




"""simple docstring"""



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

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







def   _SCREAMING_SNAKE_CASE       (							)						->		None:
 A__,				A__	=    get_dataset(lowercase_      ,			lowercase_						)
 print("Processing..."						)
 A__,				A__,				A__	=    update_image_and_anno(lowercase_      ,			lowercase_      ,			lowercase_						)

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







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_						)						->		tuple[list, list]:
 A__	=    []
 A__	=    []
 for label_file in glob.glob(os.path.join(lowercase_      ,			"*.txt"						)						):
  A__	=    label_file.split(os.sep						)[-1].rsplit("."      ,			1						)[0]
  with open(lowercase_						) as in_file:
   A__	=    in_file.readlines()
  A__	=    os.path.join(lowercase_      ,			f"""{label_name}.jpg"""						)

  A__	=    []
  for obj_list in obj_lists:
   A__	=    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(lowercase_						)
  labels.append(lowercase_						)
 return img_paths, labels







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_ = 1						)						->		tuple[list, list, list]:
 A__	=    []
 A__	=    []
 A__	=    []
 for idx in range(len(lowercase_						)						):
  A__	=    []
  A__	=    img_list[idx]
  path_list.append(lowercase_						)
  A__	=    anno_list[idx]
  A__	=    cva.imread(lowercase_						)
  if flip_type == 1:
   A__	=    cva.flip(lowercase_      ,			lowercase_						)
   for bbox in img_annos:
    A__	=    1 - bbox[1]
    new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]						)
  elif flip_type == 0:
   A__	=    cva.flip(lowercase_      ,			lowercase_						)
   for bbox in img_annos:
    A__	=    1 - bbox[2]
    new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]						)
  new_annos_lists.append(lowercase_						)
  new_imgs_list.append(lowercase_						)
 return new_imgs_list, new_annos_lists, path_list







def   _SCREAMING_SNAKE_CASE       (							lowercase_ = 32						)						->		str:
 assert number_char > 1, "The number of character should greater than 1"
 A__	=    ascii_lowercase + digits
 return "".join(random.choice(lowercase_						) for _ in range(lowercase_						)						)


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











"""simple docstring"""



import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
      from transformers import TvltImageProcessor

if is_speech_available():
      from transformers import TvltFeatureExtractor

from transformers import TvltProcessor




@require_torch
class      UpperCAmelCase_  (						unittest.TestCase							):




    def 							__magic_name__							(   self						:	str				)				->							Dict:




     '''simple docstring'''

     A__	=    "ZinengTang/tvlt-base"
     A__	=    tempfile.mkdtemp()




    def 							__magic_name__							(   self						:	int     ,			**snake_case_						:	Dict				)				->							Union[str, Any]:




     '''simple docstring'''

     return TvltImageProcessor.from_pretrained(self.checkpoint     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	Optional[int]     ,			**snake_case_						:	str				)				->							List[str]:




     '''simple docstring'''

     return TvltFeatureExtractor.from_pretrained(self.checkpoint     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	Optional[int]				)				->							str:




     '''simple docstring'''

     shutil.rmtree(self.tmpdirname				)




    def 							__magic_name__							(   self						:	Tuple				)				->							Any:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_feature_extractor()

     A__	=    TvltProcessor(image_processor=snake_case_     ,			feature_extractor=snake_case_				)
     processor.save_pretrained(self.tmpdirname				)
     A__	=    TvltProcessor.from_pretrained(self.tmpdirname				)

     self.assertIsInstance(processor.feature_extractor     ,			snake_case_				)
     self.assertIsInstance(processor.image_processor     ,			snake_case_				)




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_feature_extractor()

     A__	=    TvltProcessor(image_processor=snake_case_     ,			feature_extractor=snake_case_				)

     A__	=    np.ones([12_000]				)

     A__	=    feature_extractor(snake_case_     ,			return_tensors="np"				)
     A__	=    processor(audio=snake_case_     ,			return_tensors="np"				)

     for key in audio_dict.keys():
      self.assertAlmostEqual(audio_dict[key].sum()     ,			input_processor[key].sum()     ,			delta=1e-2				)




    def 							__magic_name__							(   self						:	Tuple				)				->							Tuple:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_feature_extractor()

     A__	=    TvltProcessor(image_processor=snake_case_     ,			feature_extractor=snake_case_				)

     A__	=    np.ones([3, 224, 224]				)

     A__	=    image_processor(snake_case_     ,			return_tensors="np"				)
     A__	=    processor(images=snake_case_     ,			return_tensors="np"				)

     for key in image_dict.keys():
      self.assertAlmostEqual(image_dict[key].sum()     ,			input_processor[key].sum()     ,			delta=1e-2				)




    def 							__magic_name__							(   self						:	Tuple				)				->							Optional[int]:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_feature_extractor()

     A__	=    TvltProcessor(image_processor=snake_case_     ,			feature_extractor=snake_case_				)

     A__	=    np.ones([12_000]				)
     A__	=    np.ones([3, 224, 224]				)

     A__	=    processor(audio=snake_case_     ,			images=snake_case_				)

     self.assertListEqual(list(inputs.keys()				)     ,			["audio_values", "audio_mask", "pixel_values", "pixel_mask"]				)

     # test if it raises when no input is passed
     with pytest.raises(snake_case_				):
      processor()




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Tuple:




     '''simple docstring'''

     A__	=    self.get_image_processor()
     A__	=    self.get_feature_extractor()

     A__	=    TvltProcessor(image_processor=snake_case_     ,			feature_extractor=snake_case_				)

     self.assertListEqual(
         processor.model_input_names     ,			image_processor.model_input_names + feature_extractor.model_input_names     ,			msg="`processor` and `image_processor`+`feature_extractor` model input names do not match"     ,			)





