
from typing import List, Optional, Union

import numpy as np

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


__a  			=					logging.get_logger(__name__)
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:int         =	['input_features', 'attention_mask']
	def __init__(       self  :			List[Any]       ,							SCREAMING_SNAKE_CASE_  :			Any=8_0       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=1_6_0_0_0       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=0.0       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=1_0       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=2_5       ,							SCREAMING_SNAKE_CASE_  :			str="hamming_window"       ,							SCREAMING_SNAKE_CASE_  :			Dict=3_27_68.0       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=0.97       ,							SCREAMING_SNAKE_CASE_  :			Any=1.0       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=True       ,							SCREAMING_SNAKE_CASE_  :			str=True       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=False       ,							**SCREAMING_SNAKE_CASE_  :			List[str]       ,							)       ->     Optional[int]:
					super().__init__(feature_size=SCREAMING_SNAKE_CASE_       ,							sampling_rate=SCREAMING_SNAKE_CASE_       ,							padding_value=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )

					lowercase_       =					feature_size
					lowercase_       =					sampling_rate
					lowercase_       =					padding_value
					lowercase_       =					hop_length
					lowercase_       =					win_length
					lowercase_       =					frame_signal_scale
					lowercase_       =					preemphasis_coeff
					lowercase_       =					mel_floor
					lowercase_       =					normalize_means
					lowercase_       =					normalize_vars
					lowercase_       =					win_function
					lowercase_       =					return_attention_mask

					lowercase_       =					win_length * sampling_rate // 1_0_0_0
					lowercase_       =					hop_length * sampling_rate // 1_0_0_0

					lowercase_       =					optimal_fft_length(self.sample_size       )
					lowercase_       =					(self.n_fft // 2) + 1
	def 						_lowercase  (       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			np.array       )       ->     np.ndarray:
					if self.win_function == "hamming_window":
									lowercase_       =					window_function(window_length=self.sample_size       ,							name=self.win_function       ,							periodic=SCREAMING_SNAKE_CASE_       )
					else:
									lowercase_       =					window_function(window_length=self.sample_size       ,							name=self.win_function       )

					lowercase_       =					mel_filter_bank(
					    num_frequency_bins=self.n_freqs       ,							num_mel_filters=self.feature_size       ,							min_frequency=0.0       ,							max_frequency=self.sampling_rate / 2.0       ,							sampling_rate=self.sampling_rate       ,							)

					lowercase_       =					spectrogram(
					    one_waveform * self.frame_signal_scale       ,							window=SCREAMING_SNAKE_CASE_       ,							frame_length=self.sample_size       ,							hop_length=self.sample_stride       ,							fft_length=self.n_fft       ,							center=SCREAMING_SNAKE_CASE_       ,							preemphasis=self.preemphasis_coeff       ,							mel_filters=SCREAMING_SNAKE_CASE_       ,							mel_floor=self.mel_floor       ,							log_mel='''log'''       ,							)
					return msfc_features.T
	def 						_lowercase  (       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			Any       )       ->     Optional[Any]:
					# make sure we normalize float32 arrays
					if self.normalize_means:
									lowercase_       =					x[:input_length].mean(axis=0       )
									lowercase_       =					np.subtract(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
					if self.normalize_vars:
									lowercase_       =					x[:input_length].std(axis=0       )
									lowercase_       =					np.divide(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

					if input_length < x.shape[0]:
									lowercase_       =					padding_value

					# make sure array is in float32
					lowercase_       =					x.astype(np.floataa       )

					return x
	def 						_lowercase  (       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			List[np.ndarray]       ,							SCREAMING_SNAKE_CASE_  :			Optional[np.ndarray] = None       )       ->     List[np.ndarray]:
					lowercase_       =					attention_mask.sum(-1       ) if attention_mask is not None else [x.shape[0] for x in input_features]
					return [self._normalize_one(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							self.padding_value       ) for x, n in zip(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )]




	def __call__(       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]       ,							SCREAMING_SNAKE_CASE_  :			Union[bool, str, PaddingStrategy] = False       ,							SCREAMING_SNAKE_CASE_  :			Optional[int] = None       ,							SCREAMING_SNAKE_CASE_  :			bool = False       ,							SCREAMING_SNAKE_CASE_  :			Optional[int] = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[bool] = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[Union[str, TensorType]] = None       ,							SCREAMING_SNAKE_CASE_  :			Optional[int] = None       ,							**SCREAMING_SNAKE_CASE_  :			Union[str, Any]       ,							)       ->     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.'''       )

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

					if is_batched:
									lowercase_       =					[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       ):
									lowercase_       =					np.asarray(SCREAMING_SNAKE_CASE_       ,							dtype=np.floataa       )
					elif isinstance(SCREAMING_SNAKE_CASE_       ,							np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
									lowercase_       =					raw_speech.astype(np.floataa       )

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

					# extract fbank features
					lowercase_       =					[self._extract_mfsc_features(SCREAMING_SNAKE_CASE_       ) for one_waveform in raw_speech]

					# convert into correct format for padding
					lowercase_       =					BatchFeature({'''input_features''': features}       )

					lowercase_       =					self.pad(
					    SCREAMING_SNAKE_CASE_       ,							padding=SCREAMING_SNAKE_CASE_       ,							max_length=SCREAMING_SNAKE_CASE_       ,							truncation=SCREAMING_SNAKE_CASE_       ,							pad_to_multiple_of=SCREAMING_SNAKE_CASE_       ,							return_attention_mask=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       ,							)
					# make sure list is in array format
					lowercase_       =					padded_inputs.get('''input_features'''       )
					if isinstance(input_features[0]       ,							SCREAMING_SNAKE_CASE_       ):
									lowercase_       =					[np.asarray(SCREAMING_SNAKE_CASE_       ,							dtype=np.floataa       ) for feature in input_features]

					lowercase_       =					padded_inputs.get('''attention_mask'''       )
					if attention_mask is not None:
									lowercase_       =					[np.asarray(SCREAMING_SNAKE_CASE_       ,							dtype=np.intaa       ) for array in attention_mask]

					if self.normalize_means or self.normalize_vars:
									lowercase_       =					(
									    np.array(SCREAMING_SNAKE_CASE_       ,							dtype=np.intaa       )
									    if self._get_padding_strategies(SCREAMING_SNAKE_CASE_       ,							max_length=SCREAMING_SNAKE_CASE_       ) is not PaddingStrategy.DO_NOT_PAD
									    and padding
									    else None
									)
									lowercase_       =					self.normalize(
									    padded_inputs['''input_features''']       ,							attention_mask=SCREAMING_SNAKE_CASE_       )

					if return_tensors is not None:
									lowercase_       =					padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE_       )

					return padded_inputs






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


__a  			=					logging.get_logger(__name__)
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Union[str, Any]         =	'upernet'
	def __init__(       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			Dict=None       ,							SCREAMING_SNAKE_CASE_  :			str=5_1_2       ,							SCREAMING_SNAKE_CASE_  :			Tuple=0.02       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=[1, 2, 3, 6]       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=True       ,							SCREAMING_SNAKE_CASE_  :			Tuple=0.4       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=3_8_4       ,							SCREAMING_SNAKE_CASE_  :			str=2_5_6       ,							SCREAMING_SNAKE_CASE_  :			Dict=1       ,							SCREAMING_SNAKE_CASE_  :			Tuple=False       ,							SCREAMING_SNAKE_CASE_  :			str=2_5_5       ,							**SCREAMING_SNAKE_CASE_  :			str       ,							)       ->     int:
					super().__init__(**SCREAMING_SNAKE_CASE_       )

					if backbone_config is None:
									logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''       )
									lowercase_       =					CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']       )
					elif isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ):
									lowercase_       =					backbone_config.get('''model_type'''       )
									lowercase_       =					CONFIG_MAPPING[backbone_model_type]
									lowercase_       =					config_class.from_dict(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					backbone_config
					lowercase_       =					hidden_size
					lowercase_       =					initializer_range
					lowercase_       =					pool_scales
					lowercase_       =					use_auxiliary_head
					lowercase_       =					auxiliary_loss_weight
					lowercase_       =					auxiliary_in_channels
					lowercase_       =					auxiliary_channels
					lowercase_       =					auxiliary_num_convs
					lowercase_       =					auxiliary_concat_input
					lowercase_       =					loss_ignore_index




	def 						_lowercase  (       self  :			List[str]       )       ->     List[str]:
					lowercase_       =					copy.deepcopy(self.__dict__       )
					lowercase_       =					self.backbone_config.to_dict()
					lowercase_       =					self.__class__.model_type
					return output




