

from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			["audio_values", "audio_mask"]


						def __init__(       self						,					A_=2048						,					A_=1						,					A_=[16, 16]						,					A_=128						,					A_=44100						,					A_=86						,					A_=2048						,					A_=0.0						,					**A_						,					):







										'''simple docstring'''


										super().__init__(
										    feature_size=A_						,					sampling_rate=A_						,					padding_value=A_						,					**A_						,					)

										_UpperCAmelCase       :		Any 		=    spectrogram_length
										_UpperCAmelCase       :		int 		=    num_channels
										_UpperCAmelCase       :		List[str] 		=    patch_size
										_UpperCAmelCase       :		Optional[int] 		=    feature_size // self.patch_size[1]
										_UpperCAmelCase       :		Optional[Any] 		=    n_fft
										_UpperCAmelCase       :		Optional[Any] 		=    sampling_rate // hop_length_to_sampling_rate
										_UpperCAmelCase       :		Any 		=    sampling_rate
										_UpperCAmelCase       :		List[str] 		=    padding_value
										_UpperCAmelCase       :		str 		=    mel_filter_bank(
										    num_frequency_bins=1 + n_fft // 2						,					num_mel_filters=A_						,					min_frequency=0.0						,					max_frequency=22050.0						,					sampling_rate=A_						,					norm="slaney"						,					mel_scale="slaney"						,					).T


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		str 		=    spectrogram(
										    A_						,					window_function(self.n_fft						,					"hann"	)						,					frame_length=self.n_fft						,					hop_length=self.hop_length						,					power=2.0						,					mel_filters=self.mel_filters.T						,					log_mel="dB"						,					db_range=80.0						,					)
										_UpperCAmelCase       :		Optional[int] 		=    log_spec[:, :-1]
										_UpperCAmelCase       :		List[Any] 		=    log_spec - 20.0
										_UpperCAmelCase       :		List[str] 		=    np.clip(log_spec / 40.0						,					-2.0						,					0.0	) + 1.0
										return log_spec





						def __call__(       self						,					A_						,					A_ = None						,					A_ = True						,					A_ = None						,					A_ = False						,					A_ = False						,					**A_						,					):







										'''simple docstring'''


										if sampling_rate is not None:
														if sampling_rate != self.sampling_rate:
																		raise ValueError(
																		    "This feature extractor is set to support sampling rate"
																		    f' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'
																		    f' with {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."	)

										_UpperCAmelCase       :		Optional[int] 		=    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}'	)
										_UpperCAmelCase       :		Optional[Any] 		=    is_batched_numpy or (
										    isinstance(A_						,					(list, tuple)	) and (isinstance(raw_speech[0]						,					(np.ndarray, tuple, list)	))
										)
										if is_batched:
														_UpperCAmelCase       :		List[Any] 		=    [np.asarray([speech]						,					dtype=np.floataa	).T for speech in raw_speech]
										elif not is_batched and not isinstance(A_						,					np.ndarray	):
														_UpperCAmelCase       :		Optional[Any] 		=    np.asarray(A_						,					dtype=np.floataa	)
										elif isinstance(A_						,					np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
														_UpperCAmelCase       :		Optional[int] 		=    raw_speech.astype(np.floataa	)
										# always return batch
										if not is_batched:
														_UpperCAmelCase       :		List[Any] 		=    [np.asarray([raw_speech]	).T]

										# Convert audio signals to log mel spectrograms, truncate by time axis
										_UpperCAmelCase       :		str 		=    [
										    self._np_extract_fbank_features(waveform.squeeze()	).T[: self.spectrogram_length] for waveform in raw_speech
										]
										if isinstance(audio_features[0]						,					A_	):
														_UpperCAmelCase       :		int 		=    [np.asarray(A_						,					dtype=np.floataa	) for feature in audio_features]

										# Create audio attention mask
										_UpperCAmelCase       :		Optional[Any] 		=    max(
										    [ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len for feature in audio_features]	)  # The maximum number of audio patches in a batch
										if return_attention_mask:
														_UpperCAmelCase       :		str 		=    [
														    (ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len) * [1]
														    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len) * [0]
														    for feature in audio_features
														]
														_UpperCAmelCase       :		int 		=    np.array(A_	).astype(np.floataa	)

										# convert into correct format for padding
										_UpperCAmelCase       :		Tuple 		=    max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
										_UpperCAmelCase       :		Tuple 		=    np.ones([len(A_	), 1, max_time_len, self.feature_size]	).astype(np.floataa	)
										_UpperCAmelCase       :		List[Any] 		=    padded_audio_features * self.padding_value
										for i in range(len(A_	)	):
														_UpperCAmelCase       :		str 		=    audio_features[i]
														_UpperCAmelCase       :		Optional[int] 		=    feature

										# return as BatchFeature
										if return_attention_mask:
														_UpperCAmelCase       :		Tuple 		=    {"audio_values": padded_audio_features, "audio_mask": audio_mask}
										else:
														_UpperCAmelCase       :		Tuple 		=    {"audio_values": padded_audio_features}

										_UpperCAmelCase       :		Tuple 		=    BatchFeature(data=A_						,					tensor_type=A_	)
										return encoded_inputs





import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

class 				a  (	UpperCAmelCase				):


						def __init__(       self						,					*A_						,					**A_	):







										'''simple docstring'''


										warnings.warn(
										    "The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
										    " Please use DeformableDetrImageProcessor instead."						,					A_						,					)
										super().__init__(*A_						,					**A_	)


