



'''simple docstring'''





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

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




def lowerCAmelCase						():



     """simple docstring"""



     _a						,					_a											=						get_dataset(__A						,			__A)
     print('''Processing...''')
     _a						,					_a						,					_a											=						update_image_and_anno(__A						,			__A						,			__A)

     for index, image in enumerate(__A):
          # 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'''						,			__A						,			[cva.IMWRITE_JPEG_QUALITY, 85])
          print(F'''Success {index+1}/{len(__A)} 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(__A)
          with open(F'''/{file_root}.txt'''						,			'''w''') as outfile:
               outfile.write('''\n'''.join(line for line in annos_list))




def lowerCAmelCase						(__A						,			__A):



     """simple docstring"""



     _a											=						[]
     _a											=						[]
     for label_file in glob.glob(os.path.join(__A						,			'''*.txt''')):
          _a											=						label_file.split(os.sep)[-1].rsplit('''.'''						,			1)[0]
          with open(__A) as in_file:
               _a											=						in_file.readlines()
          _a											=						os.path.join(__A						,			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(__A)
          labels.append(__A)
     return img_paths, labels




def lowerCAmelCase						(__A						,			__A						,			__A = 1):



     """simple docstring"""



     _a											=						[]
     _a											=						[]
     _a											=						[]
     for idx in range(len(__A)):
          _a											=						[]
          _a											=						img_list[idx]
          path_list.append(__A)
          _a											=						anno_list[idx]
          _a											=						cva.imread(__A)
          if flip_type == 1:
               _a											=						cva.flip(__A						,			__A)
               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(__A						,			__A)
               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(__A)
          new_imgs_list.append(__A)
     return new_imgs_list, new_annos_lists, path_list




def lowerCAmelCase						(__A = 32):



     """simple docstring"""



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


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








'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import MobileNetVaImageProcessor






class __A    (						unittest.TestCase		):



 '''simple docstring'''







 def __init__(self				,     A				,     A=7				,     A=3				,     A=18				,     A=30				,     A=400				,     A=True				,     A=None				,     A=True				,     A=None				,     )					->  Any:


      """simple docstring"""



      _a											=						size if size is not None else {'''shortest_edge''': 20}
      _a											=						crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
      _a											=						parent
      _a											=						batch_size
      _a											=						num_channels
      _a											=						image_size
      _a											=						min_resolution
      _a											=						max_resolution
      _a											=						do_resize
      _a											=						size
      _a											=						do_center_crop
      _a											=						crop_size



 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
      }






@require_torch
@require_vision
class __A    (						A     ,     unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase					: Tuple    = MobileNetVaImageProcessor if is_vision_available() else None



 def       a__						(self )					->  int:


      """simple docstring"""



      _a											=						MobileNetVaImageProcessingTester(self )



 @property
 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      return self.image_processor_tester.prepare_image_processor_dict()



 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						self.image_processing_class(**self.image_processor_dict )
      self.assertTrue(hasattr(A				,     '''do_resize''' ) )
      self.assertTrue(hasattr(A				,     '''size''' ) )
      self.assertTrue(hasattr(A				,     '''do_center_crop''' ) )
      self.assertTrue(hasattr(A				,     '''crop_size''' ) )



 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      _a											=						self.image_processing_class.from_dict(self.image_processor_dict )
      self.assertEqual(image_processor.size				,     {'''shortest_edge''': 20} )
      self.assertEqual(image_processor.crop_size				,     {'''height''': 18, '''width''': 18} )

      _a											=						self.image_processing_class.from_dict(self.image_processor_dict				,     size=42				,     crop_size=84 )
      self.assertEqual(image_processor.size				,     {'''shortest_edge''': 42} )
      self.assertEqual(image_processor.crop_size				,     {'''height''': 84, '''width''': 84} )



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


      """simple docstring"""



      pass



 def       a__						(self )					->  Any:


      """simple docstring"""



      _a											=						self.image_processing_class(**self.image_processor_dict )
      # create random PIL images
      _a											=						prepare_image_inputs(self.image_processor_tester				,     equal_resolution=A )
      for image in image_inputs:
           self.assertIsInstance(A				,     Image.Image )

      # Test not batched input
      _a											=						image_processing(image_inputs[0]				,     return_tensors='''pt''' ).pixel_values
      self.assertEqual(
          encoded_images.shape				,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )				,     )

      # Test batched
      _a											=						image_processing(A				,     return_tensors='''pt''' ).pixel_values
      self.assertEqual(
          encoded_images.shape				,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )				,     )



 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      _a											=						self.image_processing_class(**self.image_processor_dict )
      # create random numpy tensors
      _a											=						prepare_image_inputs(self.image_processor_tester				,     equal_resolution=A				,     numpify=A )
      for image in image_inputs:
           self.assertIsInstance(A				,     np.ndarray )

      # Test not batched input
      _a											=						image_processing(image_inputs[0]				,     return_tensors='''pt''' ).pixel_values
      self.assertEqual(
          encoded_images.shape				,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )				,     )

      # Test batched
      _a											=						image_processing(A				,     return_tensors='''pt''' ).pixel_values
      self.assertEqual(
          encoded_images.shape				,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )				,     )



 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      _a											=						self.image_processing_class(**self.image_processor_dict )
      # create random PyTorch tensors
      _a											=						prepare_image_inputs(self.image_processor_tester				,     equal_resolution=A				,     torchify=A )
      for image in image_inputs:
           self.assertIsInstance(A				,     torch.Tensor )

      # Test not batched input
      _a											=						image_processing(image_inputs[0]				,     return_tensors='''pt''' ).pixel_values
      self.assertEqual(
          encoded_images.shape				,     (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )				,     )

      # Test batched
      _a											=						image_processing(A				,     return_tensors='''pt''' ).pixel_values
      self.assertEqual(
          encoded_images.shape				,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )				,     )



