           one_waveform * self.frame_signal_scale						,							window=A_						,							frame_length=self.sample_size						,							hop_length=self.sample_stride						,							fft_length=self.n_fft						,							center=A_						,							preemphasis=self.preemphasis_coeff						,							mel_filters=A_						,							mel_floor=self.mel_floor						,							log_mel='log'						,							)
       return msfc_features.T

 def 				a__ (				self					:  Any						,							A_					:  Dict						,							A_					:  int						,							A_					:  List[str]  )   ->	List[str]:

       """simple docstring"""






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

       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					:  Dict						,							A_					:  List[np.ndarray]						,							A_					:  Optional[np.ndarray] = None  )   ->	List[np.ndarray]:

       """simple docstring"""






       lowerCamelCase_		     =    attention_mask.sum(-1  ) if attention_mask is not None else [x.shape[0] for x in input_features]
       return [self._normalize_one(A_						,							A_						,							self.padding_value  ) for x, n in zip(A_						,							A_  )]





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

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

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

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

       lowerCamelCase_		     =    self.pad(
           A_						,							padding=A_						,							max_length=A_						,							truncation=A_						,							pad_to_multiple_of=A_						,							return_attention_mask=A_						,							**A_						,							)
       # make sure list is in array format
       lowerCamelCase_		     =    padded_inputs.get('input_features'  )
       if isinstance(input_features[0]						,							A_  ):
             lowerCamelCase_		     =    [np.asarray(A_						,							dtype=np.floataa  ) for feature in input_features]

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

       if self.normalize_means or self.normalize_vars:
             lowerCamelCase_		     =    (
                 np.array(A_						,							dtype=np.intaa  )
                 if self._get_padding_strategies(A_						,							max_length=A_  ) is not PaddingStrategy.DO_NOT_PAD
                 and padding
                 else None
             )
             lowerCamelCase_		     =    self.normalize(
                 padded_inputs['input_features']						,							attention_mask=A_  )

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

       return padded_inputs









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase				:		Union[str, Any]     =   {
    "configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertOnnxConfig"],
    "tokenization_convbert": ["ConvBertTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		Optional[Any]     =   ["ConvBertTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		Dict     =   [
     "CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ConvBertForMaskedLM",
     "ConvBertForMultipleChoice",
     "ConvBertForQuestionAnswering",
     "ConvBertForSequenceClassification",
     "ConvBertForTokenClassification",
     "ConvBertLayer",
     "ConvBertModel",
     "ConvBertPreTrainedModel",
     "load_tf_weights_in_convbert",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		Tuple     =   [
     "TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFConvBertForMaskedLM",
     "TFConvBertForMultipleChoice",
     "TFConvBertForQuestionAnswering",
     "TFConvBertForSequenceClassification",
     "TFConvBertForTokenClassification",
     "TFConvBertLayer",
     "TFConvBertModel",
     "TFConvBertPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
 from .tokenization_convbert import ConvBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_convbert_fast import ConvBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_convbert import (
      CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConvBertForMaskedLM,
      ConvBertForMultipleChoice,
      ConvBertForQuestionAnswering,
      ConvBertForSequenceClassification,
      ConvBertForTokenClassification,
      ConvBertLayer,
      ConvBertModel,
      ConvBertPreTrainedModel,
      load_tf_weights_in_convbert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_convbert import (
      TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFConvBertForMaskedLM,
      TFConvBertForMultipleChoice,
      TFConvBertForQuestionAnswering,
      TFConvBertForSequenceClassification,
      TFConvBertForTokenClassification,
      TFConvBertLayer,
      TFConvBertModel,
      TFConvBertPreTrainedModel,
  )


else:
 import sys

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



