


"""simple docstring"""






import copy
from typing import Dict, Optional

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


UpperCamelCase__							:Any       =  logging.get_logger(__name__)

UpperCamelCase__							:Union[str, Any]       =  {
    '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(     UpperCAmelCase_		):

  """simple docstring"""





  A         =       "oneformer"
  A         =       {"hidden_size": "hidden_dim"}
  def __init__(   self				,							SCREAMING_SNAKE_CASE__ = None				,							SCREAMING_SNAKE_CASE__ = 2_55				,							SCREAMING_SNAKE_CASE__ = 1_50				,							SCREAMING_SNAKE_CASE__ = 0.1				,							SCREAMING_SNAKE_CASE__ = 2.0				,							SCREAMING_SNAKE_CASE__ = 5.0				,							SCREAMING_SNAKE_CASE__ = 5.0				,							SCREAMING_SNAKE_CASE__ = 0.5				,							SCREAMING_SNAKE_CASE__ = 0.0_7				,							SCREAMING_SNAKE_CASE__ = 1_25_44				,							SCREAMING_SNAKE_CASE__ = 3.0				,							SCREAMING_SNAKE_CASE__ = 0.7_5				,							SCREAMING_SNAKE_CASE__ = 0.0_2				,							SCREAMING_SNAKE_CASE__ = 1.0				,							SCREAMING_SNAKE_CASE__ = 1E-05				,							SCREAMING_SNAKE_CASE__ = False				,							SCREAMING_SNAKE_CASE__ = True				,							SCREAMING_SNAKE_CASE__ = True				,							SCREAMING_SNAKE_CASE__ = [4, 8, 16, 32]				,							SCREAMING_SNAKE_CASE__ = 77				,							SCREAMING_SNAKE_CASE__ = 2_56				,							SCREAMING_SNAKE_CASE__ = 77				,							SCREAMING_SNAKE_CASE__ = 6				,							SCREAMING_SNAKE_CASE__ = 4_94_08				,							SCREAMING_SNAKE_CASE__ = 2				,							SCREAMING_SNAKE_CASE__ = 16				,							SCREAMING_SNAKE_CASE__ = 2_56				,							SCREAMING_SNAKE_CASE__ = 2_56				,							SCREAMING_SNAKE_CASE__ = 2_56				,							SCREAMING_SNAKE_CASE__ = 10_24				,							SCREAMING_SNAKE_CASE__ = "GN"				,							SCREAMING_SNAKE_CASE__ = 6				,							SCREAMING_SNAKE_CASE__ = 10				,							SCREAMING_SNAKE_CASE__ = True				,							SCREAMING_SNAKE_CASE__ = 8				,							SCREAMING_SNAKE_CASE__ = 0.1				,							SCREAMING_SNAKE_CASE__ = 20_48				,							SCREAMING_SNAKE_CASE__ = False				,							SCREAMING_SNAKE_CASE__ = False				,							SCREAMING_SNAKE_CASE__ = 2				,							SCREAMING_SNAKE_CASE__ = 4				,							**SCREAMING_SNAKE_CASE__				,							)			->  Dict:

     """simple docstring"""



     if backbone_config is None:
        logger.info('''`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'''      )
        _UpperCamelCase				:Optional[int]				    =  CONFIG_MAPPING['''swin'''](
            image_size=2_24				,							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=SCREAMING_SNAKE_CASE__				,							out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']				,							)
     elif isinstance(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      ):
        _UpperCamelCase				:List[str]				    =  backbone_config.get('''model_type'''      )
        _UpperCamelCase				:Tuple				    =  CONFIG_MAPPING[backbone_model_type]
        _UpperCamelCase				:Tuple				    =  config_class.from_dict(SCREAMING_SNAKE_CASE__      )

     _UpperCamelCase				:List[Any]				    =  backbone_config

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

     super().__init__(**SCREAMING_SNAKE_CASE__      )





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

     """simple docstring"""



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





"""simple docstring"""






import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve





def 							A_					(   snake_case__		)				->     str:
			_UpperCamelCase				:List[Any]				    =  np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]		)
			_UpperCamelCase				:Dict				    =  np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]		)

			_UpperCamelCase				:List[str]				    =  np.abs(img_convolve(snake_case__					,				snake_case__		)		)
			_UpperCamelCase				:Dict				    =  np.abs(img_convolve(snake_case__					,				snake_case__		)		)
			# modify the pix within [0, 255]
			_UpperCamelCase				:List[Any]				    =  dst_x * 2_55 / np.max(snake_case__		)
			_UpperCamelCase				:Tuple				    =  dst_y * 2_55 / np.max(snake_case__		)

			_UpperCamelCase				:Tuple				    =  np.sqrt((np.square(snake_case__		)) + (np.square(snake_case__		))		)
			_UpperCamelCase				:Any				    =  dst_xy * 2_55 / np.max(snake_case__		)
			_UpperCamelCase				:Optional[int]				    =  dst_xy.astype(np.uinta		)

			_UpperCamelCase				:str				    =  np.arctana(snake_case__					,				snake_case__		)
			return dst, theta


if __name__ == "__main__":
					# read original image
					UpperCamelCase__							:List[str]       =  imread("""../image_data/lena.jpg""")
					# turn image in gray scale value
					UpperCamelCase__							:Union[str, Any]       =  cvtColor(img, COLOR_BGR2GRAY)

					UpperCamelCase__      , UpperCamelCase__							:Optional[int]       =  sobel_filter(gray)

					# show result images
					imshow("""sobel filter""", sobel_grad)
					imshow("""sobel theta""", sobel_theta)
					waitKey(0)

