






import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

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


a						:			Tuple     	=					logging.get_logger(__name__)
class        a       (					a__  ):



      """simple docstring"""


      a    :					Union[str, Any]							=      ['input_features', 'is_longer']
      def __init__( self   :     Dict						, __lowercase   :     int=64						, __lowercase   :     str=48000						, __lowercase   :     Optional[Any]=480						, __lowercase   :     int=10						, __lowercase   :     Tuple=1024						, __lowercase   :     List[Any]=0.0						, __lowercase   :     Dict=False						, __lowercase   :     Any = 0						, __lowercase   :     int = 14000						, __lowercase   :     List[str] = None						, __lowercase   :     str = "fusion"						, __lowercase   :     Union[str, Any] = "repeatpad"						, **__lowercase   :     Optional[int]						, )				-> Tuple:
        super().__init__(
            feature_size=_snake_case						, sampling_rate=_snake_case						, padding_value=_snake_case						, return_attention_mask=_snake_case						, **_snake_case						, )
        __UpperCAmelCase				:      List[Any]	 =    top_db
        __UpperCAmelCase				:      List[Any]	 =    truncation
        __UpperCAmelCase				:      Optional[int]	 =    padding
        __UpperCAmelCase				:      List[str]	 =    fft_window_size
        __UpperCAmelCase				:      Any	 =    (fft_window_size >> 1) + 1
        __UpperCAmelCase				:      Union[str, Any]	 =    hop_length
        __UpperCAmelCase				:      Dict	 =    max_length_s
        __UpperCAmelCase				:      List[Any]	 =    max_length_s * sampling_rate
        __UpperCAmelCase				:      str	 =    sampling_rate
        __UpperCAmelCase				:      int	 =    frequency_min
        __UpperCAmelCase				:      Dict	 =    frequency_max
        __UpperCAmelCase				:      Optional[Any]	 =    mel_filter_bank(
            num_frequency_bins=self.nb_frequency_bins						, num_mel_filters=_snake_case						, min_frequency=_snake_case						, max_frequency=_snake_case						, sampling_rate=_snake_case						, norm=_snake_case						, mel_scale="""htk"""						, )
        __UpperCAmelCase				:      Any	 =    mel_filter_bank(
            num_frequency_bins=self.nb_frequency_bins						, num_mel_filters=_snake_case						, min_frequency=_snake_case						, max_frequency=_snake_case						, sampling_rate=_snake_case						, norm="""slaney"""						, mel_scale="""slaney"""						, )
      def       UpperCAmelCase ( self   :     List[str]   )				-> Optional[int]:
        __UpperCAmelCase				:      int	 =    copy.deepcopy(self.__dict__   )
        __UpperCAmelCase				:      Any	 =    self.__class__.__name__
        if "mel_filters" in output:
          del output["mel_filters"]
        if "mel_filters_slaney" in output:
          del output["mel_filters_slaney"]
        return output
      def       UpperCAmelCase ( self   :     Optional[Any]						, __lowercase   :     Optional[Any]						, __lowercase   :     Dict = None   )				-> List[Any]:
        __UpperCAmelCase				:      str	 =    spectrogram(
            _snake_case						, window_function(self.fft_window_size						, """hann"""   )						, frame_length=self.fft_window_size						, hop_length=self.hop_length						, power=2.0						, mel_filters=_snake_case						, log_mel="""dB"""						, )
        return log_mel_spectrogram.T
      def       UpperCAmelCase ( self   :     Optional[Any]						, __lowercase   :     Tuple						, __lowercase   :     Tuple						, __lowercase   :     Optional[int]   )				-> Optional[int]:
        __UpperCAmelCase				:      Tuple	 =    np.array_split(list(range(0						, total_frames - chunk_frames + 1   )   )						, 3   )
        if len(ranges[1]   ) == 0:
          # if the audio is too short, we just use the first chunk
          __UpperCAmelCase				:      int	 =    [0]
        if len(ranges[2]   ) == 0:
          # if the audio is too short, we just use the first chunk
          __UpperCAmelCase				:      int	 =    [0]
        # randomly choose index for each part
        __UpperCAmelCase				:      Optional[int]	 =    np.random.choice(ranges[0]   )
        __UpperCAmelCase				:      List[str]	 =    np.random.choice(ranges[1]   )
        __UpperCAmelCase				:      List[str]	 =    np.random.choice(ranges[2]   )

        __UpperCAmelCase				:      Union[str, Any]	 =    mel[idx_front : idx_front + chunk_frames, :]
        __UpperCAmelCase				:      Union[str, Any]	 =    mel[idx_middle : idx_middle + chunk_frames, :]
        __UpperCAmelCase				:      int	 =    mel[idx_back : idx_back + chunk_frames, :]

        __UpperCAmelCase				:      Any	 =    torch.tensor(mel[None, None, :]   )
        __UpperCAmelCase				:      Optional[int]	 =    torch.nn.functional.interpolate(
            _snake_case						, size=[chunk_frames, 64]						, mode="""bilinear"""						, align_corners=_snake_case   )
        __UpperCAmelCase				:      Tuple	 =    mel_shrink[0][0].numpy()
        __UpperCAmelCase				:      Any	 =    np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]						, axis=0   )
        return mel_fusion
      def       UpperCAmelCase ( self   :     Optional[Any]						, __lowercase   :     Optional[Any]						, __lowercase   :     Any						, __lowercase   :     Union[str, Any]						, __lowercase   :     Dict   )				-> str:






        if waveform.shape[0] > max_length:
          if truncation == "rand_trunc":
            __UpperCAmelCase				:      List[Any]	 =    True
            # random crop to max_length (for compatibility) -> this should be handled by self.pad
            __UpperCAmelCase				:      str	 =    len(_snake_case   ) - max_length
            __UpperCAmelCase				:      Tuple	 =    np.random.randint(0						, overflow + 1   )
            __UpperCAmelCase				:      Tuple	 =    waveform[idx : idx + max_length]
            __UpperCAmelCase				:      str	 =    self._np_extract_fbank_features(_snake_case						, self.mel_filters_slaney   )[None, :]
          elif truncation == "fusion":
            __UpperCAmelCase				:      str	 =    self._np_extract_fbank_features(_snake_case						, self.mel_filters   )
            __UpperCAmelCase				:      Any	 =    max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
            __UpperCAmelCase				:      Union[str, Any]	 =    mel.shape[0]
            if chunk_frames == total_frames:
              # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
              # In this case, we just use the whole audio.
              __UpperCAmelCase				:      List[Any]	 =    np.stack([mel, mel, mel, mel]						, axis=0   )
              __UpperCAmelCase				:      Any	 =    False
            else:
              __UpperCAmelCase				:      Dict	 =    self._random_mel_fusion(_snake_case						, _snake_case						, _snake_case   )
              __UpperCAmelCase				:      Optional[Any]	 =    True
          else:
            raise NotImplementedError(f"""data_truncating {truncation} not implemented"""   )

        else:
          __UpperCAmelCase				:      List[str]	 =    False
          # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
          if waveform.shape[0] < max_length:
            if padding == "repeat":
              __UpperCAmelCase				:      List[str]	 =    int(max_length / len(_snake_case   )   )
              __UpperCAmelCase				:      Tuple	 =    np.stack(np.tile(_snake_case						, n_repeat + 1   )   )[:max_length]
            if padding == "repeatpad":
              __UpperCAmelCase				:      List[Any]	 =    int(max_length / len(_snake_case   )   )
              __UpperCAmelCase				:      int	 =    np.stack(np.tile(_snake_case						, _snake_case   )   )
            __UpperCAmelCase				:      List[str]	 =    np.pad(_snake_case						, (0, max_length - waveform.shape[0])						, mode="""constant"""						, constant_values=0   )

          if truncation == "fusion":
            __UpperCAmelCase				:      Tuple	 =    self._np_extract_fbank_features(_snake_case						, self.mel_filters   )
            __UpperCAmelCase				:      Optional[int]	 =    np.stack([input_mel, input_mel, input_mel, input_mel]						, axis=0   )
          else:
            __UpperCAmelCase				:      List[str]	 =    self._np_extract_fbank_features(_snake_case						, self.mel_filters_slaney   )[None, :]

        return input_mel, longer




      def __call__( self   :     int						, __lowercase   :     Union[str, Any]						, __lowercase   :     List[Any] = None						, __lowercase   :     Any = None						, __lowercase   :     List[Any] = None						, __lowercase   :     Dict = None						, __lowercase   :     List[Any] = None						, **__lowercase   :     List[str]						, )				-> List[Any]:
        __UpperCAmelCase				:      str	 =    truncation if truncation is not None else self.truncation
        __UpperCAmelCase				:      int	 =    padding if padding else self.padding

        if sampling_rate is not None:
          if sampling_rate != self.sampling_rate:
            raise ValueError(
                f"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
                f""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
                f""" was sampled with {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				:      Optional[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				:      List[str]	 =    np.asarray(_snake_case						, dtype=np.floataa   )
        elif isinstance(_snake_case						, np.ndarray   ) and raw_speech.dtype is np.dtype(np.floataa   ):
          __UpperCAmelCase				:      str	 =    raw_speech.astype(np.floataa   )

        # always return batch
        if not is_batched:
          __UpperCAmelCase				:      Tuple	 =    [np.asarray(_snake_case   )]

        # convert to mel spectrogram, truncate and pad if needed.
        __UpperCAmelCase				:      List[Any]	 =    [
            self._get_input_mel(_snake_case						, max_length if max_length else self.nb_max_samples						, _snake_case						, _snake_case   )
            for waveform in raw_speech
        ]

        __UpperCAmelCase				:      str	 =    []
        __UpperCAmelCase				:      int	 =    []
        for mel, longer in padded_inputs:
          input_mel.append(_snake_case   )
          is_longer.append(_snake_case   )

        if truncation == "fusion" and sum(_snake_case   ) == 0:
          # if no audio is longer than 10s, then randomly select one audio to be longer
          __UpperCAmelCase				:      int	 =    np.random.randint(0						, len(_snake_case   )   )
          __UpperCAmelCase				:      Union[str, Any]	 =    True

        if isinstance(input_mel[0]						, _snake_case   ):
          __UpperCAmelCase				:      int	 =    [np.asarray(_snake_case						, dtype=np.floataa   ) for feature in input_mel]

        # is_longer is a list of bool
        __UpperCAmelCase				:      Dict	 =    [[longer] for longer in is_longer]

        __UpperCAmelCase				:      str	 =    {"""input_features""": input_mel, """is_longer""": is_longer}
        __UpperCAmelCase				:      Union[str, Any]	 =    BatchFeature(_snake_case   )

        if return_tensors is not None:
          __UpperCAmelCase				:      int	 =    input_features.convert_to_tensors(_snake_case   )

        return input_features









"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCamelCase     :						Union[str, Any]						       =  logging.get_logger(__name__)

__UpperCamelCase     :						Tuple						       =  {
    '''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/config.json''',
    '''bert-large-uncased''': '''https://huggingface.co/bert-large-uncased/resolve/main/config.json''',
    '''bert-base-cased''': '''https://huggingface.co/bert-base-cased/resolve/main/config.json''',
    '''bert-large-cased''': '''https://huggingface.co/bert-large-cased/resolve/main/config.json''',
    '''bert-base-multilingual-uncased''': '''https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json''',
    '''bert-base-multilingual-cased''': '''https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json''',
    '''bert-base-chinese''': '''https://huggingface.co/bert-base-chinese/resolve/main/config.json''',
    '''bert-base-german-cased''': '''https://huggingface.co/bert-base-german-cased/resolve/main/config.json''',
    '''bert-large-uncased-whole-word-masking''': (
        '''https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'''
    ),
    '''bert-large-cased-whole-word-masking''': (
        '''https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'''
    ),
    '''bert-large-uncased-whole-word-masking-finetuned-squad''': (
        '''https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'''
    ),
    '''bert-large-cased-whole-word-masking-finetuned-squad''': (
        '''https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'''
    ),
    '''bert-base-cased-finetuned-mrpc''': '''https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json''',
    '''bert-base-german-dbmdz-cased''': '''https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json''',
    '''bert-base-german-dbmdz-uncased''': '''https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json''',
    '''cl-tohoku/bert-base-japanese''': '''https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json''',
    '''cl-tohoku/bert-base-japanese-whole-word-masking''': (
        '''https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'''
    ),
    '''cl-tohoku/bert-base-japanese-char''': (
        '''https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'''
    ),
    '''cl-tohoku/bert-base-japanese-char-whole-word-masking''': (
        '''https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'''
    ),
    '''TurkuNLP/bert-base-finnish-cased-v1''': (
        '''https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'''
    ),
    '''TurkuNLP/bert-base-finnish-uncased-v1''': (
        '''https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'''
    ),
    '''wietsedv/bert-base-dutch-cased''': '''https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json''',
    # See all BERT models at https://huggingface.co/models?filter=bert
}







class       a							(   a__  ):
 snake_case__             =       '''bert'''



 def __init__(     self				,       _snake_case=3_05_22				,       _snake_case=7_68				,       _snake_case=12				,       _snake_case=12				,       _snake_case=30_72				,       _snake_case="gelu"				,       _snake_case=0.1				,       _snake_case=0.1				,       _snake_case=5_12				,       _snake_case=2				,       _snake_case=0.02				,       _snake_case=1E-12				,       _snake_case=0				,       _snake_case="absolute"				,       _snake_case=True				,       _snake_case=None				,       **_snake_case				,       ):







  """simple docstring"""






  super().__init__(pad_token_id=_snake_case				,       **_snake_case     )

  lowerCAmelCase    				= vocab_size
  lowerCAmelCase    				= hidden_size
  lowerCAmelCase    				= num_hidden_layers
  lowerCAmelCase    				= num_attention_heads
  lowerCAmelCase    				= hidden_act
  lowerCAmelCase    				= intermediate_size
  lowerCAmelCase    				= hidden_dropout_prob
  lowerCAmelCase    				= attention_probs_dropout_prob
  lowerCAmelCase    				= max_position_embeddings
  lowerCAmelCase    				= type_vocab_size
  lowerCAmelCase    				= initializer_range
  lowerCAmelCase    				= layer_norm_eps
  lowerCAmelCase    				= position_embedding_type
  lowerCAmelCase    				= use_cache
  lowerCAmelCase    				= classifier_dropout







class       a							(   a__  ):



 @property
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  if self.task == "multiple-choice":
   lowerCAmelCase    				= {0: 'batch', 1: 'choice', 2: 'sequence'}
  else:
   lowerCAmelCase    				= {0: 'batch', 1: 'sequence'}
  return OrderedDict(
      [
          ('input_ids', dynamic_axis),
          ('attention_mask', dynamic_axis),
          ('token_type_ids', dynamic_axis),
      ]     )





