'''simple docstring'''
import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						(IPNDMScheduler,)
     SCREAMING_SNAKE_CASE__		   =						(('''num_inference_steps''', 50),)







     def lowerCAmelCase     (		self     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Union[str, Any]		    =  {'num_train_timesteps': 1_0_0_0}
         config.update(**_lowerCamelCase    )
         return config







     def lowerCAmelCase     (		self     ,			_lowerCamelCase=0     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Union[str, Any]		    =  dict(self.forward_default_kwargs    )
         __UpperCamelCase	:	str		    =  kwargs.pop('num_inference_steps'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	str		    =  self.dummy_sample
         __UpperCamelCase	:	List[str]		    =  0.1 * sample
         __UpperCamelCase	:	Optional[int]		    =  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

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

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

             with tempfile.TemporaryDirectory() as tmpdirname:
                 scheduler.save_config(_lowerCamelCase    )
                 __UpperCamelCase	:	Any		    =  scheduler_class.from_pretrained(_lowerCamelCase    )
                 new_scheduler.set_timesteps(_lowerCamelCase    )
                 # copy over dummy past residuals
                 __UpperCamelCase	:	int		    =  dummy_past_residuals[:]

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

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

             __UpperCamelCase	:	Dict		    =  scheduler.step(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			**_lowerCamelCase    ).prev_sample
             __UpperCamelCase	:	str		    =  new_scheduler.step(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			**_lowerCamelCase    ).prev_sample

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







     def lowerCAmelCase     (		self    ):
         pass







     def lowerCAmelCase     (		self     ,			_lowerCamelCase=0     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	int		    =  dict(self.forward_default_kwargs    )
         __UpperCamelCase	:	Tuple		    =  kwargs.pop('num_inference_steps'     ,			_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  self.dummy_sample
         __UpperCamelCase	:	Tuple		    =  0.1 * sample
         __UpperCamelCase	:	int		    =  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

         for scheduler_class in self.scheduler_classes:
             __UpperCamelCase	:	Tuple		    =  self.get_scheduler_config()
             __UpperCamelCase	:	str		    =  scheduler_class(**_lowerCamelCase    )
             scheduler.set_timesteps(_lowerCamelCase    )

             # copy over dummy past residuals (must be after setting timesteps)
             __UpperCamelCase	:	Optional[int]		    =  dummy_past_residuals[:]

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

             with tempfile.TemporaryDirectory() as tmpdirname:
                 scheduler.save_config(_lowerCamelCase    )
                 __UpperCamelCase	:	Any		    =  scheduler_class.from_pretrained(_lowerCamelCase    )
                 # copy over dummy past residuals
                 new_scheduler.set_timesteps(_lowerCamelCase    )

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

             __UpperCamelCase	:	Optional[int]		    =  scheduler.step(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			**_lowerCamelCase    ).prev_sample
             __UpperCamelCase	:	Tuple		    =  new_scheduler.step(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			**_lowerCamelCase    ).prev_sample

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

             __UpperCamelCase	:	Dict		    =  scheduler.step(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			**_lowerCamelCase    ).prev_sample
             __UpperCamelCase	:	Dict		    =  new_scheduler.step(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			**_lowerCamelCase    ).prev_sample

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







     def lowerCAmelCase     (		self     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	List[str]		    =  self.scheduler_classes[0]
         __UpperCamelCase	:	str		    =  self.get_scheduler_config(**_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  scheduler_class(**_lowerCamelCase    )

         __UpperCamelCase	:	Union[str, Any]		    =  1_0
         __UpperCamelCase	:	Any		    =  self.dummy_model()
         __UpperCamelCase	:	Tuple		    =  self.dummy_sample_deter
         scheduler.set_timesteps(_lowerCamelCase    )

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

         for i, t in enumerate(scheduler.timesteps    ):
             __UpperCamelCase	:	Dict		    =  model(_lowerCamelCase     ,			_lowerCamelCase    )
             __UpperCamelCase	:	int		    =  scheduler.step(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ).prev_sample

         return sample







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	str		    =  dict(self.forward_default_kwargs    )

         __UpperCamelCase	:	Union[str, Any]		    =  kwargs.pop('num_inference_steps'     ,			_lowerCamelCase    )

         for scheduler_class in self.scheduler_classes:
             __UpperCamelCase	:	List[Any]		    =  self.get_scheduler_config()
             __UpperCamelCase	:	Union[str, Any]		    =  scheduler_class(**_lowerCamelCase    )

             __UpperCamelCase	:	Optional[Any]		    =  self.dummy_sample
             __UpperCamelCase	:	Tuple		    =  0.1 * sample

             if num_inference_steps is not None and hasattr(_lowerCamelCase     ,			'set_timesteps'    ):
                 scheduler.set_timesteps(_lowerCamelCase    )
             elif num_inference_steps is not None and not hasattr(_lowerCamelCase     ,			'set_timesteps'    ):
                 __UpperCamelCase	:	str		    =  num_inference_steps

             # copy over dummy past residuals (must be done after set_timesteps)
             __UpperCamelCase	:	Union[str, Any]		    =  [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
             __UpperCamelCase	:	int		    =  dummy_past_residuals[:]

             __UpperCamelCase	:	Dict		    =  scheduler.timesteps[5]
             __UpperCamelCase	:	List[str]		    =  scheduler.timesteps[6]

             __UpperCamelCase	:	Optional[int]		    =  scheduler.step(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			**_lowerCamelCase    ).prev_sample
             __UpperCamelCase	:	Tuple		    =  scheduler.step(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			**_lowerCamelCase    ).prev_sample

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

             __UpperCamelCase	:	int		    =  scheduler.step(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			**_lowerCamelCase    ).prev_sample
             __UpperCamelCase	:	Dict		    =  scheduler.step(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			**_lowerCamelCase    ).prev_sample

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







     def lowerCAmelCase     (		self    ):
         for timesteps in [1_0_0, 1_0_0_0]:
             self.check_over_configs(num_train_timesteps=_lowerCamelCase     ,			time_step=_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         for t, num_inference_steps in zip([1, 5, 1_0]     ,			[1_0, 5_0, 1_0_0]    ):
             self.check_over_forward(num_inference_steps=_lowerCamelCase     ,			time_step=_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	str		    =  self.full_loop()
         __UpperCamelCase	:	Any		    =  torch.mean(torch.abs(_lowerCamelCase    )    )

         assert abs(result_mean.item() - 2_5_4_0_5_2_9    ) < 1_0




'''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"""



    __UpperCamelCase    ,					__UpperCamelCase	:	str		    =  get_dataset(_a				,     _a			)
    print('Processing...'			)
    __UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase	:	List[Any]		    =  update_image_and_anno(_a				,     _a				,     _a			)

    for index, image in enumerate(_a			):
        # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
        __UpperCamelCase	:	Tuple		    =  random_chars(3_2			)
        __UpperCamelCase	:	Tuple		    =  paths[index].split(os.sep			)[-1].rsplit('.'				,     1			)[0]
        __UpperCamelCase	:	List[str]		    =  f"""{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"""
        cva.imwrite(f"""/{file_root}.jpg"""				,     _a				,     [cva.IMWRITE_JPEG_QUALITY, 8_5]			)
        print(f"""Success {index+1}/{len(_a			)} with {file_name}"""			)
        __UpperCamelCase	:	Tuple		    =  []
        for anno in new_annos[index]:
            __UpperCamelCase	:	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  _UpperCamelCase (     _a	:	str				,     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	List[str]		    =  []
    __UpperCamelCase	:	Union[str, Any]		    =  []
    for label_file in glob.glob(os.path.join(_a				,     '*.txt'			)			):
        __UpperCamelCase	:	Any		    =  label_file.split(os.sep			)[-1].rsplit('.'				,     1			)[0]
        with open(_a			) as in_file:
            __UpperCamelCase	:	Tuple		    =  in_file.readlines()
        __UpperCamelCase	:	Dict		    =  os.path.join(_a				,     f"""{label_name}.jpg"""			)

        __UpperCamelCase	:	Optional[Any]		    =  []
        for obj_list in obj_lists:
            __UpperCamelCase	:	Optional[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  _UpperCamelCase (     _a	:	list				,     _a	:	list				,     _a	:	int = 1			):




    """simple docstring"""



    __UpperCamelCase	:	List[str]		    =  []
    __UpperCamelCase	:	str		    =  []
    __UpperCamelCase	:	str		    =  []
    for idx in range(len(_a			)			):
        __UpperCamelCase	:	Union[str, Any]		    =  []
        __UpperCamelCase	:	List[str]		    =  img_list[idx]
        path_list.append(_a			)
        __UpperCamelCase	:	Dict		    =  anno_list[idx]
        __UpperCamelCase	:	List[str]		    =  cva.imread(_a			)
        if flip_type == 1:
            __UpperCamelCase	:	List[Any]		    =  cva.flip(_a				,     _a			)
            for bbox in img_annos:
                __UpperCamelCase	:	List[str]		    =  1 - bbox[1]
                new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]			)
        elif flip_type == 0:
            __UpperCamelCase	:	Optional[Any]		    =  cva.flip(_a				,     _a			)
            for bbox in img_annos:
                __UpperCamelCase	:	List[str]		    =  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  _UpperCamelCase (     _a	:	int = 3_2			):




    """simple docstring"""



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


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



