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


__lowercase										= logging.get_logger(__name__)


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						Any      =				["""input_features""", """is_longer"""]







    def __init__(     self			,			__lowercase=64			,			__lowercase=48_000			,			__lowercase=480			,			__lowercase=10			,			__lowercase=1_024			,			__lowercase=0.0			,			__lowercase=False			,			__lowercase = 0			,			__lowercase = 14_000			,			__lowercase = None			,			__lowercase = "fusion"			,			__lowercase = "repeatpad"			,			**__lowercase			,			)	->     int:
        super().__init__(
            feature_size=__lowercase			,			sampling_rate=__lowercase			,			padding_value=__lowercase			,			return_attention_mask=__lowercase			,			**__lowercase			,			)
        __UpperCamelCase					:Union[str, Any]        =		top_db
        __UpperCamelCase					:Union[str, Any]        =		truncation
        __UpperCamelCase					:List[str]        =		padding
        __UpperCamelCase					:Dict        =		fft_window_size
        __UpperCamelCase					:str        =		(fft_window_size >> 1) + 1
        __UpperCamelCase					:Union[str, Any]        =		hop_length
        __UpperCamelCase					:List[Any]        =		max_length_s
        __UpperCamelCase					:Union[str, Any]        =		max_length_s * sampling_rate
        __UpperCamelCase					:Any        =		sampling_rate
        __UpperCamelCase					:Tuple        =		frequency_min
        __UpperCamelCase					:str        =		frequency_max
        __UpperCamelCase					:Optional[Any]        =		mel_filter_bank(
            num_frequency_bins=self.nb_frequency_bins			,			num_mel_filters=__lowercase			,			min_frequency=__lowercase			,			max_frequency=__lowercase			,			sampling_rate=__lowercase			,			norm=__lowercase			,			mel_scale='''htk'''			,			)
        __UpperCamelCase					:str        =		mel_filter_bank(
            num_frequency_bins=self.nb_frequency_bins			,			num_mel_filters=__lowercase			,			min_frequency=__lowercase			,			max_frequency=__lowercase			,			sampling_rate=__lowercase			,			norm='''slaney'''			,			mel_scale='''slaney'''			,			)







    def       UpperCamelCase__       (     self)	->     Dict[str, Any]:
        __UpperCamelCase					:str        =		copy.deepcopy(self.__dict__)
        __UpperCamelCase					: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       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None)	->     np.ndarray:
        __UpperCamelCase					:Optional[Any]        =		spectrogram(
            __lowercase			,			window_function(self.fft_window_size			,			'''hann''')			,			frame_length=self.fft_window_size			,			hop_length=self.hop_length			,			power=2.0			,			mel_filters=__lowercase			,			log_mel='''dB'''			,			)
        return log_mel_spectrogram.T







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase)	->     List[str]:
        __UpperCamelCase					:Union[str, 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
            __UpperCamelCase					:Union[str, Any]        =		[0]
        if len(ranges[2]) == 0:
            # if the audio is too short, we just use the first chunk
            __UpperCamelCase					:int        =		[0]
        # randomly choose index for each part
        __UpperCamelCase					:str        =		np.random.choice(ranges[0])
        __UpperCamelCase					:Optional[Any]        =		np.random.choice(ranges[1])
        __UpperCamelCase					:List[Any]        =		np.random.choice(ranges[2])

        __UpperCamelCase					:Tuple        =		mel[idx_front : idx_front + chunk_frames, :]
        __UpperCamelCase					:List[Any]        =		mel[idx_middle : idx_middle + chunk_frames, :]
        __UpperCamelCase					:Any        =		mel[idx_back : idx_back + chunk_frames, :]

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







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

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

            if truncation == "fusion":
                __UpperCamelCase					:List[str]        =		self._np_extract_fbank_features(__lowercase			,			self.mel_filters)
                __UpperCamelCase					:Optional[Any]        =		np.stack([input_mel, input_mel, input_mel, input_mel]			,			axis=0)
            else:
                __UpperCamelCase					:str        =		self._np_extract_fbank_features(__lowercase			,			self.mel_filters_slaney)[None, :]

        return input_mel, longer







    def __call__(     self			,			__lowercase			,			__lowercase = None			,			__lowercase = None			,			__lowercase = None			,			__lowercase = None			,			__lowercase = None			,			**__lowercase			,			)	->     BatchFeature:
        __UpperCamelCase					:Optional[int]        =		truncation if truncation is not None else self.truncation
        __UpperCamelCase					:str        =		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.''')

        __UpperCamelCase					:Optional[Any]        =		isinstance(__lowercase			,			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(__lowercase			,			(list, tuple)) and (isinstance(raw_speech[0]			,			(np.ndarray, tuple, list)))
        )

        if is_batched:
            __UpperCamelCase					:List[Any]        =		[np.asarray(__lowercase			,			dtype=np.floataa) for speech in raw_speech]
        elif not is_batched and not isinstance(__lowercase			,			np.ndarray):
            __UpperCamelCase					:Dict        =		np.asarray(__lowercase			,			dtype=np.floataa)
        elif isinstance(__lowercase			,			np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
            __UpperCamelCase					:Union[str, Any]        =		raw_speech.astype(np.floataa)

        # always return batch
        if not is_batched:
            __UpperCamelCase					:List[Any]        =		[np.asarray(__lowercase)]

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

        __UpperCamelCase					:List[Any]        =		[]
        __UpperCamelCase					:str        =		[]
        for mel, longer in padded_inputs:
            input_mel.append(__lowercase)
            is_longer.append(__lowercase)

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

        if isinstance(input_mel[0]			,			__lowercase):
            __UpperCamelCase					:Dict        =		[np.asarray(__lowercase			,			dtype=np.floataa) for feature in input_mel]

        # is_longer is a list of bool
        __UpperCamelCase					:Dict        =		[[longer] for longer in is_longer]

        __UpperCamelCase					:Optional[Any]        =		{'''input_features''': input_mel, '''is_longer''': is_longer}
        __UpperCamelCase					:Dict        =		BatchFeature(__lowercase)

        if return_tensors is not None:
            __UpperCamelCase					:int        =		input_features.convert_to_tensors(__lowercase)

        return input_features






from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
    if not (is_transformers_available() and is_torch_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
    from .notes_encoder import SpectrogramNotesEncoder
    from .continous_encoder import SpectrogramContEncoder
    from .pipeline_spectrogram_diffusion import (
        SpectrogramContEncoder,
        SpectrogramDiffusionPipeline,
        TaFilmDecoder,
    )

try:
    if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
    from .midi_utils import MidiProcessor





