






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


lowercase  							= logging.get_logger(__name__)




class 			UpperCamelCase_				(      SCREAMING_SNAKE_CASE__    ):



	'''simple docstring'''




	lowerCAmelCase													=			['input_values', 'attention_mask']




	def __init__(	self	,      a=1	,      a=1_60_00	,      a=1_28	,      a=10_24	,      a=0.0	,      a=True	,      a=-4.2_677_393	,      a=4.5_689_974	,      a=False	,      **a	,      )	->   Any:
								super().__init__(feature_size=a	,      sampling_rate=a	,      padding_value=a	,      **a )
								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 							_UpperCamelCase      (	self	,      a	,      a	,      )	->   Any:
								snake_case_           =							torch.from_numpy(a ).unsqueeze(0 )
								snake_case_           =							ta_kaldi.fbank(
								    a	,      htk_compat=a	,      sample_frequency=self.sampling_rate	,      use_energy=a	,      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(a )
								elif difference < 0:
															snake_case_           =							fbank[0:max_length, :]

								snake_case_           =							fbank.numpy()

								return fbank




	def 							_UpperCamelCase      (	self	,      a )	->   List[str]:
								return (input_values - (self.mean)) / (self.std * 2)






	def __call__(	self	,      a	,      a = None	,      a = None	,      **a	,      )	->   Optional[Any]:

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

								if is_batched:
															snake_case_           =							[np.asarray(a	,      dtype=np.floataa ) for speech in raw_speech]
								elif not is_batched and not isinstance(a	,      np.ndarray ):
															snake_case_           =							np.asarray(a	,      dtype=np.floataa )
								elif isinstance(a	,      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(a	,      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]	,      a ):
															snake_case_           =							[np.asarray(a	,      dtype=np.floataa ) for feature in input_values]

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

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

								return padded_inputs













from typing import TYPE_CHECKING

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


lowercase  							= {
    "configuration_deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							= ["DeformableDetrFeatureExtractor"]
	lowercase  							= ["DeformableDetrImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							= [
	    "DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "DeformableDetrForObjectDetection",
	    "DeformableDetrModel",
	    "DeformableDetrPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
		from .image_processing_deformable_detr import DeformableDetrImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_deformable_detr import (
		    DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DeformableDetrForObjectDetection,
		    DeformableDetrModel,
		    DeformableDetrPreTrainedModel,
		)

else:
	import sys

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





