"""simple docstring"""







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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)







class  UpperCamelCase	(    _UpperCAmelCase						):
   lowercase							=						["""pixel_values"""]
   def __init__(    self  ,__UpperCamelCase = True  ,__UpperCamelCase = None  ,__UpperCamelCase = 0.9  ,__UpperCamelCase = PILImageResampling.BICUBIC  ,__UpperCamelCase = True  ,__UpperCamelCase = None  ,__UpperCamelCase = 1 / 255  ,__UpperCamelCase = True  ,__UpperCamelCase = True  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,**__UpperCamelCase  ,)			->   str:


        '''simple docstring'''


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

        lowercase_			:       Union[str, Any]									=			do_resize
        lowercase_			:       List[str]									=			size
        lowercase_			:       str									=			crop_pct
        lowercase_			:       str									=			resample
        lowercase_			:       Optional[Any]									=			do_center_crop
        lowercase_			:       Dict									=			crop_size
        lowercase_			:       int									=			do_rescale
        lowercase_			:       Optional[int]									=			rescale_factor
        lowercase_			:       str									=			do_normalize
        lowercase_			:       str									=			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        lowercase_			:       List[str]									=			image_std if image_std is not None else IMAGENET_DEFAULT_STD
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase = None  ,__UpperCamelCase = PILImageResampling.BICUBIC  ,__UpperCamelCase = None  ,**__UpperCamelCase  ,)			->   Tuple:


        '''simple docstring'''


        lowercase_			:       Tuple									=			get_size_dict(lowercase__  ,default_to_square=lowercase__    )
        if "shortest_edge" not in size and ("height" not in size or "width" not in size):
             raise ValueError(f'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''    )
        if crop_pct is not None:
             if "shortest_edge" in size:
                  lowercase_			:       Optional[int]									=			int(size['shortest_edge'] / crop_pct    )
             elif "height" in size and "width" in size:
                  if size["height"] == size["width"]:
                       lowercase_			:       Dict									=			int(size['height'] / crop_pct    )
                  else:
                       lowercase_			:       List[str]									=			(int(size['height'] / crop_pct    ), int(size['width'] / crop_pct    ))
             else:
                  raise ValueError('Invalid size for resize: {}'.format(lowercase__    )    )

             lowercase_			:       List[Any]									=			get_resize_output_image_size(lowercase__  ,size=lowercase__  ,default_to_square=lowercase__    )
        else:
             if "shortest_edge" in size:
                  lowercase_			:       Optional[int]									=			get_resize_output_image_size(lowercase__  ,size=size['shortest_edge']  ,default_to_square=lowercase__    )
             elif "height" in size and "width" in size:
                  lowercase_			:       int									=			(size["""height"""], size["""width"""])
             else:
                  raise ValueError('Invalid size for resize: {}'.format(lowercase__    )    )

        return resize(lowercase__  ,size=lowercase__  ,resample=lowercase__  ,data_format=lowercase__  ,**lowercase__    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase = None  ,**__UpperCamelCase  ,)			->   Any:


        '''simple docstring'''


        lowercase_			:       int									=			get_size_dict(lowercase__    )
        if "height" not in size or "width" not in size:
             raise ValueError(f'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''    )
        return center_crop(lowercase__  ,size=(size['height'], size['width'])  ,data_format=lowercase__  ,**lowercase__    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase = None  ,**__UpperCamelCase  ,)			->   Optional[Any]:


        '''simple docstring'''


        return rescale(lowercase__  ,scale=lowercase__  ,data_format=lowercase__  ,**lowercase__    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase = None  ,**__UpperCamelCase  ,)			->   Optional[int]:


        '''simple docstring'''


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







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = ChannelDimension.FIRST  ,**__UpperCamelCase  ,)			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       str									=			do_resize if do_resize is not None else self.do_resize
        lowercase_			:       Any									=			crop_pct if crop_pct is not None else self.crop_pct
        lowercase_			:       List[Any]									=			resample if resample is not None else self.resample
        lowercase_			:       str									=			do_center_crop if do_center_crop is not None else self.do_center_crop
        lowercase_			:       str									=			do_rescale if do_rescale is not None else self.do_rescale
        lowercase_			:       Any									=			rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase_			:       str									=			do_normalize if do_normalize is not None else self.do_normalize
        lowercase_			:       List[Any]									=			image_mean if image_mean is not None else self.image_mean
        lowercase_			:       int									=			image_std if image_std is not None else self.image_std

        lowercase_			:       List[Any]									=			size if size is not None else self.size
        lowercase_			:       Optional[Any]									=			get_size_dict(lowercase__  ,default_to_square=lowercase__    )
        lowercase_			:       List[Any]									=			crop_size if crop_size is not None else self.crop_size
        lowercase_			:       int									=			get_size_dict(lowercase__  ,param_name='crop_size'    )

        lowercase_			:       List[str]									=			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 or resample is None:
             raise ValueError('Size and resample must be specified if do_resize is True.'    )

        if do_center_crop and crop_pct is None:
             raise ValueError('Crop_pct 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.'    )

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

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

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

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

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

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

        lowercase_			:       Dict									=			{"""pixel_values""": images}
        return BatchFeature(data=lowercase__  ,tensor_type=lowercase__    )







"""simple docstring"""







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


__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)







class  UpperCamelCase	(    lowercase_						):
   lowercase							=						['input_features']
   def __init__(    self  ,__UpperCamelCase=80  ,__UpperCamelCase=1_6000  ,__UpperCamelCase=160  ,__UpperCamelCase=30  ,__UpperCamelCase=400  ,__UpperCamelCase=0.0  ,__UpperCamelCase=False  ,**__UpperCamelCase  ,)			->   Any:


        '''simple docstring'''


        super().__init__(
            feature_size=__UpperCamelCase  ,sampling_rate=__UpperCamelCase  ,padding_value=__UpperCamelCase  ,return_attention_mask=__UpperCamelCase  ,**__UpperCamelCase  ,)
        lowercase_			:       Dict									=			n_fft
        lowercase_			:       Optional[Any]									=			hop_length
        lowercase_			:       Optional[int]									=			chunk_length
        lowercase_			:       Optional[Any]									=			chunk_length * sampling_rate
        lowercase_			:       Tuple									=			self.n_samples // hop_length
        lowercase_			:       int									=			sampling_rate
        lowercase_			:       int									=			mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2  ,num_mel_filters=__UpperCamelCase  ,min_frequency=0.0  ,max_frequency=8000.0  ,sampling_rate=__UpperCamelCase  ,norm='slaney'  ,mel_scale='slaney'  ,)
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   np.ndarray:


        '''simple docstring'''


        lowercase_			:       Union[str, Any]									=			spectrogram(
            __UpperCamelCase  ,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'  ,)
        lowercase_			:       Optional[Any]									=			log_spec[:, :-1]
        lowercase_			:       int									=			np.maximum(__UpperCamelCase  ,log_spec.max() - 8.0    )
        lowercase_			:       List[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     _UpperCAmelCase     (    __UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase = 0.0    )			->   List[np.ndarray]:


        '''simple docstring'''


        if attention_mask is not None:
             lowercase_			:       Optional[int]									=			np.array(__UpperCamelCase  ,np.intaa    )
             lowercase_			:       Optional[Any]									=			[]

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

                  normed_input_values.append(__UpperCamelCase    )
        else:
             lowercase_			:       Optional[int]									=			[(x - x.mean()) / np.sqrt(x.var() + 1e-7    ) for x in input_values]

        return normed_input_values
   def __call__(    self  ,__UpperCamelCase  ,__UpperCamelCase = True  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = "max_length"  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,**__UpperCamelCase  ,)			->   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.__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.'    )

        lowercase_			:       Optional[int]									=			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}'''    )
        lowercase_			:       Union[str, Any]									=			is_batched_numpy or (
            isinstance(__UpperCamelCase  ,(list, tuple)    ) and (isinstance(raw_speech[0]  ,(np.ndarray, tuple, list)    ))
        )

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

        # always return batch
        if not is_batched:
             lowercase_			:       List[Any]									=			[np.asarray([raw_speech]    ).T]

        lowercase_			:       str									=			BatchFeature({'input_features': raw_speech}    )

        # convert into correct format for padding

        lowercase_			:       str									=			self.pad(
            __UpperCamelCase  ,padding=__UpperCamelCase  ,max_length=max_length if max_length else self.n_samples  ,truncation=__UpperCamelCase  ,pad_to_multiple_of=__UpperCamelCase  ,return_attention_mask=return_attention_mask or do_normalize  ,)

        # zero-mean and unit-variance normalization
        if do_normalize:
             lowercase_			:       int									=			self.zero_mean_unit_var_norm(
                 padded_inputs['input_features']  ,attention_mask=padded_inputs['attention_mask']  ,padding_value=self.padding_value  ,)
             lowercase_			:       Tuple									=			np.stack(padded_inputs['input_features']  ,axis=0    )

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

        lowercase_			:       List[str]									=			[self._np_extract_fbank_features(__UpperCamelCase    ) for waveform in input_features[0]]

        if isinstance(input_features[0]  ,__UpperCamelCase    ):
             lowercase_			:       Optional[Any]									=			[np.asarray(__UpperCamelCase  ,dtype=np.floataa    ) for feature in input_features]
        else:
             lowercase_			:       str									=			input_features

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

        if return_tensors is not None:
             lowercase_			:       Union[str, Any]									=			padded_inputs.convert_to_tensors(__UpperCamelCase    )

        return padded_inputs







   def     _UpperCAmelCase     (    self    )			->   Dict[str, Any]:


        '''simple docstring'''


        lowercase_			:       Dict									=			copy.deepcopy(self.__dict__    )
        lowercase_			:       Union[str, Any]									=			self.__class__.__name__
        if "mel_filters" in output:
             del output["mel_filters"]
        return output

