"""simple docstring"""




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

import cva
_a      :		List[str]           =   ''
_a      :		str           =   ''
_a      :		Optional[Any]           =   ''
_a      :		str           =   1  # (0 is vertical, 1 is horizontal)





def 							SCREAMING_SNAKE_CASE     (			)       ->      None:
  _lowerCAmelCase			,	_lowerCAmelCase						:       List[str]  			=     get_dataset(_lowerCamelCase			,_lowerCamelCase	)
  print("""Processing..."""	)
  _lowerCAmelCase			,	_lowerCAmelCase			,	_lowerCAmelCase						:       List[str]  			=     update_image_and_anno(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)

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





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

    _lowerCAmelCase						:       Optional[Any]  			=     []
    for obj_list in obj_lists:
      _lowerCAmelCase						:       Dict  			=     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(_lowerCamelCase	)
    labels.append(_lowerCamelCase	)
  return img_paths, labels





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





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int = 32	)       ->      str:
  assert number_char > 1, "The number of character should greater than 1"
  _lowerCAmelCase						:       Optional[int]  			=     ascii_lowercase + digits
  return "".join(random.choice(_lowerCamelCase	) for _ in range(_lowerCamelCase	)	)


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





"""simple docstring"""




import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class 						__A							( unittest.TestCase		):






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     """ylacombe/bark-small"""
       _lowerCAmelCase						:       Optional[Any]  			=     tempfile.mkdtemp()
       _lowerCAmelCase						:       int  			=     """en_speaker_1"""
       _lowerCAmelCase						:       List[Any]  			=     """This is a test string"""
       _lowerCAmelCase						:       Any  			=     """speaker_embeddings_path.json"""
       _lowerCAmelCase						:       List[Any]  			=     """speaker_embeddings"""






     def     __A  (		self				,     **a__  ):
       return AutoTokenizer.from_pretrained(self.checkpoint				,     **a__  )






     def     __A  (		self  ):
       shutil.rmtree(self.tmpdirname  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     self.get_tokenizer()

       _lowerCAmelCase						:       int  			=     BarkProcessor(tokenizer=a__  )

       processor.save_pretrained(self.tmpdirname  )
       _lowerCAmelCase						:       str  			=     BarkProcessor.from_pretrained(self.tmpdirname  )

       self.assertEqual(processor.tokenizer.get_vocab()				,     tokenizer.get_vocab()  )






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint				,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				,     )
       processor.save_pretrained(
           self.tmpdirname				,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				,     speaker_embeddings_directory=self.speaker_embeddings_directory				,     )

       _lowerCAmelCase						:       Tuple  			=     self.get_tokenizer(bos_token="""(BOS)"""				,     eos_token="""(EOS)"""  )

       _lowerCAmelCase						:       List[Any]  			=     BarkProcessor.from_pretrained(
           self.tmpdirname				,     self.speaker_embeddings_dict_path				,     bos_token="""(BOS)"""				,     eos_token="""(EOS)"""				,     )

       self.assertEqual(processor.tokenizer.get_vocab()				,     tokenizer_add_kwargs.get_vocab()  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint				,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				,     )

       _lowerCAmelCase						:       Union[str, Any]  			=     35
       _lowerCAmelCase						:       Union[str, Any]  			=     2
       _lowerCAmelCase						:       Optional[int]  			=     8

       _lowerCAmelCase						:       Dict  			=     {
           """semantic_prompt""": np.ones(a__  ),
           """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)  ),
           """fine_prompt""": np.ones((nb_codebooks_total, seq_len)  ),
       }

       # test providing already loaded voice_preset
       _lowerCAmelCase						:       Dict  			=     processor(text=self.input_string				,     voice_preset=a__  )

       _lowerCAmelCase						:       Tuple  			=     inputs["""history_prompt"""]
       for key in voice_preset:
         self.assertListEqual(voice_preset[key].tolist()				,     processed_voice_preset.get(a__				,     np.array([]  )  ).tolist()  )

       # test loading voice preset from npz file
       _lowerCAmelCase						:       List[Any]  			=     os.path.join(self.tmpdirname				,     """file.npz"""  )
       np.savez(a__				,     **a__  )
       _lowerCAmelCase						:       List[Any]  			=     processor(text=self.input_string				,     voice_preset=a__  )
       _lowerCAmelCase						:       Optional[int]  			=     inputs["""history_prompt"""]

       for key in voice_preset:
         self.assertListEqual(voice_preset[key].tolist()				,     processed_voice_preset.get(a__				,     np.array([]  )  ).tolist()  )

       # test loading voice preset from the hub
       _lowerCAmelCase						:       str  			=     processor(text=self.input_string				,     voice_preset=self.voice_preset  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     self.get_tokenizer()

       _lowerCAmelCase						:       List[Any]  			=     BarkProcessor(tokenizer=a__  )

       _lowerCAmelCase						:       Dict  			=     processor(text=self.input_string  )

       _lowerCAmelCase						:       Tuple  			=     tokenizer(
           self.input_string				,     padding="""max_length"""				,     max_length=256				,     add_special_tokens=a__				,     return_attention_mask=a__				,     return_token_type_ids=a__				,     )

       for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]				,     encoded_processor[key].squeeze().tolist()  )




