



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


_lowercase						  =		logging.get_logger(__name__)



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				['''input_features''', '''is_longer''']




     def __init__(				self ,						a__=6_4 ,						a__=4_8_0_0_0 ,						a__=4_8_0 ,						a__=1_0 ,						a__=1_0_2_4 ,						a__=0.0 ,						a__=False ,						a__ = 0 ,						a__ = 1_4_0_0_0 ,						a__ = None ,						a__ = "fusion" ,						a__ = "repeatpad" ,						**a__ ,						):
         super().__init__(
             feature_size=a__ ,						sampling_rate=a__ ,						padding_value=a__ ,						return_attention_mask=a__ ,						**a__ ,						)
         A__		 =					top_db
         A__		 =					truncation
         A__		 =					padding
         A__		 =					fft_window_size
         A__		 =					(fft_window_size >> 1) + 1
         A__		 =					hop_length
         A__		 =					max_length_s
         A__		 =					max_length_s * sampling_rate
         A__		 =					sampling_rate
         A__		 =					frequency_min
         A__		 =					frequency_max
         A__		 =					mel_filter_bank(
             num_frequency_bins=self.nb_frequency_bins ,						num_mel_filters=a__ ,						min_frequency=a__ ,						max_frequency=a__ ,						sampling_rate=a__ ,						norm=a__ ,						mel_scale='''htk''' ,						)
         A__		 =					mel_filter_bank(
             num_frequency_bins=self.nb_frequency_bins ,						num_mel_filters=a__ ,						min_frequency=a__ ,						max_frequency=a__ ,						sampling_rate=a__ ,						norm='''slaney''' ,						mel_scale='''slaney''' ,						)




     def 		snake_case_		(				self):
         A__		 =					copy.deepcopy(self.__dict__)
         A__		 =					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 		snake_case_		(				self ,						a__ ,						a__ = None):
         A__		 =					spectrogram(
             a__ ,						window_function(self.fft_window_size ,						'''hann''') ,						frame_length=self.fft_window_size ,						hop_length=self.hop_length ,						power=2.0 ,						mel_filters=a__ ,						log_mel='''dB''' ,						)
         return log_mel_spectrogram.T




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__):
         A__		 =					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__		 =					[0]
         if len(ranges[2]) == 0:
             # if the audio is too short, we just use the first chunk
             A__		 =					[0]
         # randomly choose index for each part
         A__		 =					np.random.choice(ranges[0])
         A__		 =					np.random.choice(ranges[1])
         A__		 =					np.random.choice(ranges[2])

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

         A__		 =					torch.tensor(mel[None, None, :])
         A__		 =					torch.nn.functional.interpolate(
             a__ ,						size=[chunk_frames, 6_4] ,						mode='''bilinear''' ,						align_corners=a__)
         A__		 =					mel_shrink[0][0].numpy()
         A__		 =					np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,						axis=0)
         return mel_fusion




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

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

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

         return input_mel, longer




     def __call__(				self ,						a__ ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						a__ = None ,						**a__ ,						):
         A__		 =					truncation if truncation is not None else self.truncation
         A__		 =					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__		 =					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}")
         A__		 =					is_batched_numpy or (
             isinstance(a__ ,						(list, tuple)) and (isinstance(raw_speech[0] ,						(np.ndarray, tuple, list)))
         )

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

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

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

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

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

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

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

         A__		 =					{'''input_features''': input_mel, '''is_longer''': is_longer}
         A__		 =					BatchFeature(a__)

         if return_tensors is not None:
             A__		 =					input_features.convert_to_tensors(a__)

         return input_features





from typing import TYPE_CHECKING

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


_lowercase						  =		{
    "configuration_mobilevit": ["MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MobileViTConfig", "MobileViTOnnxConfig"],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		["MobileViTFeatureExtractor"]
   _lowercase						  =		["MobileViTImageProcessor"]

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

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		[
       "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

   _lowercase						  =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
