




'''simple docstring'''



import glob
import os
import random
from string import ascii_lowercase, digits

import cva
UpperCamelCase_    	=				""
UpperCamelCase_    	=				""
UpperCamelCase_    	=				""
UpperCamelCase_    	=				1  # (0 is vertical, 1 is horizontal)




def   lowercase__(			):

      """simple docstring"""






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

      for index, image in enumerate(__UpperCamelCase    ):
            # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
            SCREAMING_SNAKE_CASE   :      Tuple    					=						random_chars(32    )
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						paths[index].split(os.sep    )[-1].rsplit('.'      ,1    )[0]
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						f"{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"
            cva.imwrite(f"/{file_root}.jpg"      ,__UpperCamelCase      ,[cva.IMWRITE_JPEG_QUALITY, 85]    )
            print(f"Success {index+1}/{len(__UpperCamelCase    )} with {file_name}"    )
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[]
            for anno in new_annos[index]:
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						f"{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"
                  annos_list.append(__UpperCamelCase    )
            with open(f"/{file_root}.txt"      ,'w'    ) as outfile:
                  outfile.write('\n'.join(line for line in annos_list    )    )




def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					str    ):

      """simple docstring"""






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

            SCREAMING_SNAKE_CASE   :      str    					=						[]
            for obj_list in obj_lists:
                  SCREAMING_SNAKE_CASE   :      str    					=						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(__UpperCamelCase    )
            labels.append(__UpperCamelCase    )
      return img_paths, labels




def   lowercase__(			__UpperCamelCase:					list      ,__UpperCamelCase:					list      ,__UpperCamelCase:					int = 1    ):

      """simple docstring"""






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




def   lowercase__(			__UpperCamelCase:					int = 32    ):

      """simple docstring"""






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


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








'''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


UpperCamelCase_    	=				logging.get_logger(__name__)






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				str        =						['''audio_values''', '''audio_mask''']







       def __init__(      self,							A=2_048,							A=1,							A=[16, 16],							A=128,							A=44_100,							A=86,							A=2_048,							A=0.0,							**A,							):







             '''simple docstring'''







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

             SCREAMING_SNAKE_CASE   :      str    					=						spectrogram_length
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						num_channels
             SCREAMING_SNAKE_CASE   :      List[str]    					=						patch_size
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						feature_size // self.patch_size[1]
             SCREAMING_SNAKE_CASE   :      Dict    					=						n_fft
             SCREAMING_SNAKE_CASE   :      Tuple    					=						sampling_rate // hop_length_to_sampling_rate
             SCREAMING_SNAKE_CASE   :      str    					=						sampling_rate
             SCREAMING_SNAKE_CASE   :      int    					=						padding_value
             SCREAMING_SNAKE_CASE   :      Any    					=						mel_filter_bank(
                 num_frequency_bins=1 + n_fft // 2,							num_mel_filters=A,							min_frequency=0.0,							max_frequency=2_20_50.0,							sampling_rate=A,							norm='slaney',							mel_scale='slaney',							).T







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						spectrogram(
                 A,							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=80.0,							)
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						log_spec[:, :-1]
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						log_spec - 20.0
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						np.clip(log_spec / 40.0,							-2.0,							0.0  ) + 1.0
             return log_spec







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







             '''simple docstring'''







             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.'  )

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						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}"  )
             SCREAMING_SNAKE_CASE   :      int    					=						is_batched_numpy or (
                 isinstance(A,							(list, tuple)  ) and (isinstance(raw_speech[0],							(np.ndarray, tuple, list)  ))
             )
             if is_batched:
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[np.asarray([speech],							dtype=np.floataa  ).T for speech in raw_speech]
             elif not is_batched and not isinstance(A,							np.ndarray  ):
                   SCREAMING_SNAKE_CASE   :      Any    					=						np.asarray(A,							dtype=np.floataa  )
             elif isinstance(A,							np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						raw_speech.astype(np.floataa  )
             # always return batch
             if not is_batched:
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[np.asarray([raw_speech]  ).T]

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

             # Create audio attention mask
             SCREAMING_SNAKE_CASE   :      Tuple    					=						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:
                   SCREAMING_SNAKE_CASE   :      List[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
                   ]
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						np.array(A  ).astype(np.floataa  )

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

             # return as BatchFeature
             if return_attention_mask:
                   SCREAMING_SNAKE_CASE   :      Any    					=						{'audio_values': padded_audio_features, 'audio_mask': audio_mask}
             else:
                   SCREAMING_SNAKE_CASE   :      Dict    					=						{'audio_values': padded_audio_features}

             SCREAMING_SNAKE_CASE   :      str    					=						BatchFeature(data=A,							tensor_type=A  )
             return encoded_inputs


