



"""simple docstring"""






import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

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


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



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      ['input_features', 'is_longer']

	def __init__(	self:						str			,     _SCREAMING_SNAKE_CASE:						List[str]=64			,     _SCREAMING_SNAKE_CASE:						Optional[int]=4_8000			,     _SCREAMING_SNAKE_CASE:						int=480			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=10			,     _SCREAMING_SNAKE_CASE:						Dict=1024			,     _SCREAMING_SNAKE_CASE:						Tuple=0.0			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=False			,     _SCREAMING_SNAKE_CASE:						float = 0			,     _SCREAMING_SNAKE_CASE:						float = 1_4000			,     _SCREAMING_SNAKE_CASE:						int = None			,     _SCREAMING_SNAKE_CASE:						str = "fusion"			,     _SCREAMING_SNAKE_CASE:						str = "repeatpad"			,     **_SCREAMING_SNAKE_CASE:						List[Any]			,     )							->			Tuple:

				"""simple docstring"""

				super().__init__(
				    feature_size=_SCREAMING_SNAKE_CASE			,     sampling_rate=_SCREAMING_SNAKE_CASE			,     padding_value=_SCREAMING_SNAKE_CASE			,     return_attention_mask=_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE			,     )
				__lowerCAmelCase :    Optional[int]				=			top_db
				__lowerCAmelCase :    Dict				=			truncation
				__lowerCAmelCase :    Optional[Any]				=			padding
				__lowerCAmelCase :    List[str]				=			fft_window_size
				__lowerCAmelCase :    Dict				=			(fft_window_size >> 1) + 1
				__lowerCAmelCase :    Optional[int]				=			hop_length
				__lowerCAmelCase :    str				=			max_length_s
				__lowerCAmelCase :    Tuple				=			max_length_s * sampling_rate
				__lowerCAmelCase :    str				=			sampling_rate
				__lowerCAmelCase :    Optional[Any]				=			frequency_min
				__lowerCAmelCase :    str				=			frequency_max
				__lowerCAmelCase :    List[Any]				=			mel_filter_bank(
				    num_frequency_bins=self.nb_frequency_bins			,     num_mel_filters=_SCREAMING_SNAKE_CASE			,     min_frequency=_SCREAMING_SNAKE_CASE			,     max_frequency=_SCREAMING_SNAKE_CASE			,     sampling_rate=_SCREAMING_SNAKE_CASE			,     norm=_SCREAMING_SNAKE_CASE			,     mel_scale="htk"			,     )
				__lowerCAmelCase :    Dict				=			mel_filter_bank(
				    num_frequency_bins=self.nb_frequency_bins			,     num_mel_filters=_SCREAMING_SNAKE_CASE			,     min_frequency=_SCREAMING_SNAKE_CASE			,     max_frequency=_SCREAMING_SNAKE_CASE			,     sampling_rate=_SCREAMING_SNAKE_CASE			,     norm="slaney"			,     mel_scale="slaney"			,     )

	def 				_SCREAMING_SNAKE_CASE  (	self:						Any)							->			Dict[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			copy.deepcopy(self.__dict__)
				__lowerCAmelCase :    Optional[int]				=			self.__class__.__name__
				if "mel_filters" in output:
							del output["mel_filters"]
				if "mel_filters_slaney" in output:
							del output["mel_filters_slaney"]
				return output

	def 				_SCREAMING_SNAKE_CASE  (	self:						Any			,     _SCREAMING_SNAKE_CASE:						np.array			,     _SCREAMING_SNAKE_CASE:						Optional[np.array] = None)							->			np.ndarray:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			spectrogram(
				    _SCREAMING_SNAKE_CASE			,     window_function(self.fft_window_size			,     "hann")			,     frame_length=self.fft_window_size			,     hop_length=self.hop_length			,     power=2.0			,     mel_filters=_SCREAMING_SNAKE_CASE			,     log_mel="dB"			,     )
				return log_mel_spectrogram.T

	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						List[str]			,     _SCREAMING_SNAKE_CASE:						Dict)							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			np.array_split(list(range(0			,     total_frames - chunk_frames + 1))			,     3)
				if len(ranges[1]) == 0:
							# if the audio is too short, we just use the first chunk
							__lowerCAmelCase :    Any				=			[0]
				if len(ranges[2]) == 0:
							# if the audio is too short, we just use the first chunk
							__lowerCAmelCase :    Union[str, Any]				=			[0]
				# randomly choose index for each part
				__lowerCAmelCase :    Union[str, Any]				=			np.random.choice(ranges[0])
				__lowerCAmelCase :    Optional[Any]				=			np.random.choice(ranges[1])
				__lowerCAmelCase :    Tuple				=			np.random.choice(ranges[2])

				__lowerCAmelCase :    int				=			mel[idx_front : idx_front + chunk_frames, :]
				__lowerCAmelCase :    Dict				=			mel[idx_middle : idx_middle + chunk_frames, :]
				__lowerCAmelCase :    int				=			mel[idx_back : idx_back + chunk_frames, :]

				__lowerCAmelCase :    Optional[int]				=			torch.tensor(mel[None, None, :])
				__lowerCAmelCase :    List[str]				=			torch.nn.functional.interpolate(
				    _SCREAMING_SNAKE_CASE			,     size=[chunk_frames, 64]			,     mode="bilinear"			,     align_corners=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    int				=			mel_shrink[0][0].numpy()
				__lowerCAmelCase :    Tuple				=			np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]			,     axis=0)
				return mel_fusion

	def 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						np.array			,     _SCREAMING_SNAKE_CASE:						List[Any]			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			np.array:

				"""simple docstring"""

				if waveform.shape[0] > max_length:
							if truncation == "rand_trunc":
										__lowerCAmelCase :    Optional[Any]				=			True
										# random crop to max_length (for compatibility) -> this should be handled by self.pad
										__lowerCAmelCase :    str				=			len(_SCREAMING_SNAKE_CASE) - max_length
										__lowerCAmelCase :    str				=			np.random.randint(0			,     overflow + 1)
										__lowerCAmelCase :    Dict				=			waveform[idx : idx + max_length]
										__lowerCAmelCase :    Any				=			self._np_extract_fbank_features(_SCREAMING_SNAKE_CASE			,     self.mel_filters_slaney)[None, :]
							elif truncation == "fusion":
										__lowerCAmelCase :    Dict				=			self._np_extract_fbank_features(_SCREAMING_SNAKE_CASE			,     self.mel_filters)
										__lowerCAmelCase :    Dict				=			max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
										__lowerCAmelCase :    Dict				=			mel.shape[0]
										if chunk_frames == total_frames:
													# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
													# In this case, we just use the whole audio.
													__lowerCAmelCase :    Optional[Any]				=			np.stack([mel, mel, mel, mel]			,     axis=0)
													__lowerCAmelCase :    Union[str, Any]				=			False
										else:
													__lowerCAmelCase :    Tuple				=			self._random_mel_fusion(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
													__lowerCAmelCase :    Optional[Any]				=			True
							else:
										raise NotImplementedError(F"""data_truncating {truncation} not implemented""")

				else:
							__lowerCAmelCase :    Optional[Any]				=			False
							# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
							if waveform.shape[0] < max_length:
										if padding == "repeat":
													__lowerCAmelCase :    Union[str, Any]				=			int(max_length / len(_SCREAMING_SNAKE_CASE))
													__lowerCAmelCase :    Tuple				=			np.stack(np.tile(_SCREAMING_SNAKE_CASE			,     n_repeat + 1))[:max_length]
										if padding == "repeatpad":
													__lowerCAmelCase :    Any				=			int(max_length / len(_SCREAMING_SNAKE_CASE))
													__lowerCAmelCase :    Tuple				=			np.stack(np.tile(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE))
										__lowerCAmelCase :    Dict				=			np.pad(_SCREAMING_SNAKE_CASE			,     (0, max_length - waveform.shape[0])			,     mode="constant"			,     constant_values=0)

							if truncation == "fusion":
										__lowerCAmelCase :    Optional[int]				=			self._np_extract_fbank_features(_SCREAMING_SNAKE_CASE			,     self.mel_filters)
										__lowerCAmelCase :    Dict				=			np.stack([input_mel, input_mel, input_mel, input_mel]			,     axis=0)
							else:
										__lowerCAmelCase :    Tuple				=			self._np_extract_fbank_features(_SCREAMING_SNAKE_CASE			,     self.mel_filters_slaney)[None, :]

				return input_mel, longer







	def __call__(	self:						Any			,     _SCREAMING_SNAKE_CASE:						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,     _SCREAMING_SNAKE_CASE:						str = None			,     _SCREAMING_SNAKE_CASE:						Optional[str] = None			,     _SCREAMING_SNAKE_CASE:						Optional[int] = None			,     _SCREAMING_SNAKE_CASE:						Optional[int] = None			,     _SCREAMING_SNAKE_CASE:						Optional[Union[str, TensorType]] = None			,     **_SCREAMING_SNAKE_CASE:						Any			,     )							->			BatchFeature:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			truncation if truncation is not None else self.truncation
				__lowerCAmelCase :    Tuple				=			padding if padding else self.padding

				if sampling_rate is not None:
							if sampling_rate != self.sampling_rate:
										raise ValueError(
										    F"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
										    F""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
										    F""" was sampled 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.")

				__lowerCAmelCase :    int				=			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}""")
				__lowerCAmelCase :    Optional[Any]				=			is_batched_numpy or (
				    isinstance(_SCREAMING_SNAKE_CASE			,     (list, tuple)) and (isinstance(raw_speech[0]			,     (np.ndarray, tuple, list)))
				)

				if is_batched:
							__lowerCAmelCase :    List[str]				=			[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):
							__lowerCAmelCase :    Dict				=			np.asarray(_SCREAMING_SNAKE_CASE			,     dtype=np.floataa)
				elif isinstance(_SCREAMING_SNAKE_CASE			,     np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
							__lowerCAmelCase :    Union[str, Any]				=			raw_speech.astype(np.floataa)

				# always return batch
				if not is_batched:
							__lowerCAmelCase :    str				=			[np.asarray(_SCREAMING_SNAKE_CASE)]

				# convert to mel spectrogram, truncate and pad if needed.
				__lowerCAmelCase :    Optional[int]				=			[
				    self._get_input_mel(_SCREAMING_SNAKE_CASE			,     max_length if max_length else self.nb_max_samples			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				    for waveform in raw_speech
				]

				__lowerCAmelCase :    Optional[int]				=			[]
				__lowerCAmelCase :    List[Any]				=			[]
				for mel, longer in padded_inputs:
							input_mel.append(_SCREAMING_SNAKE_CASE)
							is_longer.append(_SCREAMING_SNAKE_CASE)

				if truncation == "fusion" and sum(_SCREAMING_SNAKE_CASE) == 0:
							# if no audio is longer than 10s, then randomly select one audio to be longer
							__lowerCAmelCase :    int				=			np.random.randint(0			,     len(_SCREAMING_SNAKE_CASE))
							__lowerCAmelCase :    int				=			True

				if isinstance(input_mel[0]			,     _SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    Optional[int]				=			[np.asarray(_SCREAMING_SNAKE_CASE			,     dtype=np.floataa) for feature in input_mel]

				# is_longer is a list of bool
				__lowerCAmelCase :    str				=			[[longer] for longer in is_longer]

				__lowerCAmelCase :    List[str]				=			{"input_features": input_mel, "is_longer": is_longer}
				__lowerCAmelCase :    Dict				=			BatchFeature(_SCREAMING_SNAKE_CASE)

				if return_tensors is not None:
							__lowerCAmelCase :    Dict				=			input_features.convert_to_tensors(_SCREAMING_SNAKE_CASE)

				return input_features




"""simple docstring"""






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case				:  Tuple =       {
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:  Optional[Any] =       [
	    'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'AutoformerForPrediction',
	    'AutoformerModel',
	    'AutoformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_autoformer import (
	    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    AutoformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_autoformer import (
		    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AutoformerForPrediction,
		    AutoformerModel,
		    AutoformerPreTrainedModel,
		)

else:
	import sys

	__snake_case				:  Optional[int] =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)