



from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


_lowerCamelCase         =	logging.get_logger(__name__)

class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):
     lowerCAmelCase       =      ["""input_values""", """attention_mask"""]





     def __init__(							self			:				Union[str, Any]  ,	UpperCamelCase			:				Optional[int]=1  ,	UpperCamelCase			:				Dict=1_6_0_0_0  ,	UpperCamelCase			:				int=1_2_8  ,	UpperCamelCase			:				Tuple=1_0_2_4  ,	UpperCamelCase			:				List[str]=0.0  ,	UpperCamelCase			:				str=True  ,	UpperCamelCase			:				Optional[Any]=-4.2_6_7_7_3_9_3  ,	UpperCamelCase			:				Union[str, Any]=4.5_6_8_9_9_7_4  ,	UpperCamelCase			:				Tuple=False  ,	**UpperCamelCase			:				Optional[Any]  ,	)->Union[str, Any]:
      super().__init__(feature_size=UpperCamelCase  ,	sampling_rate=UpperCamelCase  ,	padding_value=UpperCamelCase  ,	**UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     str							=						num_mel_bins
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						max_length
      __SCREAMING_SNAKE_CASE							:     Dict							=						do_normalize
      __SCREAMING_SNAKE_CASE							:     List[str]							=						mean
      __SCREAMING_SNAKE_CASE							:     Any							=						std
      __SCREAMING_SNAKE_CASE							:     Tuple							=						return_attention_mask





     def 						__snake_case     (							self			:				Any  ,	UpperCamelCase			:				np.ndarray  ,	UpperCamelCase			:				int  ,	)->np.ndarray:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						torch.from_numpy(UpperCamelCase					).unsqueeze(0					)
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						ta_kaldi.fbank(
          UpperCamelCase  ,	htk_compat=UpperCamelCase  ,	sample_frequency=self.sampling_rate  ,	use_energy=UpperCamelCase  ,	window_type="hanning"  ,	num_mel_bins=self.num_mel_bins  ,	dither=0.0  ,	frame_shift=1_0  ,	)

      __SCREAMING_SNAKE_CASE							:     Tuple							=						fbank.shape[0]
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						max_length - n_frames

      # pad or truncate, depending on difference
      if difference > 0:
       __SCREAMING_SNAKE_CASE							:     List[Any]							=						torch.nn.ZeroPadad((0, 0, 0, difference)					)
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						pad_module(UpperCamelCase					)
      elif difference < 0:
       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						fbank[0:max_length, :]

      __SCREAMING_SNAKE_CASE							:     Tuple							=						fbank.numpy()

      return fbank





     def 						__snake_case     (							self			:				List[Any]  ,	UpperCamelCase			:				np.ndarray					)->np.ndarray:
      return (input_values - (self.mean)) / (self.std * 2)






     def __call__(							self			:				Union[str, Any]  ,	UpperCamelCase			:				Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]  ,	UpperCamelCase			:				Optional[int] = None  ,	UpperCamelCase			:				Optional[Union[str, TensorType]] = None  ,	**UpperCamelCase			:				Optional[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."					)

      __SCREAMING_SNAKE_CASE							:     List[str]							=						isinstance(UpperCamelCase  ,	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							:     Optional[Any]							=						is_batched_numpy or (
          isinstance(UpperCamelCase  ,	(list, tuple)					) and (isinstance(raw_speech[0]  ,	(np.ndarray, tuple, list)					))
      )

      if is_batched:
       __SCREAMING_SNAKE_CASE							:     int							=						[np.asarray(UpperCamelCase  ,	dtype=np.floataa					) for speech in raw_speech]
      elif not is_batched and not isinstance(UpperCamelCase  ,	np.ndarray					):
       __SCREAMING_SNAKE_CASE							:     Any							=						np.asarray(UpperCamelCase  ,	dtype=np.floataa					)
      elif isinstance(UpperCamelCase  ,	np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						raw_speech.astype(np.floataa					)

      # always return batch
      if not is_batched:
       __SCREAMING_SNAKE_CASE							:     List[Any]							=						[raw_speech]

      # extract fbank features and pad/truncate to max_length
      __SCREAMING_SNAKE_CASE							:     Tuple							=						[self._extract_fbank_features(UpperCamelCase  ,	max_length=self.max_length					) for waveform in raw_speech]

      # convert into BatchFeature
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						BatchFeature({"input_values": features}					)

      # make sure list is in array format
      __SCREAMING_SNAKE_CASE							:     str							=						padded_inputs.get("input_values"					)
      if isinstance(input_values[0]  ,	UpperCamelCase					):
       __SCREAMING_SNAKE_CASE							:     List[str]							=						[np.asarray(UpperCamelCase  ,	dtype=np.floataa					) for feature in input_values]

      # normalization
      if self.do_normalize:
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						[self.normalize(UpperCamelCase					) for feature in input_values]

      if return_tensors is not None:
       __SCREAMING_SNAKE_CASE							:     Dict							=						padded_inputs.convert_to_tensors(UpperCamelCase					)

      return padded_inputs








'''simple docstring'''





from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
 if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.27.0")):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import (
     AudioLDMPipeline,
 )
else:
 from .pipeline_audioldm import AudioLDMPipeline






