





'''simple docstring'''



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

import cva
__magic_name__   :						List[str]    =			""""""
__magic_name__   :						Any    =			""""""
__magic_name__   :						List[str]    =			""""""
__magic_name__   :						Union[str, Any]    =			1  # (0 is vertical, 1 is horizontal)



def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case		,		_snake_case     		=				get_dataset(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  print("Processing..."       )
  _snake_case		,		_snake_case		,		_snake_case     		=				update_image_and_anno(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )

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



def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				[]
  _snake_case     		=				[]
  for label_file in glob.glob(os.path.join(SCREAMING_SNAKE_CASE__					,	"*.txt"       )       ):
    _snake_case     		=				label_file.split(os.sep       )[-1].rsplit("."					,	1       )[0]
    with open(SCREAMING_SNAKE_CASE__       ) as in_file:
      _snake_case     		=				in_file.readlines()
    _snake_case     		=				os.path.join(SCREAMING_SNAKE_CASE__					,	f'''{label_name}.jpg'''       )

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



def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__ = 1       ):







  '''simple docstring'''



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



def 				snake_case_      (		SCREAMING_SNAKE_CASE__ = 32       ):







  '''simple docstring'''



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


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









'''simple docstring'''



import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
 import torch
 from torch import nn

 from transformers import (
     Adafactor,
     AdamW,
     get_constant_schedule,
     get_constant_schedule_with_warmup,
     get_cosine_schedule_with_warmup,
     get_cosine_with_hard_restarts_schedule_with_warmup,
     get_inverse_sqrt_schedule,
     get_linear_schedule_with_warmup,
     get_polynomial_decay_schedule_with_warmup,
 )




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=10       ):







  '''simple docstring'''



  _snake_case     		=				[]
  for _ in range(SCREAMING_SNAKE_CASE__       ):
    lrs.append(scheduler.get_lr()[0]       )
    scheduler.step()
  return lrs




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=10       ):







  '''simple docstring'''



  _snake_case     		=				[]
  for step in range(SCREAMING_SNAKE_CASE__       ):
    lrs.append(scheduler.get_lr()[0]       )
    scheduler.step()
    if step == num_steps // 2:
      with tempfile.TemporaryDirectory() as tmpdirname:
        _snake_case     		=				os.path.join(SCREAMING_SNAKE_CASE__					,	"schedule.bin"       )
        torch.save(scheduler.state_dict()					,	SCREAMING_SNAKE_CASE__       )

        _snake_case     		=				torch.load(SCREAMING_SNAKE_CASE__       )
        scheduler.load_state_dict(SCREAMING_SNAKE_CASE__       )
  return lrs


@require_torch
class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				torch.tensor([0.1, -0.2, -0.1]						,   requires_grad=lowerCamelCase	)
       _snake_case     		=				torch.tensor([0.4, 0.2, -0.5]	)
       _snake_case     		=				nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       _snake_case     		=				AdamW(params=[w]						,   lr=2e-1						,   weight_decay=0.0	)
       for _ in range(100	):
         _snake_case     		=				criterion(lowerCamelCase						,   lowerCamelCase	)
         loss.backward()
         optimizer.step()
         w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
         w.grad.zero_()
       self.assertListAlmostEqual(w.tolist()						,   [0.4, 0.2, -0.5]						,   tol=1e-2	)






     def 	UpperCamelCase(					self	):
       _snake_case     		=				torch.tensor([0.1, -0.2, -0.1]						,   requires_grad=lowerCamelCase	)
       _snake_case     		=				torch.tensor([0.4, 0.2, -0.5]	)
       _snake_case     		=				nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       _snake_case     		=				Adafactor(
           params=[w]						,   lr=1e-2						,   eps=(1e-30, 1e-3)						,   clip_threshold=1.0						,   decay_rate=-0.8						,   betaa=lowerCamelCase						,   weight_decay=0.0						,   relative_step=lowerCamelCase						,   scale_parameter=lowerCamelCase						,   warmup_init=lowerCamelCase						,   )
       for _ in range(1_000	):
         _snake_case     		=				criterion(lowerCamelCase						,   lowerCamelCase	)
         loss.backward()
         optimizer.step()
         w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
         w.grad.zero_()
       self.assertListAlmostEqual(w.tolist()						,   [0.4, 0.2, -0.5]						,   tol=1e-2	)







@require_torch
class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Any      =   nn.Linear(50					,    50			) if is_torch_available() else None
     UpperCAmelCase__       :						str      =   AdamW(m.parameters()					,    lr=10.0			) if is_torch_available() else None
     UpperCAmelCase__       :						Optional[Any]      =   10

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase=None	):
       self.assertEqual(len(lowerCamelCase	)						,   len(lowerCamelCase	)	)
       for a, b in zip(lowerCamelCase						,   lowerCamelCase	):
         self.assertAlmostEqual(lowerCamelCase						,   lowerCamelCase						,   delta=lowerCamelCase						,   msg=lowerCamelCase	)






     def 	UpperCamelCase(					self	):
       _snake_case     		=				{"num_warmup_steps": 2, "num_training_steps": 10}
       # schedulers doct format
       # function: (sched_args_dict, expected_learning_rates)
       _snake_case     		=				{
           get_constant_schedule: ({}, [10.0] * self.num_steps),
           get_constant_schedule_with_warmup: (
               {"num_warmup_steps": 4},
               [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
           ),
           get_linear_schedule_with_warmup: (
               {**common_kwargs},
               [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
           ),
           get_cosine_schedule_with_warmup: (
               {**common_kwargs},
               [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
           ),
           get_cosine_with_hard_restarts_schedule_with_warmup: (
               {**common_kwargs, "num_cycles": 2},
               [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
           ),
           get_polynomial_decay_schedule_with_warmup: (
               {**common_kwargs, "power": 2.0, "lr_end": 1e-7},
               [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
           ),
           get_inverse_sqrt_schedule: (
               {"num_warmup_steps": 2},
               [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
           ),
       }

       for scheduler_func, data in scheds.items():
         _snake_case		,		_snake_case     		=				data

         _snake_case     		=				scheduler_func(self.optimizer						,   **lowerCamelCase	)
         self.assertEqual(len([scheduler.get_lr()[0]]	)						,   1	)
         _snake_case     		=				unwrap_schedule(lowerCamelCase						,   self.num_steps	)
         self.assertListAlmostEqual(
             lowerCamelCase						,   lowerCamelCase						,   tol=1e-2						,   msg=F'''failed for {scheduler_func} in normal scheduler'''						,   )

         _snake_case     		=				scheduler_func(self.optimizer						,   **lowerCamelCase	)
         if scheduler_func.__name__ != "get_constant_schedule":
           LambdaScheduleWrapper.wrap_scheduler(lowerCamelCase	)  # wrap to test picklability of the schedule
         _snake_case     		=				unwrap_and_save_reload_schedule(lowerCamelCase						,   self.num_steps	)
         self.assertListEqual(lowerCamelCase						,   lowerCamelCase						,   msg=F'''failed for {scheduler_func} in save and reload'''	)







class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase	):
       _snake_case     		=				fn

     def __call__(					self						,   *lowerCamelCase						,   **lowerCamelCase	):
       return self.fn(*lowerCamelCase						,   **lowerCamelCase	)






     @classmethod
     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				list(map(self						,   scheduler.lr_lambdas	)	)


