
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 PaddingStrategy, TensorType, logging


lowercase__		:List[str]			  =   logging.get_logger(__name__)







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	str	 =['''input_features''', '''attention_mask''']




							def __init__(    self       ,A__=8_0       ,A__=1_6_0_0_0       ,A__=8_0       ,A__=0.0       ,A__=True       ,A__=True       ,A__=True       ,**A__       ,):
												super().__init__(feature_size=A__       ,sampling_rate=A__       ,padding_value=A__       ,**A__)
												lowercase						     =						num_mel_bins
												lowercase						     =						do_ceptral_normalize
												lowercase						     =						normalize_means
												lowercase						     =						normalize_vars
												lowercase						     =						True




							def 			A__				(    self       ,A__       ,):
												lowercase						     =						waveform * (2**1_5)  # Kaldi compliance: 16-bit signed integers
												lowercase						     =						torch.from_numpy(A__).unsqueeze(0)
												lowercase						     =						ta_kaldi.fbank(A__       ,num_mel_bins=self.num_mel_bins       ,sample_frequency=self.sampling_rate)
												return features.numpy()




							@staticmethod
							def 			A__				(    A__       ,A__       ,A__ = True       ,A__ = True       ,A__ = 0.0       ,):
												# make sure we normalize float32 arrays
												if normalize_means:
																	lowercase						     =						x[:input_length].mean(axis=0)
																	lowercase						     =						np.subtract(A__       ,A__)
												if normalize_vars:
																	lowercase						     =						x[:input_length].std(axis=0)
																	lowercase						     =						np.divide(A__       ,A__)

												if input_length < x.shape[0]:
																	lowercase						     =						padding_value

												# make sure array is in float32
												lowercase						     =						x.astype(np.floataa)

												return x




							def 			A__				(    self       ,A__       ,A__ = None):
												lowercase						     =						attention_mask.sum(-1) if attention_mask is not None else [x.shape[0] for x in input_features]
												return [
												    self.utterance_cmvn(A__       ,A__       ,self.normalize_means       ,self.normalize_vars       ,self.padding_value)
												    for x, n in zip(A__       ,A__)
												]





							def __call__(    self       ,A__       ,A__ = False       ,A__ = None       ,A__ = False       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,**A__       ,):

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

												if is_batched:
																	lowercase						     =						[np.asarray(A__       ,dtype=np.floataa) for speech in raw_speech]
												elif not is_batched and not isinstance(A__       ,np.ndarray):
																	lowercase						     =						np.asarray(A__       ,dtype=np.floataa)
												elif isinstance(A__       ,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
												lowercase						     =						[self._extract_fbank_features(A__) for waveform in raw_speech]

												# convert into correct format for padding
												lowercase						     =						BatchFeature({'''input_features''': features})

												lowercase						     =						self.pad(
												    A__       ,padding=A__       ,max_length=A__       ,truncation=A__       ,pad_to_multiple_of=A__       ,return_attention_mask=A__       ,**A__       ,)

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

												lowercase						     =						padded_inputs.get('''attention_mask''')
												if attention_mask is not None:
																	lowercase						     =						[np.asarray(A__       ,dtype=np.intaa) for array in attention_mask]

												# Utterance-level cepstral mean and variance normalization
												if self.do_ceptral_normalize:
																	lowercase						     =						(
																	    np.array(A__       ,dtype=np.intaa)
																	    if self._get_padding_strategies(A__       ,max_length=A__) is not PaddingStrategy.DO_NOT_PAD
																	    else None
																	)
																	lowercase						     =						self.normalize(
																	    padded_inputs['''input_features''']       ,attention_mask=A__)

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

												return padded_inputs






from typing import TYPE_CHECKING

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


lowercase__		:str			  =   {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:str			  =   ["ViTFeatureExtractor"]
							lowercase__		:int			  =   ["ViTImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:int			  =   [
							    "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "ViTForImageClassification",
							    "ViTForMaskedImageModeling",
							    "ViTModel",
							    "ViTPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:Any			  =   [
							    "TFViTForImageClassification",
							    "TFViTModel",
							    "TFViTPreTrainedModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:Optional[int]			  =   [
							    "FlaxViTForImageClassification",
							    "FlaxViTModel",
							    "FlaxViTPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_vit import ViTFeatureExtractor
														from .image_processing_vit import ViTImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit import (
														    VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTForImageClassification,
														    ViTForMaskedImageModeling,
														    ViTModel,
														    ViTPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
							import sys

							lowercase__		:Dict			  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




