


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


lowerCamelCase											=       logging.get_logger(__name__)




class A  (   __A     ):
      UpperCamelCase__							:  Union[str, Any]      =["input_features", "attention_mask"]






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




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

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

             _lowerCamelCase       :		Union[str, Any]		=win_length * sampling_rate // 1000
             _lowerCamelCase       :		Optional[int]		=hop_length * sampling_rate // 1000

             _lowerCamelCase       :		Tuple		=optimal_fft_length(self.sample_size )
             _lowerCamelCase       :		str		=(self.n_fft // 2) + 1






      def        lowerCamelCase       (					self						:     List[str]			,   lowercase_						:     np.array )					->						np.ndarray:
             """simple docstring"""




             if self.win_function == "hamming_window":
                    _lowerCamelCase       :		Union[str, Any]		=window_function(window_length=self.sample_size			,   name=self.win_function			,   periodic=lowercase_ )
             else:
                    _lowerCamelCase       :		Optional[Any]		=window_function(window_length=self.sample_size			,   name=self.win_function )

             _lowerCamelCase       :		Tuple		=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			,   )

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






      def        lowerCamelCase       (					self						:     Union[str, Any]			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     Union[str, Any]			,   lowercase_						:     Union[str, Any] )					->						int:
             """simple docstring"""




             if self.normalize_means:
                    _lowerCamelCase       :		int		=x[:input_length].mean(axis=0 )
                    _lowerCamelCase       :		str		=np.subtract(lowercase_			,   lowercase_ )
             if self.normalize_vars:
                    _lowerCamelCase       :		int		=x[:input_length].std(axis=0 )
                    _lowerCamelCase       :		Optional[int]		=np.divide(lowercase_			,   lowercase_ )

             if input_length < x.shape[0]:
                    _lowerCamelCase       :		List[Any]		=padding_value

             # make sure array is in float32
             _lowerCamelCase       :		Tuple		=x.astype(np.floataa )

             return x






      def        lowerCamelCase       (					self						:     Tuple			,   lowercase_						:     List[np.ndarray]			,   lowercase_						:     Optional[np.ndarray] = None )					->						List[np.ndarray]:
             """simple docstring"""




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






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

             _lowerCamelCase       :		Dict		=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}''' )
             _lowerCamelCase       :		Optional[Any]		=is_batched_numpy or (
                 isinstance(lowercase_			,   (list, tuple) ) and (isinstance(raw_speech[0]			,   (np.ndarray, tuple, list) ))
             )

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

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

             # extract fbank features
             _lowerCamelCase       :		Union[str, Any]		=[self._extract_mfsc_features(lowercase_ ) for one_waveform in raw_speech]

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

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

             _lowerCamelCase       :		Tuple		=padded_inputs.get('attention_mask' )
             if attention_mask is not None:
                    _lowerCamelCase       :		int		=[np.asarray(lowercase_			,   dtype=np.intaa ) for array in attention_mask]

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

             if return_tensors is not None:
                    _lowerCamelCase       :		Tuple		=padded_inputs.convert_to_tensors(lowercase_ )

             return padded_inputs



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCamelCase  							=						logging.get_logger(__name__)


if is_vision_available():
      import PIL



class   snake_case_       (		__A       ):
   __A	:		str         =				["pixel_values"]






   def __init__(      self				:     int	,						lowercase_				:     bool = True	,						lowercase_				:     Dict[str, int] = None	,						lowercase_				:     PILImageResampling = PILImageResampling.BICUBIC	,						lowercase_				:     bool = True	,						lowercase_				:     Dict[str, int] = None	,						lowercase_				:     bool = True	,						lowercase_				:     Union[int, float] = 1 / 2_55	,						lowercase_				:     bool = True	,						lowercase_				:     Optional[Union[float, List[float]]] = None	,						lowercase_				:     Optional[Union[float, List[float]]] = None	,						lowercase_				:     bool = True	,						**lowercase_				:     Union[str, Any]	,						)					->						None:
       super().__init__(**lowercase_  )
       lowercase__ :   Tuple    					=				size if size is not None else {"shortest_edge": 2_24}
       lowercase__ :   Tuple    					=				get_size_dict(lowercase_	,						default_to_square=lowercase_  )
       lowercase__ :   List[str]    					=				crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
       lowercase__ :   Tuple    					=				get_size_dict(lowercase_	,						default_to_square=lowercase_	,						param_name="crop_size"  )

       lowercase__ :   Dict    					=				do_resize
       lowercase__ :   List[Any]    					=				size
       lowercase__ :   int    					=				resample
       lowercase__ :   Union[str, Any]    					=				do_center_crop
       lowercase__ :   Optional[int]    					=				crop_size
       lowercase__ :   List[str]    					=				do_rescale
       lowercase__ :   int    					=				rescale_factor
       lowercase__ :   List[Any]    					=				do_normalize
       lowercase__ :   Union[str, Any]    					=				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       lowercase__ :   str    					=				image_std if image_std is not None else OPENAI_CLIP_STD
       lowercase__ :   Dict    					=				do_convert_rgb






   def       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     np.ndarray	,						lowercase_				:     Dict[str, int]	,						lowercase_				:     PILImageResampling = PILImageResampling.BICUBIC	,						lowercase_				:     Optional[Union[str, ChannelDimension]] = None	,						**lowercase_				:     Union[str, Any]	,						)					->						np.ndarray:
       lowercase__ :   str    					=				get_size_dict(lowercase_	,						default_to_square=lowercase_  )
       if "shortest_edge" not in size:
           raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''  )
       lowercase__ :   Dict    					=				get_resize_output_image_size(lowercase_	,						size=size["shortest_edge"]	,						default_to_square=lowercase_  )
       return resize(lowercase_	,						size=lowercase_	,						resample=lowercase_	,						data_format=lowercase_	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     int	,						lowercase_				:     np.ndarray	,						lowercase_				:     Dict[str, int]	,						lowercase_				:     Optional[Union[str, ChannelDimension]] = None	,						**lowercase_				:     int	,						)					->						np.ndarray:
       lowercase__ :   Optional[Any]    					=				get_size_dict(lowercase_  )
       if "height" not in size or "width" not in size:
           raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''  )
       return center_crop(lowercase_	,						size=(size["height"], size["width"])	,						data_format=lowercase_	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     np.ndarray	,						lowercase_				:     Union[int, float]	,						lowercase_				:     Optional[Union[str, ChannelDimension]] = None	,						**lowercase_				:     Optional[Any]	,						)					->						Any:
       return rescale(lowercase_	,						scale=lowercase_	,						data_format=lowercase_	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     str	,						lowercase_				:     np.ndarray	,						lowercase_				:     Union[float, List[float]]	,						lowercase_				:     Union[float, List[float]]	,						lowercase_				:     Optional[Union[str, ChannelDimension]] = None	,						**lowercase_				:     str	,						)					->						np.ndarray:
       return normalize(lowercase_	,						mean=lowercase_	,						std=lowercase_	,						data_format=lowercase_	,						**lowercase_  )







   def       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     ImageInput	,						lowercase_				:     bool = None	,						lowercase_				:     Dict[str, int] = None	,						lowercase_				:     PILImageResampling = None	,						lowercase_				:     bool = None	,						lowercase_				:     int = None	,						lowercase_				:     bool = None	,						lowercase_				:     float = None	,						lowercase_				:     bool = None	,						lowercase_				:     Optional[Union[float, List[float]]] = None	,						lowercase_				:     Optional[Union[float, List[float]]] = None	,						lowercase_				:     bool = None	,						lowercase_				:     Optional[Union[str, TensorType]] = None	,						lowercase_				:     Optional[ChannelDimension] = ChannelDimension.FIRST	,						**lowercase_				:     Union[str, Any]	,						)					->						PIL.Image.Image:
       lowercase__ :   int    					=				do_resize if do_resize is not None else self.do_resize
       lowercase__ :   Dict    					=				size if size is not None else self.size
       lowercase__ :   List[Any]    					=				get_size_dict(lowercase_	,						param_name="size"	,						default_to_square=lowercase_  )
       lowercase__ :   Dict    					=				resample if resample is not None else self.resample
       lowercase__ :   int    					=				do_center_crop if do_center_crop is not None else self.do_center_crop
       lowercase__ :   Dict    					=				crop_size if crop_size is not None else self.crop_size
       lowercase__ :   List[str]    					=				get_size_dict(lowercase_	,						param_name="crop_size"	,						default_to_square=lowercase_  )
       lowercase__ :   int    					=				do_rescale if do_rescale is not None else self.do_rescale
       lowercase__ :   Dict    					=				rescale_factor if rescale_factor is not None else self.rescale_factor
       lowercase__ :   Union[str, Any]    					=				do_normalize if do_normalize is not None else self.do_normalize
       lowercase__ :   int    					=				image_mean if image_mean is not None else self.image_mean
       lowercase__ :   List[str]    					=				image_std if image_std is not None else self.image_std
       lowercase__ :   Union[str, Any]    					=				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       lowercase__ :   Union[str, Any]    					=				make_list_of_images(lowercase_  )

       if not valid_images(lowercase_  ):
           raise ValueError(
               "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
               "torch.Tensor, tf.Tensor or jax.ndarray."  )

       if do_resize and size is None:
           raise ValueError("Size must be specified if do_resize is True."  )

       if do_center_crop and crop_size is None:
           raise ValueError("Crop size must be specified if do_center_crop is True."  )

       if do_rescale and rescale_factor is None:
           raise ValueError("Rescale factor must be specified if do_rescale is True."  )

       if do_normalize and (image_mean is None or image_std is None):
           raise ValueError("Image mean and std must be specified if do_normalize is True."  )

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
           lowercase__ :   Dict    					=				[convert_to_rgb(lowercase_  ) for image in images]

       # All transformations expect numpy arrays.
       lowercase__ :   Optional[Any]    					=				[to_numpy_array(lowercase_  ) for image in images]

       if do_resize:
           lowercase__ :   List[Any]    					=				[self.resize(image=lowercase_	,						size=lowercase_	,						resample=lowercase_  ) for image in images]

       if do_center_crop:
           lowercase__ :   int    					=				[self.center_crop(image=lowercase_	,						size=lowercase_  ) for image in images]

       if do_rescale:
           lowercase__ :   str    					=				[self.rescale(image=lowercase_	,						scale=lowercase_  ) for image in images]

       if do_normalize:
           lowercase__ :   Optional[int]    					=				[self.normalize(image=lowercase_	,						mean=lowercase_	,						std=lowercase_  ) for image in images]

       lowercase__ :   Optional[Any]    					=				[to_channel_dimension_format(lowercase_	,						lowercase_  ) for image in images]

       lowercase__ :   List[str]    					=				{"pixel_values": images}
       return BatchFeature(data=lowercase_	,						tensor_type=lowercase_  )



