'''simple docstring'''
from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


a=  logging.get_logger(__name__)






class 			__lowercase		(						UpperCamelCase_						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						['''audio_values''', '''audio_mask''']







     def __init__(		self     ,			_lowerCamelCase=2_0_4_8     ,			_lowerCamelCase=1     ,			_lowerCamelCase=[1_6, 1_6]     ,			_lowerCamelCase=1_2_8     ,			_lowerCamelCase=4_4_1_0_0     ,			_lowerCamelCase=8_6     ,			_lowerCamelCase=2_0_4_8     ,			_lowerCamelCase=0.0     ,			**_lowerCamelCase     ,			):
         super().__init__(
             feature_size=_lowerCamelCase     ,			sampling_rate=_lowerCamelCase     ,			padding_value=_lowerCamelCase     ,			**_lowerCamelCase     ,			)

         __UpperCamelCase	:	Optional[int]		    =  spectrogram_length
         __UpperCamelCase	:	Optional[int]		    =  num_channels
         __UpperCamelCase	:	Any		    =  patch_size
         __UpperCamelCase	:	Any		    =  feature_size // self.patch_size[1]
         __UpperCamelCase	:	List[str]		    =  n_fft
         __UpperCamelCase	:	Optional[Any]		    =  sampling_rate // hop_length_to_sampling_rate
         __UpperCamelCase	:	List[str]		    =  sampling_rate
         __UpperCamelCase	:	Any		    =  padding_value
         __UpperCamelCase	:	Dict		    =  mel_filter_bank(
             num_frequency_bins=1 + n_fft // 2     ,			num_mel_filters=_lowerCamelCase     ,			min_frequency=0.0     ,			max_frequency=2_2_0_5_0.0     ,			sampling_rate=_lowerCamelCase     ,			norm='slaney'     ,			mel_scale='slaney'     ,			).T







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	str		    =  spectrogram(
             _lowerCamelCase     ,			window_function(self.n_fft     ,			'hann'    )     ,			frame_length=self.n_fft     ,			hop_length=self.hop_length     ,			power=2.0     ,			mel_filters=self.mel_filters.T     ,			log_mel='dB'     ,			db_range=8_0.0     ,			)
         __UpperCamelCase	:	Optional[Any]		    =  log_spec[:, :-1]
         __UpperCamelCase	:	Any		    =  log_spec - 2_0.0
         __UpperCamelCase	:	Any		    =  np.clip(log_spec / 4_0.0     ,			-2.0     ,			0.0    ) + 1.0
         return log_spec







     def __call__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None     ,			_lowerCamelCase = True     ,			_lowerCamelCase = None     ,			_lowerCamelCase = False     ,			_lowerCamelCase = False     ,			**_lowerCamelCase     ,			):

         if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     'This feature extractor is set to support sampling rate'
                     f""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
                     f""" 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	:	int		    =  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	:	Optional[int]		    =  is_batched_numpy or (
             isinstance(_lowerCamelCase     ,			(list, tuple)    ) and (isinstance(raw_speech[0]     ,			(np.ndarray, tuple, list)    ))
         )
         if is_batched:
             __UpperCamelCase	:	List[str]		    =  [np.asarray([speech]     ,			dtype=np.floataa    ).T for speech in raw_speech]
         elif not is_batched and not isinstance(_lowerCamelCase     ,			np.ndarray    ):
             __UpperCamelCase	:	List[Any]		    =  np.asarray(_lowerCamelCase     ,			dtype=np.floataa    )
         elif isinstance(_lowerCamelCase     ,			np.ndarray    ) and raw_speech.dtype is np.dtype(np.floataa    ):
             __UpperCamelCase	:	str		    =  raw_speech.astype(np.floataa    )
         # always return batch
         if not is_batched:
             __UpperCamelCase	:	List[Any]		    =  [np.asarray([raw_speech]    ).T]

         # Convert audio signals to log mel spectrograms, truncate by time axis
         __UpperCamelCase	:	Optional[int]		    =  [
             self._np_extract_fbank_features(waveform.squeeze()    ).T[: self.spectrogram_length] for waveform in raw_speech
         ]
         if isinstance(audio_features[0]     ,			_lowerCamelCase    ):
             __UpperCamelCase	:	Optional[Any]		    =  [np.asarray(_lowerCamelCase     ,			dtype=np.floataa    ) for feature in audio_features]

         # Create audio attention mask
         __UpperCamelCase	:	Optional[int]		    =  max(
             [ceil(feature.shape[0] / self.patch_size[0]    ) * self.freq_len for feature in audio_features]    )  # The maximum number of audio patches in a batch
         if return_attention_mask:
             __UpperCamelCase	:	Union[str, Any]		    =  [
                 (ceil(feature.shape[0] / self.patch_size[0]    ) * self.freq_len) * [1]
                 + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]    ) * self.freq_len) * [0]
                 for feature in audio_features
             ]
             __UpperCamelCase	:	List[Any]		    =  np.array(_lowerCamelCase    ).astype(np.floataa    )

         # convert into correct format for padding
         __UpperCamelCase	:	Optional[Any]		    =  max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
         __UpperCamelCase	:	int		    =  np.ones([len(_lowerCamelCase    ), 1, max_time_len, self.feature_size]    ).astype(np.floataa    )
         __UpperCamelCase	:	List[str]		    =  padded_audio_features * self.padding_value
         for i in range(len(_lowerCamelCase    )    ):
             __UpperCamelCase	:	str		    =  audio_features[i]
             __UpperCamelCase	:	str		    =  feature

         # return as BatchFeature
         if return_attention_mask:
             __UpperCamelCase	:	List[Any]		    =  {'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
         else:
             __UpperCamelCase	:	Union[str, Any]		    =  {'''audio_values''': padded_audio_features}

         __UpperCamelCase	:	Dict		    =  BatchFeature(data=_lowerCamelCase     ,			tensor_type=_lowerCamelCase    )
         return encoded_inputs




'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits

import cva
a=  ''''''
a=  ''''''
a=  ''''''
a=  1  # (0 is vertical, 1 is horizontal)







def  _UpperCamelCase (     ):




    """simple docstring"""



    __UpperCamelCase    ,					__UpperCamelCase	:	str		    =  get_dataset(_a				,     _a			)
    print('Processing...'			)
    __UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase	:	List[Any]		    =  update_image_and_anno(_a				,     _a				,     _a			)

    for index, image in enumerate(_a			):
        # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
        __UpperCamelCase	:	Tuple		    =  random_chars(3_2			)
        __UpperCamelCase	:	Tuple		    =  paths[index].split(os.sep			)[-1].rsplit('.'				,     1			)[0]
        __UpperCamelCase	:	List[str]		    =  f"""{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"""
        cva.imwrite(f"""/{file_root}.jpg"""				,     _a				,     [cva.IMWRITE_JPEG_QUALITY, 8_5]			)
        print(f"""Success {index+1}/{len(_a			)} with {file_name}"""			)
        __UpperCamelCase	:	Tuple		    =  []
        for anno in new_annos[index]:
            __UpperCamelCase	:	Any		    =  f"""{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"""
            annos_list.append(_a			)
        with open(f"""/{file_root}.txt"""				,     'w'			) as outfile:
            outfile.write('\n'.join(line for line in annos_list			)			)







def  _UpperCamelCase (     _a	:	str				,     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	List[str]		    =  []
    __UpperCamelCase	:	Union[str, Any]		    =  []
    for label_file in glob.glob(os.path.join(_a				,     '*.txt'			)			):
        __UpperCamelCase	:	Any		    =  label_file.split(os.sep			)[-1].rsplit('.'				,     1			)[0]
        with open(_a			) as in_file:
            __UpperCamelCase	:	Tuple		    =  in_file.readlines()
        __UpperCamelCase	:	Dict		    =  os.path.join(_a				,     f"""{label_name}.jpg"""			)

        __UpperCamelCase	:	Optional[Any]		    =  []
        for obj_list in obj_lists:
            __UpperCamelCase	:	Optional[Any]		    =  obj_list.rstrip('\n'			).split(' '			)
            boxes.append(
                [
                    int(obj[0]			),
                    float(obj[1]			),
                    float(obj[2]			),
                    float(obj[3]			),
                    float(obj[4]			),
                ]			)
        if not boxes:
            continue
        img_paths.append(_a			)
        labels.append(_a			)
    return img_paths, labels







def  _UpperCamelCase (     _a	:	list				,     _a	:	list				,     _a	:	int = 1			):




    """simple docstring"""



    __UpperCamelCase	:	List[str]		    =  []
    __UpperCamelCase	:	str		    =  []
    __UpperCamelCase	:	str		    =  []
    for idx in range(len(_a			)			):
        __UpperCamelCase	:	Union[str, Any]		    =  []
        __UpperCamelCase	:	List[str]		    =  img_list[idx]
        path_list.append(_a			)
        __UpperCamelCase	:	Dict		    =  anno_list[idx]
        __UpperCamelCase	:	List[str]		    =  cva.imread(_a			)
        if flip_type == 1:
            __UpperCamelCase	:	List[Any]		    =  cva.flip(_a				,     _a			)
            for bbox in img_annos:
                __UpperCamelCase	:	List[str]		    =  1 - bbox[1]
                new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]			)
        elif flip_type == 0:
            __UpperCamelCase	:	Optional[Any]		    =  cva.flip(_a				,     _a			)
            for bbox in img_annos:
                __UpperCamelCase	:	List[str]		    =  1 - bbox[2]
                new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]			)
        new_annos_lists.append(_a			)
        new_imgs_list.append(_a			)
    return new_imgs_list, new_annos_lists, path_list







def  _UpperCamelCase (     _a	:	int = 3_2			):




    """simple docstring"""



    assert number_char > 1, "The number of character should greater than 1"
    __UpperCamelCase	:	Optional[Any]		    =  ascii_lowercase + digits
    return "".join(random.choice(_a			) for _ in range(_a			)			)


if __name__ == "__main__":
  main()
  print('''DONE ✅''')



