






# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")




class 			snake_case__   :

       '''simple docstring'''



       def __init__(							self			,						a__			,						a__			,						a__ = True			,						a__ = False    ) ->      Union[str, Any]:






            '''simple docstring'''





            __snake_case			:int          =						scheduler
            __snake_case			:str          =						optimizers if isinstance(a__			,						(list, tuple)    ) else [optimizers]
            __snake_case			:int          =						split_batches
            __snake_case			:List[Any]          =						step_with_optimizer
            __snake_case			:Optional[int]          =						GradientState()



       def 					__lowercase						(							self			,						*a__			,						**a__    ) ->      int:






            '''simple docstring'''





            if not self.step_with_optimizer:
                 # No link between scheduler and optimizer -> just step
                 self.scheduler.step(*a__			,						**a__    )
                 return

            # Otherwise, first make sure the optimizer was stepped.
            if not self.gradient_state.sync_gradients:
                 if self.gradient_state.adjust_scheduler:
                      self.scheduler._step_count += 1
                 return

            for opt in self.optimizers:
                 if opt.step_was_skipped:
                      return
            if self.split_batches:
                 # Split batches -> the training dataloader batch size is not changed so one step per training step
                 self.scheduler.step(*a__			,						**a__    )
            else:
                 # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
                 # num_processes steps per training step
                 __snake_case			:List[str]          =						AcceleratorState().num_processes
                 for _ in range(a__    ):
                      # Special case when using OneCycle and `drop_last` was not used
                      if hasattr(self.scheduler			,						"""total_steps"""    ):
                           if self.scheduler._step_count <= self.scheduler.total_steps:
                                self.scheduler.step(*a__			,						**a__    )
                      else:
                           self.scheduler.step(*a__			,						**a__    )



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            return self.scheduler.get_last_lr()



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






            '''simple docstring'''





            return self.scheduler.state_dict()



       def 					__lowercase						(							self			,						a__    ) ->      Any:






            '''simple docstring'''





            self.scheduler.load_state_dict(a__    )



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            return self.scheduler.get_lr()



       def 					__lowercase						(							self			,						*a__			,						**a__    ) ->      Optional[int]:






            '''simple docstring'''





            return self.scheduler.print_lr(*a__			,						**a__    )










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

import cva
lowerCamelCase__							=     """"""
lowerCamelCase__							=     """"""
lowerCamelCase__							=     """"""
lowerCamelCase__							=     1  # (0 is vertical, 1 is horizontal)






def   UpperCamelCase  (       ):




     '''simple docstring'''






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

     for index, image in enumerate(snake_case__  ):
          # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
          __snake_case			:str          =						random_chars(32  )
          __snake_case			:Optional[Any]          =						paths[index].split(os.sep  )[-1].rsplit("""."""	,1  )[0]
          __snake_case			:List[str]          =						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			:Dict          =						[]
     for label_file in glob.glob(os.path.join(snake_case__	,"""*.txt"""  )  ):
          __snake_case			:Tuple          =						label_file.split(os.sep  )[-1].rsplit("""."""	,1  )[0]
          with open(snake_case__  ) as in_file:
               __snake_case			:Optional[int]          =						in_file.readlines()
          __snake_case			:Any          =						os.path.join(snake_case__	,f'''{label_name}.jpg'''  )

          __snake_case			:List[str]          =						[]
          for obj_list in obj_lists:
               __snake_case			:int          =						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			:int          =						[]
     for idx in range(len(snake_case__  )  ):
          __snake_case			:Optional[int]          =						[]
          __snake_case			:List[str]          =						img_list[idx]
          path_list.append(snake_case__  )
          __snake_case			:Union[str, Any]          =						anno_list[idx]
          __snake_case			:List[Any]          =						cva.imread(snake_case__  )
          if flip_type == 1:
               __snake_case			:Optional[Any]          =						cva.flip(snake_case__	,snake_case__  )
               for bbox in img_annos:
                    __snake_case			:Union[str, Any]          =						1 - bbox[1]
                    new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]  )
          elif flip_type == 0:
               __snake_case			:Union[str, Any]          =						cva.flip(snake_case__	,snake_case__  )
               for bbox in img_annos:
                    __snake_case			:Optional[Any]          =						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			:Optional[Any]          =						ascii_lowercase + digits
     return "".join(random.choice(snake_case__  ) for _ in range(snake_case__  )  )


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


