






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:     Dict   					=					logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  ['''input_features''', '''is_longer''']


  def __init__(      self		:			Optional[Any]       ,				lowerCamelCase		:			int=64       ,				lowerCamelCase		:			int=4_8000       ,				lowerCamelCase		:			List[str]=480       ,				lowerCamelCase		:			int=10       ,				lowerCamelCase		:			Optional[int]=1024       ,				lowerCamelCase		:			List[Any]=0.0       ,				lowerCamelCase		:			Tuple=False       ,				lowerCamelCase		:			float = 0       ,				lowerCamelCase		:			float = 1_4000       ,				lowerCamelCase		:			int = None       ,				lowerCamelCase		:			str = "fusion"       ,				lowerCamelCase		:			str = "repeatpad"       ,				**lowerCamelCase		:			Union[str, Any]       ,				)						->	Tuple:


         """simple docstring"""
         super().__init__(
             feature_size=lowerCamelCase       ,				sampling_rate=lowerCamelCase       ,				padding_value=lowerCamelCase       ,				return_attention_mask=lowerCamelCase       ,				**lowerCamelCase       ,				)
         _UpperCAmelCase							=    top_db
         _UpperCAmelCase							=    truncation
         _UpperCAmelCase							=    padding
         _UpperCAmelCase							=    fft_window_size
         _UpperCAmelCase							=    (fft_window_size >> 1) + 1
         _UpperCAmelCase							=    hop_length
         _UpperCAmelCase							=    max_length_s
         _UpperCAmelCase							=    max_length_s * sampling_rate
         _UpperCAmelCase							=    sampling_rate
         _UpperCAmelCase							=    frequency_min
         _UpperCAmelCase							=    frequency_max
         _UpperCAmelCase							=    mel_filter_bank(
             num_frequency_bins=self.nb_frequency_bins       ,				num_mel_filters=lowerCamelCase       ,				min_frequency=lowerCamelCase       ,				max_frequency=lowerCamelCase       ,				sampling_rate=lowerCamelCase       ,				norm=lowerCamelCase       ,				mel_scale="""htk"""       ,				)
         _UpperCAmelCase							=    mel_filter_bank(
             num_frequency_bins=self.nb_frequency_bins       ,				num_mel_filters=lowerCamelCase       ,				min_frequency=lowerCamelCase       ,				max_frequency=lowerCamelCase       ,				sampling_rate=lowerCamelCase       ,				norm="""slaney"""       ,				mel_scale="""slaney"""       ,				)


  def  lowerCamelCase       (      self		:			List[Any]							)						->	Dict[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    copy.deepcopy(self.__dict__							)
         _UpperCAmelCase							=    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  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			np.array       ,				lowerCamelCase		:			Optional[np.array] = None							)						->	np.ndarray:


         """simple docstring"""
         _UpperCAmelCase							=    spectrogram(
             lowerCamelCase       ,				window_function(self.fft_window_size       ,				"""hann"""							)       ,				frame_length=self.fft_window_size       ,				hop_length=self.hop_length       ,				power=2.0       ,				mel_filters=lowerCamelCase       ,				log_mel="""dB"""       ,				)
         return log_mel_spectrogram.T


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Optional[int]							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    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							=    [0]
         if len(ranges[2]							) == 0:
                # if the audio is too short, we just use the first chunk
                _UpperCAmelCase							=    [0]
         # randomly choose index for each part
         _UpperCAmelCase							=    np.random.choice(ranges[0]							)
         _UpperCAmelCase							=    np.random.choice(ranges[1]							)
         _UpperCAmelCase							=    np.random.choice(ranges[2]							)

         _UpperCAmelCase							=    mel[idx_front : idx_front + chunk_frames, :]
         _UpperCAmelCase							=    mel[idx_middle : idx_middle + chunk_frames, :]
         _UpperCAmelCase							=    mel[idx_back : idx_back + chunk_frames, :]

         _UpperCAmelCase							=    torch.tensor(mel[None, None, :]							)
         _UpperCAmelCase							=    torch.nn.functional.interpolate(
             lowerCamelCase       ,				size=[chunk_frames, 64]       ,				mode="""bilinear"""       ,				align_corners=lowerCamelCase							)
         _UpperCAmelCase							=    mel_shrink[0][0].numpy()
         _UpperCAmelCase							=    np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]       ,				axis=0							)
         return mel_fusion


  def  lowerCamelCase       (      self		:			str       ,				lowerCamelCase		:			np.array       ,				lowerCamelCase		:			Dict       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Optional[Any]							)						->	np.array:


         """simple docstring"""
         if waveform.shape[0] > max_length:
                if truncation == "rand_trunc":
                       _UpperCAmelCase							=    True
                       # random crop to max_length (for compatibility) -> this should be handled by self.pad
                       _UpperCAmelCase							=    len(lowerCamelCase							) - max_length
                       _UpperCAmelCase							=    np.random.randint(0       ,				overflow + 1							)
                       _UpperCAmelCase							=    waveform[idx : idx + max_length]
                       _UpperCAmelCase							=    self._np_extract_fbank_features(lowerCamelCase       ,				self.mel_filters_slaney							)[None, :]
                elif truncation == "fusion":
                       _UpperCAmelCase							=    self._np_extract_fbank_features(lowerCamelCase       ,				self.mel_filters							)
                       _UpperCAmelCase							=    max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                       _UpperCAmelCase							=    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							=    np.stack([mel, mel, mel, mel]       ,				axis=0							)
                              _UpperCAmelCase							=    False
                       else:
                              _UpperCAmelCase							=    self._random_mel_fusion(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase							)
                              _UpperCAmelCase							=    True
                else:
                       raise NotImplementedError(f"""data_truncating {truncation} not implemented"""							)

         else:
                _UpperCAmelCase							=    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							=    int(max_length / len(lowerCamelCase							)							)
                              _UpperCAmelCase							=    np.stack(np.tile(lowerCamelCase       ,				n_repeat + 1							)							)[:max_length]
                       if padding == "repeatpad":
                              _UpperCAmelCase							=    int(max_length / len(lowerCamelCase							)							)
                              _UpperCAmelCase							=    np.stack(np.tile(lowerCamelCase       ,				lowerCamelCase							)							)
                       _UpperCAmelCase							=    np.pad(lowerCamelCase       ,				(0, max_length - waveform.shape[0])       ,				mode="""constant"""       ,				constant_values=0							)

                if truncation == "fusion":
                       _UpperCAmelCase							=    self._np_extract_fbank_features(lowerCamelCase       ,				self.mel_filters							)
                       _UpperCAmelCase							=    np.stack([input_mel, input_mel, input_mel, input_mel]       ,				axis=0							)
                else:
                       _UpperCAmelCase							=    self._np_extract_fbank_features(lowerCamelCase       ,				self.mel_filters_slaney							)[None, :]

         return input_mel, longer





  def __call__(      self		:			int       ,				lowerCamelCase		:			Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]       ,				lowerCamelCase		:			str = None       ,				lowerCamelCase		:			Optional[str] = None       ,				lowerCamelCase		:			Optional[int] = None       ,				lowerCamelCase		:			Optional[int] = None       ,				lowerCamelCase		:			Optional[Union[str, TensorType]] = None       ,				**lowerCamelCase		:			int       ,				)						->	BatchFeature:


         """simple docstring"""
         _UpperCAmelCase							=    truncation if truncation is not None else self.truncation
         _UpperCAmelCase							=    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							=    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}"""							)
         _UpperCAmelCase							=    is_batched_numpy or (
             isinstance(lowerCamelCase       ,				(list, tuple)							) and (isinstance(raw_speech[0]       ,				(np.ndarray, tuple, list)							))
         )

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

         # always return batch
         if not is_batched:
                _UpperCAmelCase							=    [np.asarray(lowerCamelCase							)]

         # convert to mel spectrogram, truncate and pad if needed.
         _UpperCAmelCase							=    [
             self._get_input_mel(lowerCamelCase       ,				max_length if max_length else self.nb_max_samples       ,				lowerCamelCase       ,				lowerCamelCase							)
             for waveform in raw_speech
         ]

         _UpperCAmelCase							=    []
         _UpperCAmelCase							=    []
         for mel, longer in padded_inputs:
                input_mel.append(lowerCamelCase							)
                is_longer.append(lowerCamelCase							)

         if truncation == "fusion" and sum(lowerCamelCase							) == 0:
                # if no audio is longer than 10s, then randomly select one audio to be longer
                _UpperCAmelCase							=    np.random.randint(0       ,				len(lowerCamelCase							)							)
                _UpperCAmelCase							=    True

         if isinstance(input_mel[0]       ,				lowerCamelCase							):
                _UpperCAmelCase							=    [np.asarray(lowerCamelCase       ,				dtype=np.floataa							) for feature in input_mel]

         # is_longer is a list of bool
         _UpperCAmelCase							=    [[longer] for longer in is_longer]

         _UpperCAmelCase							=    {"""input_features""": input_mel, """is_longer""": is_longer}
         _UpperCAmelCase							=    BatchFeature(lowerCamelCase							)

         if return_tensors is not None:
                _UpperCAmelCase							=    input_features.convert_to_tensors(lowerCamelCase							)

         return input_features







from typing import TYPE_CHECKING

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


__a:     int   					=					{
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a:     Dict   					=					['''MvpTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a:     Tuple   					=					[
      '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''MvpForCausalLM''',
      '''MvpForConditionalGeneration''',
      '''MvpForQuestionAnswering''',
      '''MvpForSequenceClassification''',
      '''MvpModel''',
      '''MvpPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
  from .tokenization_mvp import MvpTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mvp_fast import MvpTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mvp import (
        MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
        MvpForCausalLM,
        MvpForConditionalGeneration,
        MvpForQuestionAnswering,
        MvpForSequenceClassification,
        MvpModel,
        MvpPreTrainedModel,
    )

else:
  import sys

  __a:     Optional[Any]   					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)