



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

import numpy as np

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


UpperCAmelCase_									=							logging.get_logger(__name__)





class 		UpperCAmelCase						(						snake_case_				):
   SCREAMING_SNAKE_CASE__							=     ['''input_features''']





   def __init__(	self       ,     _lowerCAmelCase=80       ,     _lowerCAmelCase=16_000       ,     _lowerCAmelCase=160       ,     _lowerCAmelCase=30       ,     _lowerCAmelCase=400       ,     _lowerCAmelCase=0.0       ,     _lowerCAmelCase=False       ,     **_lowerCAmelCase       ,     ):
         super().__init__(
             feature_size=_lowerCAmelCase       ,     sampling_rate=_lowerCAmelCase       ,     padding_value=_lowerCAmelCase       ,     return_attention_mask=_lowerCAmelCase       ,     **_lowerCAmelCase       ,     )
         _lowerCAmelCase 			=  n_fft
         _lowerCAmelCase 			=  hop_length
         _lowerCAmelCase 			=  chunk_length
         _lowerCAmelCase 			=  chunk_length * sampling_rate
         _lowerCAmelCase 			=  self.n_samples // hop_length
         _lowerCAmelCase 			=  sampling_rate
         _lowerCAmelCase 			=  mel_filter_bank(
             num_frequency_bins=1 + n_fft // 2       ,     num_mel_filters=_lowerCAmelCase       ,     min_frequency=0.0       ,     max_frequency=8_000.0       ,     sampling_rate=_lowerCAmelCase       ,     norm='''slaney'''       ,     mel_scale='''slaney'''       ,     )





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  spectrogram(
             _lowerCAmelCase       ,     window_function(self.n_fft       ,     '''hann'''			)       ,     frame_length=self.n_fft       ,     hop_length=self.hop_length       ,     power=2.0       ,     mel_filters=self.mel_filters       ,     log_mel='''log10'''       ,     )
         _lowerCAmelCase 			=  log_spec[:, :-1]
         _lowerCAmelCase 			=  np.maximum(_lowerCAmelCase       ,     log_spec.max() - 8.0			)
         _lowerCAmelCase 			=  (log_spec + 4.0) / 4.0
         return log_spec





   @staticmethod
   # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
   def 	__lowerCAmelCase							(	_lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = 0.0			):
         if attention_mask is not None:
               _lowerCAmelCase 			=  np.array(_lowerCAmelCase       ,     np.intaa			)
               _lowerCAmelCase 			=  []

               for vector, length in zip(_lowerCAmelCase       ,     attention_mask.sum(-1			)			):
                     _lowerCAmelCase 			=  (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7			)
                     if length < normed_slice.shape[0]:
                           _lowerCAmelCase 			=  padding_value

                     normed_input_values.append(_lowerCAmelCase			)
         else:
               _lowerCAmelCase 			=  [(x - x.mean()) / np.sqrt(x.var() + 1E-7			) for x in input_values]

         return normed_input_values





   def __call__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase = True       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = "max_length"       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     **_lowerCAmelCase       ,     ):

         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.'''			)

         _lowerCAmelCase 			=  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}'''			)
         _lowerCAmelCase 			=  is_batched_numpy or (
             isinstance(_lowerCAmelCase       ,     (list, tuple)			) and (isinstance(raw_speech[0]       ,     (np.ndarray, tuple, list)			))
         )

         if is_batched:
               _lowerCAmelCase 			=  [np.asarray([speech]       ,     dtype=np.floataa			).T for speech in raw_speech]
         elif not is_batched and not isinstance(_lowerCAmelCase       ,     np.ndarray			):
               _lowerCAmelCase 			=  np.asarray(_lowerCAmelCase       ,     dtype=np.floataa			)
         elif isinstance(_lowerCAmelCase       ,     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 			=  [np.asarray([raw_speech]			).T]

         _lowerCAmelCase 			=  BatchFeature({'''input_features''': raw_speech}			)

         # convert into correct format for padding

         _lowerCAmelCase 			=  self.pad(
             _lowerCAmelCase       ,     padding=_lowerCAmelCase       ,     max_length=max_length if max_length else self.n_samples       ,     truncation=_lowerCAmelCase       ,     pad_to_multiple_of=_lowerCAmelCase       ,     return_attention_mask=return_attention_mask or do_normalize       ,     )

         # zero-mean and unit-variance normalization
         if do_normalize:
               _lowerCAmelCase 			=  self.zero_mean_unit_var_norm(
                   padded_inputs['''input_features''']       ,     attention_mask=padded_inputs['''attention_mask''']       ,     padding_value=self.padding_value       ,     )
               _lowerCAmelCase 			=  np.stack(padded_inputs['''input_features''']       ,     axis=0			)

         # make sure list is in array format
         _lowerCAmelCase 			=  padded_inputs.get('''input_features'''			).transpose(2       ,     0       ,     1			)

         _lowerCAmelCase 			=  [self._np_extract_fbank_features(_lowerCAmelCase			) for waveform in input_features[0]]

         if isinstance(input_features[0]       ,     _lowerCAmelCase			):
               _lowerCAmelCase 			=  [np.asarray(_lowerCAmelCase       ,     dtype=np.floataa			) for feature in input_features]
         else:
               _lowerCAmelCase 			=  input_features

         if return_attention_mask:
               # rescale from sample (48000) to feature (3000)
               _lowerCAmelCase 			=  padded_inputs['''attention_mask'''][:, :: self.hop_length]

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

         return padded_inputs





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  copy.deepcopy(self.__dict__			)
         _lowerCAmelCase 			=  self.__class__.__name__
         if "mel_filters" in output:
               del output["mel_filters"]
         return output




from typing import TYPE_CHECKING

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


UpperCAmelCase_									=							{"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							["DeiTFeatureExtractor"]
    UpperCAmelCase_									=							["DeiTImageProcessor"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							[
        "DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "DeiTForImageClassification",
        "DeiTForImageClassificationWithTeacher",
        "DeiTForMaskedImageModeling",
        "DeiTModel",
        "DeiTPreTrainedModel",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							[
        "TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFDeiTForImageClassification",
        "TFDeiTForImageClassificationWithTeacher",
        "TFDeiTForMaskedImageModeling",
        "TFDeiTModel",
        "TFDeiTPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_deit import DeiTFeatureExtractor
        from .image_processing_deit import DeiTImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deit import (
            DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            DeiTForImageClassification,
            DeiTForImageClassificationWithTeacher,
            DeiTForMaskedImageModeling,
            DeiTModel,
            DeiTPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_deit import (
            TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFDeiTForImageClassification,
            TFDeiTForImageClassificationWithTeacher,
            TFDeiTForMaskedImageModeling,
            TFDeiTModel,
            TFDeiTPreTrainedModel,
        )


else:
    import sys

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