




"""simple docstring"""


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

import cva
A_      			=       ''''''
A_      			=       ''''''
A_      			=       ''''''
A_      			=       1  # (0 is vertical, 1 is horizontal)


def       UpperCAmelCase__ ():
  """simple docstring"""

  _snake_case	,      _snake_case							:	Any			=						get_dataset(snake_case__	,     snake_case__					)
  print("""Processing..."""					)
  _snake_case	,      _snake_case	,      _snake_case							:	List[Any]			=						update_image_and_anno(snake_case__	,     snake_case__	,     snake_case__					)

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


def       UpperCAmelCase__ (snake_case__							:      str	,     snake_case__							:      str					):
  """simple docstring"""

  _snake_case							:	List[str]			=						[]
  _snake_case							:	List[str]			=						[]
  for label_file in glob.glob(os.path.join(snake_case__	,     """*.txt"""					)					):
    _snake_case							:	int			=						label_file.split(os.sep					)[-1].rsplit("""."""	,     1					)[0]
    with open(snake_case__					) as in_file:
      _snake_case							:	Union[str, Any]			=						in_file.readlines()
    _snake_case							:	int			=						os.path.join(snake_case__	,     F"{label_name}.jpg"					)

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


def       UpperCAmelCase__ (snake_case__							:      list	,     snake_case__							:      list	,     snake_case__							:      int = 1					):
  """simple docstring"""

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


def       UpperCAmelCase__ (snake_case__							:      int = 32					):
  """simple docstring"""

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


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








"""simple docstring"""


import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class       lowercase(  __a				):







 '''simple docstring'''





 lowercase__				      =      (IPNDMScheduler,)
 lowercase__				      =      (("num_inference_steps", 50),)


 def 				UpperCamelCase_     (       self:	Union[str, Any],   **a_:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						{"""num_train_timesteps""": 1_000}
   config.update(**a_	)
   return config


 def 				UpperCamelCase_     (       self:	Tuple,   a_:	Optional[int]=0,   **a_:	int	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						dict(self.forward_default_kwargs	)
   _snake_case							:	Optional[Any]			=						kwargs.pop("""num_inference_steps""",   a_	)
   _snake_case							:	Optional[Any]			=						self.dummy_sample
   _snake_case							:	Dict			=						0.1 * sample
   _snake_case							:	Any			=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

   for scheduler_class in self.scheduler_classes:
     _snake_case							:	int			=						self.get_scheduler_config(**a_	)
     _snake_case							:	Dict			=						scheduler_class(**a_	)
     scheduler.set_timesteps(a_	)
     # copy over dummy past residuals
     _snake_case							:	int			=						dummy_past_residuals[:]

     if time_step is None:
       _snake_case							:	Union[str, Any]			=						scheduler.timesteps[len(scheduler.timesteps	) // 2]

     with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(a_	)
       _snake_case							:	Tuple			=						scheduler_class.from_pretrained(a_	)
       new_scheduler.set_timesteps(a_	)
       # copy over dummy past residuals
       _snake_case							:	Optional[Any]			=						dummy_past_residuals[:]

     _snake_case							:	List[Any]			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	str			=						new_scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"

     _snake_case							:	Optional[Any]			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	Optional[int]			=						new_scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	str,   a_:	Any=0,   **a_:	Tuple	):




   '''simple docstring'''



   _snake_case							:	str			=						dict(self.forward_default_kwargs	)
   _snake_case							:	List[Any]			=						kwargs.pop("""num_inference_steps""",   a_	)
   _snake_case							:	Optional[int]			=						self.dummy_sample
   _snake_case							:	Tuple			=						0.1 * sample
   _snake_case							:	Optional[int]			=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

   for scheduler_class in self.scheduler_classes:
     _snake_case							:	Any			=						self.get_scheduler_config()
     _snake_case							:	Tuple			=						scheduler_class(**a_	)
     scheduler.set_timesteps(a_	)

     # copy over dummy past residuals (must be after setting timesteps)
     _snake_case							:	Union[str, Any]			=						dummy_past_residuals[:]

     if time_step is None:
       _snake_case							:	Tuple			=						scheduler.timesteps[len(scheduler.timesteps	) // 2]

     with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(a_	)
       _snake_case							:	List[str]			=						scheduler_class.from_pretrained(a_	)
       # copy over dummy past residuals
       new_scheduler.set_timesteps(a_	)

       # copy over dummy past residual (must be after setting timesteps)
       _snake_case							:	List[str]			=						dummy_past_residuals[:]

     _snake_case							:	str			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	Any			=						new_scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"

     _snake_case							:	str			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	int			=						new_scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"


 def 				UpperCamelCase_     (       self:	List[Any],   **a_:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						self.scheduler_classes[0]
   _snake_case							:	Any			=						self.get_scheduler_config(**a_	)
   _snake_case							:	List[Any]			=						scheduler_class(**a_	)

   _snake_case							:	Union[str, Any]			=						10
   _snake_case							:	Union[str, Any]			=						self.dummy_model()
   _snake_case							:	List[Any]			=						self.dummy_sample_deter
   scheduler.set_timesteps(a_	)

   for i, t in enumerate(scheduler.timesteps	):
     _snake_case							:	Optional[Any]			=						model(a_,   a_	)
     _snake_case							:	Any			=						scheduler.step(a_,   a_,   a_	).prev_sample

   for i, t in enumerate(scheduler.timesteps	):
     _snake_case							:	Union[str, Any]			=						model(a_,   a_	)
     _snake_case							:	Any			=						scheduler.step(a_,   a_,   a_	).prev_sample

   return sample


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   _snake_case							:	str			=						dict(self.forward_default_kwargs	)

   _snake_case							:	int			=						kwargs.pop("""num_inference_steps""",   a_	)

   for scheduler_class in self.scheduler_classes:
     _snake_case							:	Union[str, Any]			=						self.get_scheduler_config()
     _snake_case							:	Tuple			=						scheduler_class(**a_	)

     _snake_case							:	Dict			=						self.dummy_sample
     _snake_case							:	List[str]			=						0.1 * sample

     if num_inference_steps is not None and hasattr(a_,   """set_timesteps"""	):
       scheduler.set_timesteps(a_	)
     elif num_inference_steps is not None and not hasattr(a_,   """set_timesteps"""	):
       _snake_case							:	Dict			=						num_inference_steps

     # copy over dummy past residuals (must be done after set_timesteps)
     _snake_case							:	Union[str, Any]			=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
     _snake_case							:	List[str]			=						dummy_past_residuals[:]

     _snake_case							:	Optional[int]			=						scheduler.timesteps[5]
     _snake_case							:	Optional[Any]			=						scheduler.timesteps[6]

     _snake_case							:	str			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	List[str]			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     self.assertEqual(output_a.shape,   sample.shape	)
     self.assertEqual(output_a.shape,   output_a.shape	)

     _snake_case							:	Any			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	Any			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     self.assertEqual(output_a.shape,   sample.shape	)
     self.assertEqual(output_a.shape,   output_a.shape	)


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   for timesteps in [100, 1_000]:
     self.check_over_configs(num_train_timesteps=a_,   time_step=a_	)


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   for t, num_inference_steps in zip([1, 5, 10],   [10, 50, 100]	):
     self.check_over_forward(num_inference_steps=a_,   time_step=a_	)


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.full_loop()
   _snake_case							:	Optional[int]			=						torch.mean(torch.abs(a_	)	)

   assert abs(result_mean.item() - 2_540_529	) < 10


