
'''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


__lowerCAmelCase  							= logging.get_logger(__name__)


class     _lowerCAmelCase					(					__snake_case   ):







							'''simple docstring'''

							lowerCAmelCase_      		=		["input_values", "attention_mask"]

							def __init__(self						,						UpperCAmelCase=1						,						UpperCAmelCase=16000						,						UpperCAmelCase=128						,						UpperCAmelCase=1024						,						UpperCAmelCase=0.0						,						UpperCAmelCase=True						,						UpperCAmelCase=-4.267_7393						,						UpperCAmelCase=4.568_9974						,						UpperCAmelCase=False						,						**UpperCAmelCase						,						)  ->     List[str]:
														super().__init__(feature_size=UpperCAmelCase						,						sampling_rate=UpperCAmelCase						,						padding_value=UpperCAmelCase						,						**UpperCAmelCase						)
														_snake_case     =						num_mel_bins
														_snake_case     =						max_length
														_snake_case     =						do_normalize
														_snake_case     =						mean
														_snake_case     =						std
														_snake_case     =						return_attention_mask

							def  lowercase							(self						,						UpperCAmelCase						,						UpperCAmelCase						,						)  ->     np.ndarray:
														_snake_case     =						torch.from_numpy(UpperCAmelCase						).unsqueeze(0						)
														_snake_case     =						ta_kaldi.fbank(
														    UpperCAmelCase						,						htk_compat=UpperCAmelCase						,						sample_frequency=self.sampling_rate						,						use_energy=UpperCAmelCase						,						window_type="""hanning"""						,						num_mel_bins=self.num_mel_bins						,						dither=0.0						,						frame_shift=10						,						)

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

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

														_snake_case     =						fbank.numpy()

														return fbank

							def  lowercase							(self						,						UpperCAmelCase						)  ->     np.ndarray:
														return (input_values - (self.mean)) / (self.std * 2)





							def __call__(self						,						UpperCAmelCase						,						UpperCAmelCase = None						,						UpperCAmelCase = None						,						**UpperCAmelCase						,						)  ->     BatchFeature:

														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."""						)

														_snake_case     =						isinstance(UpperCAmelCase						,						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}"""						)
														_snake_case     =						is_batched_numpy or (
														    isinstance(UpperCAmelCase						,						(list, tuple)						) and (isinstance(raw_speech[0]						,						(np.ndarray, tuple, list)						))
														)

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

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

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

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

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

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

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

														return padded_inputs

'''simple docstring'''
from typing import TYPE_CHECKING

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


__lowerCAmelCase  							= {
    'configuration_deta': ['DETA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetaConfig'],
}

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

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase  							= [
				    'DETA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'DetaForObjectDetection',
				    'DetaModel',
				    'DetaPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_deta import DetaImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_deta import (
								    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DetaForObjectDetection,
								    DetaModel,
								    DetaPreTrainedModel,
								)

else:
				import sys

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