






import copy
from typing import Dict, Optional

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


SCREAMING_SNAKE_CASE								=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE								=  {
    'shi-labs/oneformer_ade20k_swin_tiny': (
        'https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}
class 							A_      (  __lowercase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     Optional[int]	=   "oneformer"
 _SCREAMING_SNAKE_CASE							:     Tuple	=   {"hidden_size": "hidden_dim"}


 def __init__(		self							,					_A = None							,					_A = 255							,					_A = 150							,					_A = 0.1							,					_A = 2.0							,					_A = 5.0							,					_A = 5.0							,					_A = 0.5							,					_A = 0.07							,					_A = 12544							,					_A = 3.0							,					_A = 0.75							,					_A = 0.02							,					_A = 1.0							,					_A = 1e-05							,					_A = False							,					_A = True							,					_A = True							,					_A = [4, 8, 16, 32]							,					_A = 77							,					_A = 256							,					_A = 77							,					_A = 6							,					_A = 49408							,					_A = 2							,					_A = 16							,					_A = 256							,					_A = 256							,					_A = 256							,					_A = 1024							,					_A = "GN"							,					_A = 6							,					_A = 10							,					_A = True							,					_A = 8							,					_A = 0.1							,					_A = 2048							,					_A = False							,					_A = False							,					_A = 2							,					_A = 4							,					**_A							,					)	->						str:

     """simple docstring"""



     if backbone_config is None:
         logger.info('''`backbone_config` is unset. Initializing the config with the default `Swin` backbone.''')
         _UpperCAmelCase			: Any     					=			CONFIG_MAPPING['''swin'''](
             image_size=224							,					in_channels=3							,					patch_size=4							,					embed_dim=96							,					depths=[2, 2, 6, 2]							,					num_heads=[3, 6, 12, 24]							,					window_size=7							,					drop_path_rate=0.3							,					use_absolute_embeddings=_A							,					out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']							,					)
     elif isinstance(_A							,					_A):
         _UpperCAmelCase			: List[Any]     					=			backbone_config.get('''model_type''')
         _UpperCAmelCase			: Optional[Any]     					=			CONFIG_MAPPING[backbone_model_type]
         _UpperCAmelCase			: Dict     					=			config_class.from_dict(_A)

     _UpperCAmelCase			: Optional[Any]     					=			backbone_config

     _UpperCAmelCase			: Tuple     					=			ignore_value
     _UpperCAmelCase			: List[Any]     					=			num_queries
     _UpperCAmelCase			: Optional[int]     					=			no_object_weight
     _UpperCAmelCase			: Dict     					=			class_weight
     _UpperCAmelCase			: str     					=			mask_weight
     _UpperCAmelCase			: Optional[Any]     					=			dice_weight
     _UpperCAmelCase			: Tuple     					=			contrastive_weight
     _UpperCAmelCase			: Dict     					=			contrastive_temperature
     _UpperCAmelCase			: Optional[Any]     					=			train_num_points
     _UpperCAmelCase			: Optional[int]     					=			oversample_ratio
     _UpperCAmelCase			: List[Any]     					=			importance_sample_ratio
     _UpperCAmelCase			: str     					=			init_std
     _UpperCAmelCase			: Any     					=			init_xavier_std
     _UpperCAmelCase			: Optional[int]     					=			layer_norm_eps
     _UpperCAmelCase			: Dict     					=			is_training
     _UpperCAmelCase			: Tuple     					=			use_auxiliary_loss
     _UpperCAmelCase			: Dict     					=			output_auxiliary_logits
     _UpperCAmelCase			: str     					=			strides
     _UpperCAmelCase			: List[Any]     					=			task_seq_len
     _UpperCAmelCase			: Dict     					=			text_encoder_width
     _UpperCAmelCase			: int     					=			text_encoder_context_length
     _UpperCAmelCase			: Any     					=			text_encoder_num_layers
     _UpperCAmelCase			: List[Any]     					=			text_encoder_vocab_size
     _UpperCAmelCase			: Dict     					=			text_encoder_proj_layers
     _UpperCAmelCase			: str     					=			text_encoder_n_ctx
     _UpperCAmelCase			: Optional[int]     					=			conv_dim
     _UpperCAmelCase			: Optional[int]     					=			mask_dim
     _UpperCAmelCase			: Tuple     					=			hidden_dim
     _UpperCAmelCase			: Optional[Any]     					=			encoder_feedforward_dim
     _UpperCAmelCase			: int     					=			norm
     _UpperCAmelCase			: Optional[int]     					=			encoder_layers
     _UpperCAmelCase			: Dict     					=			decoder_layers
     _UpperCAmelCase			: Tuple     					=			use_task_norm
     _UpperCAmelCase			: Dict     					=			num_attention_heads
     _UpperCAmelCase			: Any     					=			dropout
     _UpperCAmelCase			: Dict     					=			dim_feedforward
     _UpperCAmelCase			: Optional[int]     					=			pre_norm
     _UpperCAmelCase			: Any     					=			enforce_input_proj
     _UpperCAmelCase			: List[Any]     					=			query_dec_layers
     _UpperCAmelCase			: Optional[Any]     					=			common_stride
     _UpperCAmelCase			: int     					=			decoder_layers

     super().__init__(**_A)


 def 	snake_case__			(		self)	->						Dict[str, any]:

     """simple docstring"""



     _UpperCAmelCase			: Tuple     					=			copy.deepcopy(self.__dict__)
     _UpperCAmelCase			: str     					=			self.backbone_config.to_dict()
     _UpperCAmelCase			: int     					=			self.__class__.model_type
     return output












from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import divide, inta, multiply, ravel, sort, zeros_like

def        _lowerCamelCase		(  __A      :							str						,						__A      :							List[str]=3	) ->   List[Any]:
    _UpperCAmelCase			: Tuple     					=			int(mask / 2	)
    # copy image size
    _UpperCAmelCase			: Any     					=			zeros_like(__A	)
    for i in range(__A						,						gray_img.shape[0] - bd	):
        for j in range(__A						,						gray_img.shape[1] - bd	):
            # get mask according with mask
            _UpperCAmelCase			: Any     					=			ravel(gray_img[i - bd : i + bd + 1, j - bd : j + bd + 1]	)
            # calculate mask median
            _UpperCAmelCase			: List[str]     					=			sort(__A	)[inta(divide((multiply(__A						,						__A	))						,						2	) + 1	)]
            _UpperCAmelCase			: int     					=			median
    return median_img


if __name__ == "__main__":
  # read original image
  SCREAMING_SNAKE_CASE								=  imread('../image_data/lena.jpg')
  # turn image in gray scale value
  SCREAMING_SNAKE_CASE								=  cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  SCREAMING_SNAKE_CASE								=  median_filter(gray, 3)
  SCREAMING_SNAKE_CASE								=  median_filter(gray, 5)

  # show result images
  imshow('median filter with 3x3 mask', medianaxa)
  imshow('median filter with 5x5 mask', medianaxa)
  waitKey(0)




