


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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__UpperCAmelCase : int 			=				logging.get_logger(__name__)



class    UpperCAmelCase_	(							__lowerCAmelCase):
       '''simple docstring'''
       __UpperCamelCase      :     List[Any]							=					['''pixel_values''']






       def __init__(     self ,					__SCREAMING_SNAKE_CASE = True ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC ,					__SCREAMING_SNAKE_CASE = True ,					__SCREAMING_SNAKE_CASE = True ,					__SCREAMING_SNAKE_CASE = 1 / 255 ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = True ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              super().__init__(**lowerCAmelCase_						)
              UpperCamelCase    :	Union[str, Any]   	=		size if size is not None else {'''height''': 224, '''width''': 224}
              UpperCamelCase    :	Dict   	=		get_size_dict(lowerCAmelCase_						)
              UpperCamelCase    :	Union[str, Any]   	=		crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
              UpperCamelCase    :	Dict   	=		get_size_dict(lowerCAmelCase_ ,					default_to_square=lowerCAmelCase_ ,					param_name='''crop_size'''						)

              UpperCamelCase    :	str   	=		do_resize
              UpperCamelCase    :	Any   	=		do_rescale
              UpperCamelCase    :	str   	=		do_normalize
              UpperCamelCase    :	Optional[Any]   	=		do_center_crop
              UpperCamelCase    :	List[Any]   	=		crop_size
              UpperCamelCase    :	List[Any]   	=		size
              UpperCamelCase    :	Any   	=		resample
              UpperCamelCase    :	str   	=		rescale_factor
              UpperCamelCase    :	Optional[Any]   	=		image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
              UpperCamelCase    :	List[Any]   	=		image_std if image_std is not None else IMAGENET_DEFAULT_STD






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR ,					__SCREAMING_SNAKE_CASE = None ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		get_size_dict(lowerCAmelCase_						)

              if "shortest_edge" in size:
                     UpperCamelCase    :	Optional[Any]   	=		get_resize_output_image_size(lowerCAmelCase_ ,					size=size['''shortest_edge'''] ,					default_to_square=lowerCAmelCase_						)
                     # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
              elif "height" in size and "width" in size:
                     UpperCamelCase    :	Optional[Any]   	=		(size['''height'''], size['''width'''])
              else:
                     raise ValueError(f"""Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}"""						)
              return resize(lowerCAmelCase_ ,					size=lowerCAmelCase_ ,					resample=lowerCAmelCase_ ,					data_format=lowerCAmelCase_ ,					**lowerCAmelCase_						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              UpperCamelCase    :	int   	=		get_size_dict(lowerCAmelCase_						)
              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(lowerCAmelCase_ ,					size=(size['''height'''], size['''width''']) ,					data_format=lowerCAmelCase_ ,					**lowerCAmelCase_						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None ,					**__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              return rescale(lowerCAmelCase_ ,					scale=lowerCAmelCase_ ,					data_format=lowerCAmelCase_ ,					**lowerCAmelCase_						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              return normalize(lowerCAmelCase_ ,					mean=lowerCAmelCase_ ,					std=lowerCAmelCase_ ,					data_format=lowerCAmelCase_ ,					**lowerCAmelCase_						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = ChannelDimension.FIRST ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		do_resize if do_resize is not None else self.do_resize
              UpperCamelCase    :	Any   	=		do_rescale if do_rescale is not None else self.do_rescale
              UpperCamelCase    :	Tuple   	=		do_normalize if do_normalize is not None else self.do_normalize
              UpperCamelCase    :	int   	=		do_center_crop if do_center_crop is not None else self.do_center_crop
              UpperCamelCase    :	str   	=		crop_size if crop_size is not None else self.crop_size
              UpperCamelCase    :	Dict   	=		get_size_dict(lowerCAmelCase_ ,					param_name='''crop_size''' ,					default_to_square=lowerCAmelCase_						)
              UpperCamelCase    :	Any   	=		resample if resample is not None else self.resample
              UpperCamelCase    :	Optional[Any]   	=		rescale_factor if rescale_factor is not None else self.rescale_factor
              UpperCamelCase    :	Union[str, Any]   	=		image_mean if image_mean is not None else self.image_mean
              UpperCamelCase    :	str   	=		image_std if image_std is not None else self.image_std

              UpperCamelCase    :	Optional[int]   	=		size if size is not None else self.size
              UpperCamelCase    :	List[Any]   	=		get_size_dict(lowerCAmelCase_						)

              if not is_batched(lowerCAmelCase_						):
                     UpperCamelCase    :	Optional[Any]   	=		[images]

              if not valid_images(lowerCAmelCase_						):
                     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.'''						)

              # All transformations expect numpy arrays.
              UpperCamelCase    :	int   	=		[to_numpy_array(lowerCAmelCase_						) for image in images]

              if do_resize:
                     UpperCamelCase    :	Optional[int]   	=		[self.resize(image=lowerCAmelCase_ ,					size=lowerCAmelCase_ ,					resample=lowerCAmelCase_						) for image in images]

              if do_center_crop:
                     UpperCamelCase    :	Any   	=		[self.center_crop(image=lowerCAmelCase_ ,					size=lowerCAmelCase_						) for image in images]

              if do_rescale:
                     UpperCamelCase    :	Optional[int]   	=		[self.rescale(image=lowerCAmelCase_ ,					scale=lowerCAmelCase_						) for image in images]

              if do_normalize:
                     UpperCamelCase    :	Any   	=		[self.normalize(image=lowerCAmelCase_ ,					mean=lowerCAmelCase_ ,					std=lowerCAmelCase_						) for image in images]

              UpperCamelCase    :	int   	=		[to_channel_dimension_format(lowerCAmelCase_ ,					lowerCAmelCase_						) for image in images]

              UpperCamelCase    :	Any   	=		{'''pixel_values''': images}
              return BatchFeature(data=lowerCAmelCase_ ,					tensor_type=lowerCAmelCase_						)





import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

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


__UpperCAmelCase : Dict 			=				logging.get_logger(__name__)



class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     Any							=					["input_features"]






       def __init__(     self ,					__SCREAMING_SNAKE_CASE=80 ,					__SCREAMING_SNAKE_CASE=16_000 ,					__SCREAMING_SNAKE_CASE=160 ,					__SCREAMING_SNAKE_CASE=30 ,					__SCREAMING_SNAKE_CASE=400 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=False ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              super().__init__(
                  feature_size=__SCREAMING_SNAKE_CASE ,					sampling_rate=__SCREAMING_SNAKE_CASE ,					padding_value=__SCREAMING_SNAKE_CASE ,					return_attention_mask=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE ,					)
              UpperCamelCase    :	List[str]   	=		n_fft
              UpperCamelCase    :	Dict   	=		hop_length
              UpperCamelCase    :	Dict   	=		chunk_length
              UpperCamelCase    :	List[str]   	=		chunk_length * sampling_rate
              UpperCamelCase    :	Dict   	=		self.n_samples // hop_length
              UpperCamelCase    :	str   	=		sampling_rate
              UpperCamelCase    :	Union[str, Any]   	=		mel_filter_bank(
                  num_frequency_bins=1 + n_fft // 2 ,					num_mel_filters=__SCREAMING_SNAKE_CASE ,					min_frequency=0.0 ,					max_frequency=8_000.0 ,					sampling_rate=__SCREAMING_SNAKE_CASE ,					norm='''slaney''' ,					mel_scale='''slaney''' ,					)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		spectrogram(
                  __SCREAMING_SNAKE_CASE ,					window_function(self.n_fft ,					'''hann'''						) ,					frame_length=self.n_fft ,					hop_length=self.hop_length ,					power=2.0 ,					mel_filters=self.mel_filters ,					log_mel='''log10''' ,					)
              UpperCamelCase    :	int   	=		log_spec[:, :-1]
              UpperCamelCase    :	int   	=		np.maximum(__SCREAMING_SNAKE_CASE ,					log_spec.max() - 8.0						)
              UpperCamelCase    :	Any   	=		(log_spec + 4.0) / 4.0
              return log_spec






       @staticmethod
       # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
       def  _lowercase   (     __SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = 0.0						):




              """simple docstring"""




              if attention_mask is not None:
                     UpperCamelCase    :	List[Any]   	=		np.array(__SCREAMING_SNAKE_CASE ,					np.intaa						)
                     UpperCamelCase    :	Optional[Any]   	=		[]

                     for vector, length in zip(__SCREAMING_SNAKE_CASE ,					attention_mask.sum(-1						)						):
                            UpperCamelCase    :	Optional[Any]   	=		(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7						)
                            if length < normed_slice.shape[0]:
                                   UpperCamelCase    :	Optional[int]   	=		padding_value

                            normed_input_values.append(__SCREAMING_SNAKE_CASE						)
              else:
                     UpperCamelCase    :	Union[str, Any]   	=		[(x - x.mean()) / np.sqrt(x.var() + 1e-7						) for x in input_values]

              return normed_input_values






       def __call__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = True ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = "max_length" ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					**__SCREAMING_SNAKE_CASE ,					):




              """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.__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    :	Tuple   	=		isinstance(__SCREAMING_SNAKE_CASE ,					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    :	Union[str, Any]   	=		is_batched_numpy or (
                  isinstance(__SCREAMING_SNAKE_CASE ,					(list, tuple)						) and (isinstance(raw_speech[0] ,					(np.ndarray, tuple, list)						))
              )

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

              # always return batch
              if not is_batched:
                     UpperCamelCase    :	Optional[int]   	=		[np.asarray([raw_speech]						).T]

              UpperCamelCase    :	Optional[int]   	=		BatchFeature({'''input_features''': raw_speech}						)

              # convert into correct format for padding

              UpperCamelCase    :	Optional[Any]   	=		self.pad(
                  __SCREAMING_SNAKE_CASE ,					padding=__SCREAMING_SNAKE_CASE ,					max_length=max_length if max_length else self.n_samples ,					truncation=__SCREAMING_SNAKE_CASE ,					pad_to_multiple_of=__SCREAMING_SNAKE_CASE ,					return_attention_mask=return_attention_mask or do_normalize ,					)

              # zero-mean and unit-variance normalization
              if do_normalize:
                     UpperCamelCase    :	Optional[Any]   	=		self.zero_mean_unit_var_norm(
                         padded_inputs['''input_features'''] ,					attention_mask=padded_inputs['''attention_mask'''] ,					padding_value=self.padding_value ,					)
                     UpperCamelCase    :	List[str]   	=		np.stack(padded_inputs['''input_features'''] ,					axis=0						)

              # make sure list is in array format
              UpperCamelCase    :	Dict   	=		padded_inputs.get('''input_features'''						).transpose(2 ,					0 ,					1						)

              UpperCamelCase    :	Tuple   	=		[self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE						) for waveform in input_features[0]]

              if isinstance(input_features[0] ,					__SCREAMING_SNAKE_CASE						):
                     UpperCamelCase    :	Optional[int]   	=		[np.asarray(__SCREAMING_SNAKE_CASE ,					dtype=np.floataa						) for feature in input_features]
              else:
                     UpperCamelCase    :	Dict   	=		input_features

              if return_attention_mask:
                     # rescale from sample (48000) to feature (3000)
                     UpperCamelCase    :	Union[str, Any]   	=		padded_inputs['''attention_mask'''][:, :: self.hop_length]

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

              return padded_inputs






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[Any]   	=		copy.deepcopy(self.__dict__						)
              UpperCamelCase    :	List[str]   	=		self.__class__.__name__
              if "mel_filters" in output:
                     del output["mel_filters"]
              return output

