






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:     List[str]   					=					logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  ['''input_features''', '''attention_mask''']


  def __init__(      self		:			Optional[int]       ,				lowerCamelCase		:			List[Any]=80       ,				lowerCamelCase		:			str=1_6000       ,				lowerCamelCase		:			List[Any]=0.0       ,				lowerCamelCase		:			Dict=10       ,				lowerCamelCase		:			Dict=25       ,				lowerCamelCase		:			str="hamming_window"       ,				lowerCamelCase		:			Union[str, Any]=3_2768.0       ,				lowerCamelCase		:			Any=0.97       ,				lowerCamelCase		:			Optional[Any]=1.0       ,				lowerCamelCase		:			Tuple=True       ,				lowerCamelCase		:			List[str]=True       ,				lowerCamelCase		:			List[str]=False       ,				**lowerCamelCase		:			int       ,				)						->	Optional[int]:


         """simple docstring"""
         super().__init__(feature_size=lowerCamelCase       ,				sampling_rate=lowerCamelCase       ,				padding_value=lowerCamelCase       ,				**lowerCamelCase							)

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

         _UpperCAmelCase							=    win_length * sampling_rate // 1000
         _UpperCAmelCase							=    hop_length * sampling_rate // 1000

         _UpperCAmelCase							=    optimal_fft_length(self.sample_size							)
         _UpperCAmelCase							=    (self.n_fft // 2) + 1


  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			np.array							)						->	np.ndarray:


         """simple docstring"""
         if self.win_function == "hamming_window":
                _UpperCAmelCase							=    window_function(window_length=self.sample_size       ,				name=self.win_function       ,				periodic=lowerCamelCase							)
         else:
                _UpperCAmelCase							=    window_function(window_length=self.sample_size       ,				name=self.win_function							)

         _UpperCAmelCase							=    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       ,				)

         _UpperCAmelCase							=    spectrogram(
             one_waveform * self.frame_signal_scale       ,				window=lowerCamelCase       ,				frame_length=self.sample_size       ,				hop_length=self.sample_stride       ,				fft_length=self.n_fft       ,				center=lowerCamelCase       ,				preemphasis=self.preemphasis_coeff       ,				mel_filters=lowerCamelCase       ,				mel_floor=self.mel_floor       ,				log_mel="""log"""       ,				)
         return msfc_features.T


  def  lowerCamelCase       (      self		:			List[str]       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			Union[str, Any]       ,				lowerCamelCase		:			Any							)						->	List[str]:


         """simple docstring"""
         # make sure we normalize float32 arrays
         if self.normalize_means:
                _UpperCAmelCase							=    x[:input_length].mean(axis=0							)
                _UpperCAmelCase							=    np.subtract(lowerCamelCase       ,				lowerCamelCase							)
         if self.normalize_vars:
                _UpperCAmelCase							=    x[:input_length].std(axis=0							)
                _UpperCAmelCase							=    np.divide(lowerCamelCase       ,				lowerCamelCase							)

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

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

         return x


  def  lowerCamelCase       (      self		:			Optional[int]       ,				lowerCamelCase		:			List[np.ndarray]       ,				lowerCamelCase		:			Optional[np.ndarray] = None							)						->	List[np.ndarray]:


         """simple docstring"""
         _UpperCAmelCase							=    attention_mask.sum(-1							) if attention_mask is not None else [x.shape[0] for x in input_features]
         return [self._normalize_one(lowerCamelCase       ,				lowerCamelCase       ,				self.padding_value							) for x, n in zip(lowerCamelCase       ,				lowerCamelCase							)]





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


         """simple docstring"""

         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."""							)

         _UpperCAmelCase							=    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							=    is_batched_numpy or (
             isinstance(lowerCamelCase       ,				(list, tuple)							) and (isinstance(raw_speech[0]       ,				(np.ndarray, tuple, list)							))
         )

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

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

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

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

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

         _UpperCAmelCase							=    padded_inputs.get("""attention_mask"""							)
         if attention_mask is not None:
                _UpperCAmelCase							=    [np.asarray(lowerCamelCase       ,				dtype=np.intaa							) for array in attention_mask]

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

         if return_tensors is not None:
                _UpperCAmelCase							=    padded_inputs.convert_to_tensors(lowerCamelCase							)

         return padded_inputs







# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel  # noqa: F401
from ..controlnet.pipeline_controlnet import StableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    '''stable diffusion controlnet''',
    '''0.22.0''',
    '''Importing `StableDiffusionControlNetPipeline` or `MultiControlNetModel` from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import StableDiffusionControlNetPipeline` instead.''',
    standard_warn=False,
    stacklevel=3,
)