






"""simple docstring"""


from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_UpperCamelCase    :   str   			=						logging.get_logger(__name__)






class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					['''pixel_values''']





    def __init__(							self     : Any    ,		A     : bool = True    ,		A     : int = 3_2    ,		A     : List[str]=PILImageResampling.BILINEAR    ,		A     : bool = True    ,		**A     : Union[str, Any]    ,		):


      '''simple docstring'''




      a					:      str    =       do_resize
      a					:      int    =       do_rescale
      a					:      List[str]    =       size_divisor
      a					:      Optional[Any]    =       resample
      super().__init__(**A      )





    def     lowerCamelCase__				(							self     : Union[str, Any]    ,		A     : np.ndarray    ,		A     : int    ,		A     : List[Any]    ,		A     : Optional[ChannelDimension] = None    ,		**A     : Optional[Any]      ):


      '''simple docstring'''




      a,					a					:      Optional[int]    =       get_image_size(A      )
      # Rounds the height and width down to the closest multiple of size_divisor
      a					:      List[Any]    =       height // size_divisor * size_divisor
      a					:      Dict    =       width // size_divisor * size_divisor
      a					:      Any    =       resize(A    ,		(new_h, new_w)    ,		resample=A    ,		data_format=A    ,		**A      )
      return image





    def     lowerCamelCase__				(							self     : Union[str, Any]    ,		A     : np.ndarray    ,		A     : float    ,		A     : Optional[ChannelDimension] = None    ,		**A     : Optional[Any]      ):


      '''simple docstring'''




      return rescale(image=A    ,		scale=A    ,		data_format=A    ,		**A      )





    def     lowerCamelCase__				(							self     : Dict    ,		A     : Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]    ,		A     : Optional[bool] = None    ,		A     : Optional[int] = None    ,		A     : List[Any]=None    ,		A     : Optional[bool] = None    ,		A     : Optional[Union[TensorType, str]] = None    ,		A     : ChannelDimension = ChannelDimension.FIRST    ,		**A     : Optional[Any]    ,		):


      '''simple docstring'''




      a					:      Tuple    =       do_resize if do_resize is not None else self.do_resize
      a					:      List[str]    =       do_rescale if do_rescale is not None else self.do_rescale
      a					:      int    =       size_divisor if size_divisor is not None else self.size_divisor
      a					:      int    =       resample if resample is not None else self.resample

      if do_resize and size_divisor is None:
        raise ValueError('size_divisor is required for resizing'      )

      a					:      int    =       make_list_of_images(A      )

      if not valid_images(A      ):
        raise ValueError('Invalid image(s)'      )

      # All transformations expect numpy arrays.
      a					:      List[Any]    =       [to_numpy_array(A      ) for img in images]

      if do_resize:
        a					:      Any    =       [self.resize(A    ,		size_divisor=A    ,		resample=A      ) for image in images]

      if do_rescale:
        a					:      Tuple    =       [self.rescale(A    ,		scale=1 / 2_5_5      ) for image in images]

      a					:      str    =       [to_channel_dimension_format(A    ,		A      ) for image in images]

      a					:      Any    =       {'pixel_values': images}
      return BatchFeature(data=A    ,		tensor_type=A      )












"""simple docstring"""


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


_UpperCamelCase    :   int   			=						logging.get_logger(__name__)






class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					['''input_features''', '''attention_mask''']





    def __init__(							self     : Optional[int]    ,		A     : Optional[Any]=8_0    ,		A     : str=1_6_0_0_0    ,		A     : List[str]=0.0    ,		A     : Any=1_0    ,		A     : Union[str, Any]=2_5    ,		A     : str="hamming_window"    ,		A     : str=3_27_68.0    ,		A     : Union[str, Any]=0.97    ,		A     : Dict=1.0    ,		A     : Any=True    ,		A     : Union[str, Any]=True    ,		A     : List[Any]=False    ,		**A     : Tuple    ,		):


      '''simple docstring'''




      super().__init__(feature_size=A    ,		sampling_rate=A    ,		padding_value=A    ,		**A      )

      a					:      Any    =       feature_size
      a					:      List[Any]    =       sampling_rate
      a					:      Any    =       padding_value
      a					:      str    =       hop_length
      a					:      Any    =       win_length
      a					:      List[Any]    =       frame_signal_scale
      a					:      Tuple    =       preemphasis_coeff
      a					:      Dict    =       mel_floor
      a					:      Optional[int]    =       normalize_means
      a					:      List[str]    =       normalize_vars
      a					:      Dict    =       win_function
      a					:      Union[str, Any]    =       return_attention_mask

      a					:      List[Any]    =       win_length * sampling_rate // 1_0_0_0
      a					:      Tuple    =       hop_length * sampling_rate // 1_0_0_0

      a					:      List[Any]    =       optimal_fft_length(self.sample_size      )
      a					:      Any    =       (self.n_fft // 2) + 1





    def     lowerCamelCase__				(							self     : List[Any]    ,		A     : np.array      ):


      '''simple docstring'''




      if self.win_function == "hamming_window":
        a					:      List[str]    =       window_function(window_length=self.sample_size    ,		name=self.win_function    ,		periodic=A      )
      else:
        a					:      Dict    =       window_function(window_length=self.sample_size    ,		name=self.win_function      )

      a					:      str    =       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    ,		)

      a					:      List[Any]    =       spectrogram(
          one_waveform * self.frame_signal_scale    ,		window=A    ,		frame_length=self.sample_size    ,		hop_length=self.sample_stride    ,		fft_length=self.n_fft    ,		center=A    ,		preemphasis=self.preemphasis_coeff    ,		mel_filters=A    ,		mel_floor=self.mel_floor    ,		log_mel='log'    ,		)
      return msfc_features.T





    def     lowerCamelCase__				(							self     : int    ,		A     : Tuple    ,		A     : int    ,		A     : Optional[int]      ):


      '''simple docstring'''




      if self.normalize_means:
        a					:      Any    =       x[:input_length].mean(axis=0      )
        a					:      Dict    =       np.subtract(A    ,		A      )
      if self.normalize_vars:
        a					:      Dict    =       x[:input_length].std(axis=0      )
        a					:      Dict    =       np.divide(A    ,		A      )

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

      # make sure array is in float32
      a					:      Optional[int]    =       x.astype(np.floataa      )

      return x





    def     lowerCamelCase__				(							self     : str    ,		A     : List[np.ndarray]    ,		A     : Optional[np.ndarray] = None      ):


      '''simple docstring'''




      a					:      str    =       attention_mask.sum(-1      ) if attention_mask is not None else [x.shape[0] for x in input_features]
      return [self._normalize_one(A    ,		A    ,		self.padding_value      ) for x, n in zip(A    ,		A      )]





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


      '''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.'      )

      a					:      Optional[int]    =       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}'''      )
      a					:      Dict    =       is_batched_numpy or (
          isinstance(A    ,		(list, tuple)      ) and (isinstance(raw_speech[0]    ,		(np.ndarray, tuple, list)      ))
      )

      if is_batched:
        a					:      str    =       [np.asarray(A    ,		dtype=np.floataa      ) for speech in raw_speech]
      elif not is_batched and not isinstance(A    ,		np.ndarray      ):
        a					:      List[str]    =       np.asarray(A    ,		dtype=np.floataa      )
      elif isinstance(A    ,		np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
        a					:      Optional[int]    =       raw_speech.astype(np.floataa      )

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

      # extract fbank features
      a					:      str    =       [self._extract_mfsc_features(A      ) for one_waveform in raw_speech]

      # convert into correct format for padding
      a					:      int    =       BatchFeature({'input_features': features}      )

      a					:      Union[str, Any]    =       self.pad(
          A    ,		padding=A    ,		max_length=A    ,		truncation=A    ,		pad_to_multiple_of=A    ,		return_attention_mask=A    ,		**A    ,		)
      # make sure list is in array format
      a					:      Optional[Any]    =       padded_inputs.get('input_features'      )
      if isinstance(input_features[0]    ,		A      ):
        a					:      List[str]    =       [np.asarray(A    ,		dtype=np.floataa      ) for feature in input_features]

      a					:      List[Any]    =       padded_inputs.get('attention_mask'      )
      if attention_mask is not None:
        a					:      int    =       [np.asarray(A    ,		dtype=np.intaa      ) for array in attention_mask]

      if self.normalize_means or self.normalize_vars:
        a					:      Any    =       (
            np.array(A    ,		dtype=np.intaa      )
            if self._get_padding_strategies(A    ,		max_length=A      ) is not PaddingStrategy.DO_NOT_PAD
            and padding
            else None
        )
        a					:      List[str]    =       self.normalize(
            padded_inputs['input_features']    ,		attention_mask=A      )

      if return_tensors is not None:
        a					:      Optional[int]    =       padded_inputs.convert_to_tensors(A      )

      return padded_inputs




