




'''simple docstring'''


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


lowerCAmelCase_      :  Optional[Any]				     =  logging.get_logger(__name__)







class      SCREAMING_SNAKE_CASE    (						snake_case_				):
      __magic_name__  :       Optional[int]   							=					['''input_features''', '''is_longer''']






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


           '''simple docstring'''

           super().__init__(
               feature_size=lowercase__						,						sampling_rate=lowercase__						,						padding_value=lowercase__						,						return_attention_mask=lowercase__						,						**lowercase__						,						)
           a_     :							str     						=  top_db
           a_     :							int     						=  truncation
           a_     :							Optional[int]     						=  padding
           a_     :							Tuple     						=  fft_window_size
           a_     :							str     						=  (fft_window_size >> 1) + 1
           a_     :							Tuple     						=  hop_length
           a_     :							Union[str, Any]     						=  max_length_s
           a_     :							Optional[int]     						=  max_length_s * sampling_rate
           a_     :							List[Any]     						=  sampling_rate
           a_     :							List[Any]     						=  frequency_min
           a_     :							Union[str, Any]     						=  frequency_max
           a_     :							Tuple     						=  mel_filter_bank(
               num_frequency_bins=self.nb_frequency_bins						,						num_mel_filters=lowercase__						,						min_frequency=lowercase__						,						max_frequency=lowercase__						,						sampling_rate=lowercase__						,						norm=lowercase__						,						mel_scale="""htk"""						,						)
           a_     :							List[str]     						=  mel_filter_bank(
               num_frequency_bins=self.nb_frequency_bins						,						num_mel_filters=lowercase__						,						min_frequency=lowercase__						,						max_frequency=lowercase__						,						sampling_rate=lowercase__						,						norm="""slaney"""						,						mel_scale="""slaney"""						,						)






      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  copy.deepcopy(self.__dict__       )
           a_     :							List[str]     						=  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 							lowercase_						( self   :  Tuple						,						lowercase__   :  np.array						,						lowercase__   :  Optional[np.array] = None       ):


           '''simple docstring'''

           a_     :							List[str]     						=  spectrogram(
               lowercase__						,						window_function(self.fft_window_size						,						"""hann"""       )						,						frame_length=self.fft_window_size						,						hop_length=self.hop_length						,						power=2.0						,						mel_filters=lowercase__						,						log_mel="""dB"""						,						)
           return log_mel_spectrogram.T






      def 							lowercase_						( self   :  int						,						lowercase__   :  Tuple						,						lowercase__   :  Union[str, Any]						,						lowercase__   :  Dict       ):


           '''simple docstring'''

           a_     :							Any     						=  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
                a_     :							Union[str, Any]     						=  [0]
           if len(ranges[2]       ) == 0:
                # if the audio is too short, we just use the first chunk
                a_     :							str     						=  [0]
           # randomly choose index for each part
           a_     :							Union[str, Any]     						=  np.random.choice(ranges[0]       )
           a_     :							Optional[Any]     						=  np.random.choice(ranges[1]       )
           a_     :							Dict     						=  np.random.choice(ranges[2]       )

           a_     :							Tuple     						=  mel[idx_front : idx_front + chunk_frames, :]
           a_     :							Any     						=  mel[idx_middle : idx_middle + chunk_frames, :]
           a_     :							Tuple     						=  mel[idx_back : idx_back + chunk_frames, :]

           a_     :							int     						=  torch.tensor(mel[None, None, :]       )
           a_     :							Optional[int]     						=  torch.nn.functional.interpolate(
               lowercase__						,						size=[chunk_frames, 64]						,						mode="""bilinear"""						,						align_corners=lowercase__       )
           a_     :							Any     						=  mel_shrink[0][0].numpy()
           a_     :							Union[str, Any]     						=  np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]						,						axis=0       )
           return mel_fusion






      def 							lowercase_						( self   :  List[Any]						,						lowercase__   :  np.array						,						lowercase__   :  Dict						,						lowercase__   :  Optional[Any]						,						lowercase__   :  Union[str, Any]       ):


           '''simple docstring'''

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

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

                if truncation == "fusion":
                     a_     :							Union[str, Any]     						=  self._np_extract_fbank_features(lowercase__						,						self.mel_filters       )
                     a_     :							Tuple     						=  np.stack([input_mel, input_mel, input_mel, input_mel]						,						axis=0       )
                else:
                     a_     :							List[str]     						=  self._np_extract_fbank_features(lowercase__						,						self.mel_filters_slaney       )[None, :]

           return input_mel, longer






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


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  truncation if truncation is not None else self.truncation
           a_     :							Tuple     						=  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."""       )

           a_     :							int     						=  isinstance(lowercase__						,						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}"       )
           a_     :							str     						=  is_batched_numpy or (
               isinstance(lowercase__						,						(list, tuple)       ) and (isinstance(raw_speech[0]						,						(np.ndarray, tuple, list)       ))
           )

           if is_batched:
                a_     :							int     						=  [np.asarray(lowercase__						,						dtype=np.floataa       ) for speech in raw_speech]
           elif not is_batched and not isinstance(lowercase__						,						np.ndarray       ):
                a_     :							Dict     						=  np.asarray(lowercase__						,						dtype=np.floataa       )
           elif isinstance(lowercase__						,						np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
                a_     :							Any     						=  raw_speech.astype(np.floataa       )

           # always return batch
           if not is_batched:
                a_     :							str     						=  [np.asarray(lowercase__       )]

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

           a_     :							List[Any]     						=  []
           a_     :							str     						=  []
           for mel, longer in padded_inputs:
                input_mel.append(lowercase__       )
                is_longer.append(lowercase__       )

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

           if isinstance(input_mel[0]						,						lowercase__       ):
                a_     :							Any     						=  [np.asarray(lowercase__						,						dtype=np.floataa       ) for feature in input_mel]

           # is_longer is a list of bool
           a_     :							List[Any]     						=  [[longer] for longer in is_longer]

           a_     :							Any     						=  {"""input_features""": input_mel, """is_longer""": is_longer}
           a_     :							str     						=  BatchFeature(lowercase__       )

           if return_tensors is not None:
                a_     :							Dict     						=  input_features.convert_to_tensors(lowercase__       )

           return input_features






'''simple docstring'''


from typing import TYPE_CHECKING

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


lowerCAmelCase_      :  Optional[Any]				     =  {
    'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MobileViTConfig', 'MobileViTOnnxConfig'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  int				     =  ['MobileViTFeatureExtractor']
 lowerCAmelCase_      :  int				     =  ['MobileViTImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  str				     =  [
     'MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MobileViTForImageClassification',
     'MobileViTForSemanticSegmentation',
     'MobileViTModel',
     'MobileViTPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_      :  Optional[Any]				     =  [
     'TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFMobileViTForImageClassification',
     'TFMobileViTForSemanticSegmentation',
     'TFMobileViTModel',
     'TFMobileViTPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_mobilevit import MobileViTFeatureExtractor
  from .image_processing_mobilevit import MobileViTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mobilevit import (
      MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      MobileViTForImageClassification,
      MobileViTForSemanticSegmentation,
      MobileViTModel,
      MobileViTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mobilevit import (
      TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFMobileViTForImageClassification,
      TFMobileViTForSemanticSegmentation,
      TFMobileViTModel,
      TFMobileViTPreTrainedModel,
  )


else:
 import sys

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