
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase__							=     logging.get_logger(__name__)

lowerCamelCase__							=     {
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}




class 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				List[Any] 				= "deta"
       lowerCamelCase							:				Optional[Any] 				= {
           "hidden_size": "d_model",
           "num_attention_heads": "encoder_attention_heads",
       }



       def __init__(							self			,						a__=None			,						a__=9_00			,						a__=20_48			,						a__=6			,						a__=20_48			,						a__=8			,						a__=6			,						a__=10_24			,						a__=8			,						a__=0.0			,						a__=True			,						a__="relu"			,						a__=2_56			,						a__=0.1			,						a__=0.0			,						a__=0.0			,						a__=0.02			,						a__=1.0			,						a__=True			,						a__=False			,						a__="sine"			,						a__=5			,						a__=4			,						a__=4			,						a__=True			,						a__=3_00			,						a__=True			,						a__=True			,						a__=1			,						a__=5			,						a__=2			,						a__=1			,						a__=1			,						a__=5			,						a__=2			,						a__=0.1			,						a__=0.25			,						**a__			,						) ->      Any:






            '''simple docstring'''





            if backbone_config is None:
                 logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""    )
                 __snake_case			:List[Any]          =						CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]    )
            else:
                 if isinstance(a__			,						a__    ):
                      __snake_case			:List[Any]          =						backbone_config.pop("""model_type"""    )
                      __snake_case			:List[str]          =						CONFIG_MAPPING[backbone_model_type]
                      __snake_case			:Union[str, Any]          =						config_class.from_dict(a__    )

            __snake_case			:Optional[int]          =						backbone_config
            __snake_case			:Union[str, Any]          =						num_queries
            __snake_case			:Optional[Any]          =						max_position_embeddings
            __snake_case			:List[Any]          =						d_model
            __snake_case			:Dict          =						encoder_ffn_dim
            __snake_case			:Any          =						encoder_layers
            __snake_case			:str          =						encoder_attention_heads
            __snake_case			:Union[str, Any]          =						decoder_ffn_dim
            __snake_case			:Any          =						decoder_layers
            __snake_case			:Optional[int]          =						decoder_attention_heads
            __snake_case			:str          =						dropout
            __snake_case			:Any          =						attention_dropout
            __snake_case			:Any          =						activation_dropout
            __snake_case			:Dict          =						activation_function
            __snake_case			:Tuple          =						init_std
            __snake_case			:Tuple          =						init_xavier_std
            __snake_case			:Optional[Any]          =						encoder_layerdrop
            __snake_case			:List[str]          =						auxiliary_loss
            __snake_case			:str          =						position_embedding_type
            # deformable attributes
            __snake_case			:Union[str, Any]          =						num_feature_levels
            __snake_case			:List[str]          =						encoder_n_points
            __snake_case			:Dict          =						decoder_n_points
            __snake_case			:List[Any]          =						two_stage
            __snake_case			:Optional[Any]          =						two_stage_num_proposals
            __snake_case			:str          =						with_box_refine
            __snake_case			:List[str]          =						assign_first_stage
            if two_stage is True and with_box_refine is False:
                 raise ValueError("""If two_stage is True, with_box_refine must be True."""    )
            # Hungarian matcher
            __snake_case			:Optional[int]          =						class_cost
            __snake_case			:Optional[int]          =						bbox_cost
            __snake_case			:Tuple          =						giou_cost
            # Loss coefficients
            __snake_case			:int          =						mask_loss_coefficient
            __snake_case			:List[Any]          =						dice_loss_coefficient
            __snake_case			:str          =						bbox_loss_coefficient
            __snake_case			:Optional[int]          =						giou_loss_coefficient
            __snake_case			:int          =						eos_coefficient
            __snake_case			:Any          =						focal_alpha
            super().__init__(is_encoder_decoder=a__			,						**a__    )



       @property
       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            return self.encoder_attention_heads



       @property
       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            return self.d_model



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						copy.deepcopy(self.__dict__    )
            __snake_case			:Union[str, Any]          =						self.backbone_config.to_dict()
            __snake_case			:Union[str, Any]          =						self.__class__.model_type
            return output










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 ✅""")


