

"""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


__A	:       List[Any]        =						logging.get_logger(__name__)





class      __UpperCamelCase					(  _A		):
		SCREAMING_SNAKE_CASE														=       ["input_values", "attention_mask"]


		def __init__(self					:						List[Any]	,	__SCREAMING_SNAKE_CASE					:						Any=1	,	__SCREAMING_SNAKE_CASE					:						List[str]=1_6_0_0_0	,	__SCREAMING_SNAKE_CASE					:						List[Any]=1_2_8	,	__SCREAMING_SNAKE_CASE					:						str=1_0_2_4	,	__SCREAMING_SNAKE_CASE					:						Optional[Any]=0.0	,	__SCREAMING_SNAKE_CASE					:						Optional[Any]=True	,	__SCREAMING_SNAKE_CASE					:						str=-4.2_6_7_7_3_9_3	,	__SCREAMING_SNAKE_CASE					:						Tuple=4.5_6_8_9_9_7_4	,	__SCREAMING_SNAKE_CASE					:						int=False	,	**__SCREAMING_SNAKE_CASE					:						Optional[int]	,	):
							super().__init__(feature_size=__SCREAMING_SNAKE_CASE	,	sampling_rate=__SCREAMING_SNAKE_CASE	,	padding_value=__SCREAMING_SNAKE_CASE	,	**__SCREAMING_SNAKE_CASE)
							A     =   num_mel_bins
							A     =   max_length
							A     =   do_normalize
							A     =   mean
							A     =   std
							A     =   return_attention_mask


		def    SCREAMING_SNAKE_CASE__  (self					:						int	,	__SCREAMING_SNAKE_CASE					:						np.ndarray	,	__SCREAMING_SNAKE_CASE					:						int	,	):
							A     =   torch.from_numpy(__SCREAMING_SNAKE_CASE).unsqueeze(0)
							A     =   ta_kaldi.fbank(
							    __SCREAMING_SNAKE_CASE	,	htk_compat=__SCREAMING_SNAKE_CASE	,	sample_frequency=self.sampling_rate	,	use_energy=__SCREAMING_SNAKE_CASE	,	window_type="hanning"	,	num_mel_bins=self.num_mel_bins	,	dither=0.0	,	frame_shift=1_0	,	)

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

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

							A     =   fbank.numpy()

							return fbank


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]	,	__SCREAMING_SNAKE_CASE					:						np.ndarray):
							return (input_values - (self.mean)) / (self.std * 2)


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

							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.")

							A     =   isinstance(__SCREAMING_SNAKE_CASE	,	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(__SCREAMING_SNAKE_CASE	,	(list, tuple)) and (isinstance(raw_speech[0]	,	(np.ndarray, tuple, list)))
							)

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

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

							# convert into BatchFeature
							A     =   BatchFeature({"input_values": features})

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

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

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

							return padded_inputs








"""simple docstring"""




from typing import TYPE_CHECKING

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


__A	:       Dict        =						{'configuration_cvt': ['CVT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CvtConfig']}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A	:       Any        =						[
	    'CVT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'CvtForImageClassification',
	    'CvtModel',
	    'CvtPreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A	:       List[str]        =						[
	    'TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFCvtForImageClassification',
	    'TFCvtModel',
	    'TFCvtPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_cvt import (
		    CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CvtForImageClassification,
		    CvtModel,
		    CvtPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_cvt import (
		    TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFCvtForImageClassification,
		    TFCvtModel,
		    TFCvtPreTrainedModel,
		)


else:
	import sys

	__A	:       str        =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





