



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 							UpperCAmelCase__		(				)->None:
      _lowerCAmelCase			,   _lowerCAmelCase 			=  get_dataset(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)
      print('''Processing...'''				)
      _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase 			=  update_image_and_anno(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)

      for index, image in enumerate(_SCREAMING_SNAKE_CASE				):
            # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
            _lowerCAmelCase 			=  random_chars(3_2				)
            _lowerCAmelCase 			=  paths[index].split(os.sep				)[-1].rsplit('''.'''     ,				1				)[0]
            _lowerCAmelCase 			=  f'''{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'''
            cva.imwrite(f'''/{file_root}.jpg'''     ,				_SCREAMING_SNAKE_CASE     ,				[cva.IMWRITE_JPEG_QUALITY, 8_5]				)
            print(f'''Success {index+1}/{len(_SCREAMING_SNAKE_CASE				)} with {file_name}'''				)
            _lowerCAmelCase 			=  []
            for anno in new_annos[index]:
                  _lowerCAmelCase 			=  f'''{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'''
                  annos_list.append(_SCREAMING_SNAKE_CASE				)
            with open(f'''/{file_root}.txt'''     ,				'''w'''				) as outfile:
                  outfile.write('''\n'''.join(line for line in annos_list				)				)
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	str     ,				_SCREAMING_SNAKE_CASE   :	str				)->tuple[list, list]:
      _lowerCAmelCase 			=  []
      _lowerCAmelCase 			=  []
      for label_file in glob.glob(os.path.join(_SCREAMING_SNAKE_CASE     ,				'''*.txt'''				)				):
            _lowerCAmelCase 			=  label_file.split(os.sep				)[-1].rsplit('''.'''     ,				1				)[0]
            with open(_SCREAMING_SNAKE_CASE				) as in_file:
                  _lowerCAmelCase 			=  in_file.readlines()
            _lowerCAmelCase 			=  os.path.join(_SCREAMING_SNAKE_CASE     ,				f'''{label_name}.jpg'''				)

            _lowerCAmelCase 			=  []
            for obj_list in obj_lists:
                  _lowerCAmelCase 			=  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(_SCREAMING_SNAKE_CASE				)
            labels.append(_SCREAMING_SNAKE_CASE				)
      return img_paths, labels
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	list     ,				_SCREAMING_SNAKE_CASE   :	list     ,				_SCREAMING_SNAKE_CASE   :	int = 1				)->tuple[list, list, list]:
      _lowerCAmelCase 			=  []
      _lowerCAmelCase 			=  []
      _lowerCAmelCase 			=  []
      for idx in range(len(_SCREAMING_SNAKE_CASE				)				):
            _lowerCAmelCase 			=  []
            _lowerCAmelCase 			=  img_list[idx]
            path_list.append(_SCREAMING_SNAKE_CASE				)
            _lowerCAmelCase 			=  anno_list[idx]
            _lowerCAmelCase 			=  cva.imread(_SCREAMING_SNAKE_CASE				)
            if flip_type == 1:
                  _lowerCAmelCase 			=  cva.flip(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)
                  for bbox in img_annos:
                        _lowerCAmelCase 			=  1 - bbox[1]
                        new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]				)
            elif flip_type == 0:
                  _lowerCAmelCase 			=  cva.flip(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)
                  for bbox in img_annos:
                        _lowerCAmelCase 			=  1 - bbox[2]
                        new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]				)
            new_annos_lists.append(_SCREAMING_SNAKE_CASE				)
            new_imgs_list.append(_SCREAMING_SNAKE_CASE				)
      return new_imgs_list, new_annos_lists, path_list
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	int = 3_2				)->str:
      assert number_char > 1, "The number of character should greater than 1"
      _lowerCAmelCase 			=  ascii_lowercase + digits
      return "".join(random.choice(_SCREAMING_SNAKE_CASE				) for _ in range(_SCREAMING_SNAKE_CASE				)				)


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




from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class 		UpperCAmelCase						(						snake_case_				):
   SCREAMING_SNAKE_CASE__							=     '''ClapFeatureExtractor'''
   SCREAMING_SNAKE_CASE__							=     ('''RobertaTokenizer''', '''RobertaTokenizerFast''')





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         super().__init__(_lowerCAmelCase       ,     _lowerCAmelCase			)





   def __call__(	self       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=None       ,     _lowerCAmelCase=None       ,     **_lowerCAmelCase			):
         _lowerCAmelCase 			=  kwargs.pop('''sampling_rate'''       ,     _lowerCAmelCase			)

         if text is None and audios is None:
               raise ValueError('''You have to specify either text or audios. Both cannot be none.'''			)

         if text is not None:
               _lowerCAmelCase 			=  self.tokenizer(_lowerCAmelCase       ,     return_tensors=_lowerCAmelCase       ,     **_lowerCAmelCase			)

         if audios is not None:
               _lowerCAmelCase 			=  self.feature_extractor(
                   _lowerCAmelCase       ,     sampling_rate=_lowerCAmelCase       ,     return_tensors=_lowerCAmelCase       ,     **_lowerCAmelCase			)

         if text is not None and audios is not None:
               _lowerCAmelCase 			=  audio_features.input_features
               return encoding
         elif text is not None:
               return encoding
         else:
               return BatchEncoding(data=dict(**_lowerCAmelCase			)       ,     tensor_type=_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         return self.tokenizer.batch_decode(*_lowerCAmelCase       ,     **_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     *_lowerCAmelCase       ,     **_lowerCAmelCase			):
         return self.tokenizer.decode(*_lowerCAmelCase       ,     **_lowerCAmelCase			)





   @property
   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self.tokenizer.model_input_names
         _lowerCAmelCase 			=  self.feature_extractor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names			)			)