





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


_A					:				Tuple				=			logging.get_logger(__name__)



class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		List[Any]				=	["""input_features""", """is_longer"""]



    def __init__(       self   ,				A_=64   ,				A_=4_80_00   ,				A_=4_80   ,				A_=10   ,				A_=10_24   ,				A_=0.0   ,				A_=False   ,				A_ = 0   ,				A_ = 1_40_00   ,				A_ = None   ,				A_ = "fusion"   ,				A_ = "repeatpad"   ,				**A_   ,				):





        '''simple docstring'''

        super().__init__(
            feature_size=A_   ,				sampling_rate=A_   ,				padding_value=A_   ,				return_attention_mask=A_   ,				**A_   ,				)
        SCREAMING_SNAKE_CASE__											=			top_db
        SCREAMING_SNAKE_CASE__											=			truncation
        SCREAMING_SNAKE_CASE__											=			padding
        SCREAMING_SNAKE_CASE__											=			fft_window_size
        SCREAMING_SNAKE_CASE__											=			(fft_window_size >> 1) + 1
        SCREAMING_SNAKE_CASE__											=			hop_length
        SCREAMING_SNAKE_CASE__											=			max_length_s
        SCREAMING_SNAKE_CASE__											=			max_length_s * sampling_rate
        SCREAMING_SNAKE_CASE__											=			sampling_rate
        SCREAMING_SNAKE_CASE__											=			frequency_min
        SCREAMING_SNAKE_CASE__											=			frequency_max
        SCREAMING_SNAKE_CASE__											=			mel_filter_bank(
            num_frequency_bins=self.nb_frequency_bins   ,				num_mel_filters=A_   ,				min_frequency=A_   ,				max_frequency=A_   ,				sampling_rate=A_   ,				norm=A_   ,				mel_scale='''htk'''   ,				)
        SCREAMING_SNAKE_CASE__											=			mel_filter_bank(
            num_frequency_bins=self.nb_frequency_bins   ,				num_mel_filters=A_   ,				min_frequency=A_   ,				max_frequency=A_   ,				sampling_rate=A_   ,				norm='''slaney'''   ,				mel_scale='''slaney'''   ,				)



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			copy.deepcopy(self.__dict__     )
        SCREAMING_SNAKE_CASE__											=			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  lowercase_					(       self   ,				A_   ,				A_ = None     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			spectrogram(
            A_   ,				window_function(self.fft_window_size   ,				'''hann'''     )   ,				frame_length=self.fft_window_size   ,				hop_length=self.hop_length   ,				power=2.0   ,				mel_filters=A_   ,				log_mel='''dB'''   ,				)
        return log_mel_spectrogram.T



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			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
            SCREAMING_SNAKE_CASE__											=			[0]
        if len(ranges[2]     ) == 0:
            # if the audio is too short, we just use the first chunk
            SCREAMING_SNAKE_CASE__											=			[0]
        # randomly choose index for each part
        SCREAMING_SNAKE_CASE__											=			np.random.choice(ranges[0]     )
        SCREAMING_SNAKE_CASE__											=			np.random.choice(ranges[1]     )
        SCREAMING_SNAKE_CASE__											=			np.random.choice(ranges[2]     )

        SCREAMING_SNAKE_CASE__											=			mel[idx_front : idx_front + chunk_frames, :]
        SCREAMING_SNAKE_CASE__											=			mel[idx_middle : idx_middle + chunk_frames, :]
        SCREAMING_SNAKE_CASE__											=			mel[idx_back : idx_back + chunk_frames, :]

        SCREAMING_SNAKE_CASE__											=			torch.tensor(mel[None, None, :]     )
        SCREAMING_SNAKE_CASE__											=			torch.nn.functional.interpolate(
            A_   ,				size=[chunk_frames, 64]   ,				mode='''bilinear'''   ,				align_corners=A_     )
        SCREAMING_SNAKE_CASE__											=			mel_shrink[0][0].numpy()
        SCREAMING_SNAKE_CASE__											=			np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]   ,				axis=0     )
        return mel_fusion



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        if waveform.shape[0] > max_length:
            if truncation == "rand_trunc":
                SCREAMING_SNAKE_CASE__											=			True
                # random crop to max_length (for compatibility) -> this should be handled by self.pad
                SCREAMING_SNAKE_CASE__											=			len(A_     ) - max_length
                SCREAMING_SNAKE_CASE__											=			np.random.randint(0   ,				overflow + 1     )
                SCREAMING_SNAKE_CASE__											=			waveform[idx : idx + max_length]
                SCREAMING_SNAKE_CASE__											=			self._np_extract_fbank_features(A_   ,				self.mel_filters_slaney     )[None, :]
            elif truncation == "fusion":
                SCREAMING_SNAKE_CASE__											=			self._np_extract_fbank_features(A_   ,				self.mel_filters     )
                SCREAMING_SNAKE_CASE__											=			max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                SCREAMING_SNAKE_CASE__											=			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.
                    SCREAMING_SNAKE_CASE__											=			np.stack([mel, mel, mel, mel]   ,				axis=0     )
                    SCREAMING_SNAKE_CASE__											=			False
                else:
                    SCREAMING_SNAKE_CASE__											=			self._random_mel_fusion(A_   ,				A_   ,				A_     )
                    SCREAMING_SNAKE_CASE__											=			True
            else:
                raise NotImplementedError(f'''data_truncating {truncation} not implemented'''     )

        else:
            SCREAMING_SNAKE_CASE__											=			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":
                    SCREAMING_SNAKE_CASE__											=			int(max_length / len(A_     )     )
                    SCREAMING_SNAKE_CASE__											=			np.stack(np.tile(A_   ,				n_repeat + 1     )     )[:max_length]
                if padding == "repeatpad":
                    SCREAMING_SNAKE_CASE__											=			int(max_length / len(A_     )     )
                    SCREAMING_SNAKE_CASE__											=			np.stack(np.tile(A_   ,				A_     )     )
                SCREAMING_SNAKE_CASE__											=			np.pad(A_   ,				(0, max_length - waveform.shape[0])   ,				mode='''constant'''   ,				constant_values=0     )

            if truncation == "fusion":
                SCREAMING_SNAKE_CASE__											=			self._np_extract_fbank_features(A_   ,				self.mel_filters     )
                SCREAMING_SNAKE_CASE__											=			np.stack([input_mel, input_mel, input_mel, input_mel]   ,				axis=0     )
            else:
                SCREAMING_SNAKE_CASE__											=			self._np_extract_fbank_features(A_   ,				self.mel_filters_slaney     )[None, :]

        return input_mel, longer




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





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			truncation if truncation is not None else self.truncation
        SCREAMING_SNAKE_CASE__											=			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.'''     )

        SCREAMING_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}'''     )
        SCREAMING_SNAKE_CASE__											=			is_batched_numpy or (
            isinstance(A_   ,				(list, tuple)     ) and (isinstance(raw_speech[0]   ,				(np.ndarray, tuple, list)     ))
        )

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

        # always return batch
        if not is_batched:
            SCREAMING_SNAKE_CASE__											=			[np.asarray(A_     )]

        # convert to mel spectrogram, truncate and pad if needed.
        SCREAMING_SNAKE_CASE__											=			[
            self._get_input_mel(A_   ,				max_length if max_length else self.nb_max_samples   ,				A_   ,				A_     )
            for waveform in raw_speech
        ]

        SCREAMING_SNAKE_CASE__											=			[]
        SCREAMING_SNAKE_CASE__											=			[]
        for mel, longer in padded_inputs:
            input_mel.append(A_     )
            is_longer.append(A_     )

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

        if isinstance(input_mel[0]   ,				A_     ):
            SCREAMING_SNAKE_CASE__											=			[np.asarray(A_   ,				dtype=np.floataa     ) for feature in input_mel]

        # is_longer is a list of bool
        SCREAMING_SNAKE_CASE__											=			[[longer] for longer in is_longer]

        SCREAMING_SNAKE_CASE__											=			{'''input_features''': input_mel, '''is_longer''': is_longer}
        SCREAMING_SNAKE_CASE__											=			BatchFeature(A_     )

        if return_tensors is not None:
            SCREAMING_SNAKE_CASE__											=			input_features.convert_to_tensors(A_     )

        return input_features







from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate



@dataclass(frozen=__SCREAMING_SNAKE_CASE    )
class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		str				=	field(default="""summarization"""			,     metadata={"""include_in_asdict_even_if_is_default""": True}    )
    lowerCamelCase__    :		ClassVar[Features]				=	Features({"""text""": Value("""string"""    )}    )
    lowerCamelCase__    :		ClassVar[Features]				=	Features({"""summary""": Value("""string"""    )}    )
    lowerCamelCase__    :		str				=	"text"
    lowerCamelCase__    :		str				=	"summary"



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return {self.text_column: "text", self.summary_column: "summary"}
