

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


_lowerCAmelCase  :      Optional[int]			 =logging.get_logger(__name__)

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   ["input_features", "is_longer"]





					def __init__(				self				,      lowerCamelCase__=6_4				,      lowerCamelCase__=4_8_0_0_0				,      lowerCamelCase__=4_8_0				,      lowerCamelCase__=1_0				,      lowerCamelCase__=1_0_2_4				,      lowerCamelCase__=0.0				,      lowerCamelCase__=False				,      lowerCamelCase__ = 0				,      lowerCamelCase__ = 1_4_0_0_0				,      lowerCamelCase__ = None				,      lowerCamelCase__ = "fusion"				,      lowerCamelCase__ = "repeatpad"				,      **lowerCamelCase__				,      ):
						super().__init__(
						    feature_size=lowerCamelCase__				,      sampling_rate=lowerCamelCase__				,      padding_value=lowerCamelCase__				,      return_attention_mask=lowerCamelCase__				,      **lowerCamelCase__				,      )
						UpperCAmelCase__:	str									= top_db
						UpperCAmelCase__:	Optional[int]									= truncation
						UpperCAmelCase__:	Any									= padding
						UpperCAmelCase__:	Tuple									= fft_window_size
						UpperCAmelCase__:	List[Any]									= (fft_window_size >> 1) + 1
						UpperCAmelCase__:	Union[str, Any]									= hop_length
						UpperCAmelCase__:	Dict									= max_length_s
						UpperCAmelCase__:	str									= max_length_s * sampling_rate
						UpperCAmelCase__:	Any									= sampling_rate
						UpperCAmelCase__:	List[Any]									= frequency_min
						UpperCAmelCase__:	List[Any]									= frequency_max
						UpperCAmelCase__:	Dict									= mel_filter_bank(
						    num_frequency_bins=self.nb_frequency_bins				,      num_mel_filters=lowerCamelCase__				,      min_frequency=lowerCamelCase__				,      max_frequency=lowerCamelCase__				,      sampling_rate=lowerCamelCase__				,      norm=lowerCamelCase__				,      mel_scale="htk"				,      )
						UpperCAmelCase__:	Optional[Any]									= mel_filter_bank(
						    num_frequency_bins=self.nb_frequency_bins				,      num_mel_filters=lowerCamelCase__				,      min_frequency=lowerCamelCase__				,      max_frequency=lowerCamelCase__				,      sampling_rate=lowerCamelCase__				,      norm="slaney"				,      mel_scale="slaney"				,      )





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Union[str, Any]									= copy.deepcopy(self.__dict__						)
						UpperCAmelCase__:	Any									= 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				,      lowerCamelCase__				,      lowerCamelCase__ = None						):
						UpperCAmelCase__:	int									= spectrogram(
						    lowerCamelCase__				,      window_function(self.fft_window_size				,      "hann"						)				,      frame_length=self.fft_window_size				,      hop_length=self.hop_length				,      power=2.0				,      mel_filters=lowerCamelCase__				,      log_mel="dB"				,      )
						return log_mel_spectrogram.T





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						UpperCAmelCase__:	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__:	Optional[Any]									= [0]
						if len(ranges[2]						) == 0:
							# if the audio is too short, we just use the first chunk
							UpperCAmelCase__:	List[Any]									= [0]
						# randomly choose index for each part
						UpperCAmelCase__:	str									= np.random.choice(ranges[0]						)
						UpperCAmelCase__:	Tuple									= np.random.choice(ranges[1]						)
						UpperCAmelCase__:	str									= np.random.choice(ranges[2]						)

						UpperCAmelCase__:	Dict									= mel[idx_front : idx_front + chunk_frames, :]
						UpperCAmelCase__:	Dict									= mel[idx_middle : idx_middle + chunk_frames, :]
						UpperCAmelCase__:	Optional[Any]									= mel[idx_back : idx_back + chunk_frames, :]

						UpperCAmelCase__:	Dict									= torch.tensor(mel[None, None, :]						)
						UpperCAmelCase__:	Optional[Any]									= torch.nn.functional.interpolate(
						    lowerCamelCase__				,      size=[chunk_frames, 6_4]				,      mode="bilinear"				,      align_corners=lowerCamelCase__						)
						UpperCAmelCase__:	str									= mel_shrink[0][0].numpy()
						UpperCAmelCase__:	int									= np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]				,      axis=0						)
						return mel_fusion





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

						else:
							UpperCAmelCase__:	List[str]									= 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__:	List[str]									= int(max_length / len(lowerCamelCase__						)						)
									UpperCAmelCase__:	Optional[int]									= np.stack(np.tile(lowerCamelCase__				,      n_repeat + 1						)						)[:max_length]
								if padding == "repeatpad":
									UpperCAmelCase__:	Dict									= int(max_length / len(lowerCamelCase__						)						)
									UpperCAmelCase__:	Dict									= np.stack(np.tile(lowerCamelCase__				,      lowerCamelCase__						)						)
								UpperCAmelCase__:	str									= np.pad(lowerCamelCase__				,      (0, max_length - waveform.shape[0])				,      mode="constant"				,      constant_values=0						)

							if truncation == "fusion":
								UpperCAmelCase__:	int									= self._np_extract_fbank_features(lowerCamelCase__				,      self.mel_filters						)
								UpperCAmelCase__:	Union[str, Any]									= np.stack([input_mel, input_mel, input_mel, input_mel]				,      axis=0						)
							else:
								UpperCAmelCase__:	Optional[Any]									= self._np_extract_fbank_features(lowerCamelCase__				,      self.mel_filters_slaney						)[None, :]

						return input_mel, longer





					def __call__(				self				,      lowerCamelCase__				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      **lowerCamelCase__				,      ):
						UpperCAmelCase__:	Optional[int]									= truncation if truncation is not None else self.truncation
						UpperCAmelCase__:	List[Any]									= 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__:	str									= isinstance(lowerCamelCase__				,      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(lowerCamelCase__				,      (list, tuple)						) and (isinstance(raw_speech[0]				,      (np.ndarray, tuple, list)						))
						)

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

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

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

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

						if truncation == "fusion" and sum(lowerCamelCase__						) == 0:
							# if no audio is longer than 10s, then randomly select one audio to be longer
							UpperCAmelCase__:	List[str]									= np.random.randint(0				,      len(lowerCamelCase__						)						)
							UpperCAmelCase__:	Any									= True

						if isinstance(input_mel[0]				,      lowerCamelCase__						):
							UpperCAmelCase__:	Tuple									= [np.asarray(lowerCamelCase__				,      dtype=np.floataa						) for feature in input_mel]

						# is_longer is a list of bool
						UpperCAmelCase__:	Optional[Any]									= [[longer] for longer in is_longer]

						UpperCAmelCase__:	Tuple									= {"input_features": input_mel, "is_longer": is_longer}
						UpperCAmelCase__:	Any									= BatchFeature(lowerCamelCase__						)

						if return_tensors is not None:
							UpperCAmelCase__:	List[Any]									= input_features.convert_to_tensors(lowerCamelCase__						)

						return input_features


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase  :      Union[str, Any]			 ={
    """configuration_clap""": [
        """CLAP_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ClapAudioConfig""",
        """ClapConfig""",
        """ClapTextConfig""",
    ],
    """processing_clap""": ["""ClapProcessor"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCAmelCase  :      Union[str, Any]			 =[
					    """CLAP_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """ClapModel""",
					    """ClapPreTrainedModel""",
					    """ClapTextModel""",
					    """ClapTextModelWithProjection""",
					    """ClapAudioModel""",
					    """ClapAudioModelWithProjection""",
					]
					_lowerCAmelCase  :      List[str]			 =["""ClapFeatureExtractor"""]

if TYPE_CHECKING:
					from .configuration_clap import (
					    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
					    ClapAudioConfig,
					    ClapConfig,
					    ClapTextConfig,
					)
					from .processing_clap import ClapProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_clap import ClapFeatureExtractor
										from .modeling_clap import (
										    CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ClapAudioModel,
										    ClapAudioModelWithProjection,
										    ClapModel,
										    ClapPreTrainedModel,
										    ClapTextModel,
										    ClapTextModelWithProjection,
										)


else:
					import sys

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