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

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

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_  ,       lowerCamelCase_	  =					get_dataset(__UpperCamelCase			,							__UpperCamelCase  )
      print('Processing...'  )
      lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					update_image_and_anno(__UpperCamelCase			,							__UpperCamelCase			,							__UpperCamelCase  )

      for index, image in enumerate(__UpperCamelCase  ):
            # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
            lowerCamelCase_	  =					random_chars(32  )
            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"""			,							__UpperCamelCase			,							[cva.IMWRITE_JPEG_QUALITY, 85]  )
            print(f"""Success {index+1}/{len(__UpperCamelCase  )} 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(__UpperCamelCase  )
            with open(f"""/{file_root}.txt"""			,							'w'  ) as outfile:
                  outfile.write('\n'.join(line for line in annos_list  )  )

def     _SCREAMING_SNAKE_CASE    (							lowercase			: Tuple			,							lowercase			: str  ):


      '''simple docstring'''



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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[Any]			,							lowercase			: Any			,							lowercase			: Optional[Any] = 1  ):


      '''simple docstring'''



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

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str = 32  ):


      '''simple docstring'''



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


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        a__				(       self      :						Optional[int]		,							A_      :						Tuple		,							A_      :						str		,							A_      :						int						)     ->			Any:

       """simple docstring"""






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

 def        a__				(       self      :						int						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					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(A_						):
             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        a__				(       self      :						List[Any]						)     ->			List[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					None
       ops.enable_eager_execution_internal()
       lowerCamelCase_	  =					tf.config.list_physical_devices('CPU'						)
       if len(A_						) == 1:
             tf.config.set_logical_device_configuration(
                 physical_devices[0]		,							[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]						)
       lowerCamelCase_	  =					tf.config.list_logical_devices(device_type='CPU'						)
       lowerCamelCase_	  =					tf.distribute.MirroredStrategy(devices=devices[:2]						)

       with strategy.scope():
             lowerCamelCase_	  =					GradientAccumulator()
             lowerCamelCase_	  =					tf.Variable([4.0, 3.0]						)
             lowerCamelCase_  ,       lowerCamelCase_	  =					create_optimizer(5E-5		,							10		,							5						)
             lowerCamelCase_	  =					tf.Variable([0.0, 0.0]		,							trainable=A_						)

       def accumulate_on_replica(A_      :						Any						):
             accumulator([gradient]						)

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

       @tf.function
       def accumulate(A_      :						List[Any]		,							A_      :						Tuple						):
             with strategy.scope():
                   lowerCamelCase_	  =					strategy.experimental_local_results(A_						)
                   local_variables[0].assign(A_						)
                   local_variables[1].assign(A_						)
                   strategy.run(A_		,							args=(gradient_placeholder,)						)

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

       def _check_local_values(A_      :						List[Any]		,							A_      :						str						):
             lowerCamelCase_	  =					strategy.experimental_local_results(accumulator._gradients[0]						)
             self.assertListAlmostEqual(values[0].value()		,							A_		,							tol=1E-2						)
             self.assertListAlmostEqual(values[1].value()		,							A_		,							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]						)



