





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

import cva
lowercase				:	Optional[Any]      						=  """"""
lowercase				:	int      						=  """"""
lowercase				:	List[Any]      						=  """"""
lowercase				:	Optional[int]      						=  1  # (0 is vertical, 1 is horizontal)



def A_     (		)  ->		None:
       a__						:     str			=    get_dataset(A__		,				A__    )
       print('Processing...'    )
       a__						:     Tuple			=    update_image_and_anno(A__		,				A__		,				A__    )

       for index, image in enumerate(A__    ):
              # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
              a__						:     int			=    random_chars(32    )
              a__						:     Optional[Any]			=    paths[index].split(os.sep    )[-1].rsplit('.'		,				1    )[0]
              a__						:     Optional[int]			=    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__						:     List[str]			=    []
              for anno in new_annos[index]:
                     a__						:     Union[str, Any]			=    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 A_     (		A__		,				A__    )  ->		tuple[list, list]:
       a__						:     int			=    []
       a__						:     int			=    []
       for label_file in glob.glob(os.path.join(A__		,				'*.txt'    )    ):
              a__						:     Optional[Any]			=    label_file.split(os.sep    )[-1].rsplit('.'		,				1    )[0]
              with open(A__    ) as in_file:
                     a__						:     Tuple			=    in_file.readlines()
              a__						:     Dict			=    os.path.join(A__		,				F'{label_name}.jpg'    )

              a__						:     int			=    []
              for obj_list in obj_lists:
                     a__						:     Union[str, Any]			=    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 A_     (		A__		,				A__		,				A__ = 1    )  ->		tuple[list, list, list]:
       a__						:     Optional[int]			=    []
       a__						:     Any			=    []
       a__						:     Dict			=    []
       for idx in range(len(A__    )    ):
              a__						:     Optional[int]			=    []
              a__						:     Optional[Any]			=    img_list[idx]
              path_list.append(A__    )
              a__						:     Union[str, Any]			=    anno_list[idx]
              a__						:     List[str]			=    cva.imread(A__    )
              if flip_type == 1:
                     a__						:     List[str]			=    cva.flip(A__		,				A__    )
                     for bbox in img_annos:
                            a__						:     Optional[Any]			=    1 - bbox[1]
                            new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]    )
              elif flip_type == 0:
                     a__						:     Optional[Any]			=    cva.flip(A__		,				A__    )
                     for bbox in img_annos:
                            a__						:     Optional[int]			=    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 A_     (		A__ = 32    )  ->		str:
       assert number_char > 1, "The number of character should greater than 1"
       a__						:     Optional[int]			=    ascii_lowercase + digits
       return "".join(random.choice(A__    ) for _ in range(A__    )    )


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









import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
    import tensorflow as tf
    from tensorflow.python.eager import context
    from tensorflow.python.framework import ops

    from transformers import GradientAccumulator, create_optimizer





@require_tf
class 			A__   (							unittest.TestCase     ):




   """simple docstring"""



   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase)      ->						List[str]:







          '''simple docstring'''

          self.assertEqual(len(lowercase) ,  len(lowercase))
          for a, b in zip(lowercase ,  lowercase):
                 self.assertAlmostEqual(lowercase ,  lowercase ,  delta=lowercase)

   def 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

          a__						:     Tuple			=    GradientAccumulator()
          accumulator([tf.constant([1.0, 2.0])])
          accumulator([tf.constant([-2.0, 1.0])])
          accumulator([tf.constant([-1.0, 2.0])])
          with self.assertRaises(lowercase):
                 accumulator([tf.constant([1.0, 1.0]), tf.constant([2.0, 2.0])])
          self.assertEqual(accumulator.step ,  3)
          self.assertEqual(len(accumulator.gradients) ,  1)
          self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() ,  [-2.0, 5.0] ,  tol=1e-2)
          accumulator.reset()
          self.assertEqual(accumulator.step ,  0)
          self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() ,  [0.0, 0.0] ,  tol=1e-2)

   def 							__lowercase		( self)      ->						List[Any]:







          '''simple docstring'''

          a__						:     Tuple			=    None
          ops.enable_eager_execution_internal()
          a__						:     Optional[int]			=    tf.config.list_physical_devices('CPU')
          if len(lowercase) == 1:
                 tf.config.set_logical_device_configuration(
                     physical_devices[0] ,  [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()])
          a__						:     int			=    tf.config.list_logical_devices(device_type='CPU')
          a__						:     Union[str, Any]			=    tf.distribute.MirroredStrategy(devices=devices[:2])

          with strategy.scope():
                 a__						:     str			=    GradientAccumulator()
                 a__						:     Tuple			=    tf.Variable([4.0, 3.0])
                 a__			,							a__						:     Tuple			=    create_optimizer(5e-5 ,  10 ,  5)
                 a__						:     Tuple			=    tf.Variable([0.0, 0.0] ,  trainable=lowercase)

          def accumulate_on_replica(lowercase):
                 accumulator([gradient])

          def apply_on_replica():
                 optimizer.apply_gradients(list(zip(accumulator.gradients ,  [variable])))

          @tf.function
          def accumulate(lowercase ,  lowercase):
                 with strategy.scope():
                        a__						:     Union[str, Any]			=    strategy.experimental_local_results(lowercase)
                        local_variables[0].assign(lowercase)
                        local_variables[1].assign(lowercase)
                        strategy.run(lowercase ,  args=(gradient_placeholder,))

          @tf.function
          def apply_grad():
                 with strategy.scope():
                        strategy.run(lowercase)

          def _check_local_values(lowercase ,  lowercase):
                 a__						:     Union[str, Any]			=    strategy.experimental_local_results(accumulator._gradients[0])
                 self.assertListAlmostEqual(values[0].value() ,  lowercase ,  tol=1e-2)
                 self.assertListAlmostEqual(values[1].value() ,  lowercase ,  tol=1e-2)

          accumulate([1.0, 2.0] ,  [-1.0, 1.0])
          accumulate([3.0, -1.0] ,  [-1.0, -1.0])
          accumulate([-2.0, 2.0] ,  [3.0, -2.0])
          self.assertEqual(accumulator.step ,  3)
          _check_local_values([2.0, 3.0] ,  [1.0, -2.0])
          apply_grad()
          self.assertListAlmostEqual(variable.value() ,  [4.0, 3.0] ,  tol=1e-2)
          accumulator.reset()
          self.assertEqual(accumulator.step ,  0)
          _check_local_values([0.0, 0.0] ,  [0.0, 0.0])


