

'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


UpperCAmelCase							=					logging.get_logger(__name__)


class 					lowerCAmelCase       ( A				):
       lowerCAmelCase_				=	["input_values", "attention_mask"]







       def __init__(							self     :	Any  , __lowercase     :	Tuple=1  , __lowercase     :	Tuple=16000  , __lowercase     :	Optional[Any]=128  , __lowercase     :	Dict=1024  , __lowercase     :	Optional[int]=0.0  , __lowercase     :	Union[str, Any]=True  , __lowercase     :	int=-4.2_6_7_7_3_9_3  , __lowercase     :	Union[str, Any]=4.5_6_8_9_9_7_4  , __lowercase     :	Tuple=False  , **__lowercase     :	Any  , ):







            """simple docstring"""







            super().__init__(feature_size=__lowercase  , sampling_rate=__lowercase  , padding_value=__lowercase  , **__lowercase )
            __lowercase     						=num_mel_bins
            __lowercase     						=max_length
            __lowercase     						=do_normalize
            __lowercase     						=mean
            __lowercase     						=std
            __lowercase     						=return_attention_mask







       def      snake_case				(							self     :	Union[str, Any]  , __lowercase     :	np.ndarray  , __lowercase     :	int  , ):







            """simple docstring"""







            __lowercase     						=torch.from_numpy(__lowercase ).unsqueeze(0 )
            __lowercase     						=ta_kaldi.fbank(
                __lowercase  , htk_compat=__lowercase  , sample_frequency=self.sampling_rate  , use_energy=__lowercase  , window_type='hanning'  , num_mel_bins=self.num_mel_bins  , dither=0.0  , frame_shift=10  , )

            __lowercase     						=fbank.shape[0]
            __lowercase     						=max_length - n_frames

            # pad or truncate, depending on difference
            if difference > 0:
                 __lowercase     						=torch.nn.ZeroPadad((0, 0, 0, difference) )
                 __lowercase     						=pad_module(__lowercase )
            elif difference < 0:
                 __lowercase     						=fbank[0:max_length, :]

            __lowercase     						=fbank.numpy()

            return fbank







       def      snake_case				(							self     :	Dict  , __lowercase     :	np.ndarray ):







            """simple docstring"""







            return (input_values - (self.mean)) / (self.std * 2)







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







            """simple docstring"""







            if sampling_rate is not None:
                 if sampling_rate != self.sampling_rate:
                      raise ValueError(
                          f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
                          f''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
                          f''' {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.' )

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

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

            # always return batch
            if not is_batched:
                 __lowercase     						=[raw_speech]

            # extract fbank features and pad/truncate to max_length
            __lowercase     						=[self._extract_fbank_features(__lowercase  , max_length=self.max_length ) for waveform in raw_speech]

            # convert into BatchFeature
            __lowercase     						=BatchFeature({'input_values': features} )

            # make sure list is in array format
            __lowercase     						=padded_inputs.get('input_values' )
            if isinstance(input_values[0]  , __lowercase ):
                 __lowercase     						=[np.asarray(__lowercase  , dtype=np.floataa ) for feature in input_values]

            # normalization
            if self.do_normalize:
                 __lowercase     						=[self.normalize(__lowercase ) for feature in input_values]

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

            return padded_inputs





'''simple docstring'''

from typing import TYPE_CHECKING

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


UpperCAmelCase							=					{'''configuration_vit_hybrid''': ['''VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTHybridConfig''']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase							=					[
           '''VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''ViTHybridForImageClassification''',
           '''ViTHybridModel''',
           '''ViTHybridPreTrainedModel''',
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase							=					['''ViTHybridImageProcessor''']


if TYPE_CHECKING:
       from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vit_hybrid import (
                  VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ViTHybridForImageClassification,
                  ViTHybridModel,
                  ViTHybridPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
       import sys

       UpperCAmelCase							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


