




'''simple docstring'''





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
  from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


a		:  Optional[int]   			=			logging.get_logger(__name__)

a		:  Any   			=			{
    '''microsoft/deberta-base''': '''https://huggingface.co/microsoft/deberta-base/resolve/main/config.json''',
    '''microsoft/deberta-large''': '''https://huggingface.co/microsoft/deberta-large/resolve/main/config.json''',
    '''microsoft/deberta-xlarge''': '''https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json''',
    '''microsoft/deberta-base-mnli''': '''https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json''',
    '''microsoft/deberta-large-mnli''': '''https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json''',
    '''microsoft/deberta-xlarge-mnli''': '''https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json''',
}




class     UpperCamelCase					(  snake_case__					):
  a    :List[str]      					=				'deberta'



  def __init__( self		,    __lowerCAmelCase=5_0265		,    __lowerCAmelCase=768		,    __lowerCAmelCase=12		,    __lowerCAmelCase=12		,    __lowerCAmelCase=3072		,    __lowerCAmelCase="gelu"		,    __lowerCAmelCase=0.1		,    __lowerCAmelCase=0.1		,    __lowerCAmelCase=512		,    __lowerCAmelCase=0		,    __lowerCAmelCase=0.02		,    __lowerCAmelCase=1e-7		,    __lowerCAmelCase=False		,    __lowerCAmelCase=-1		,    __lowerCAmelCase=0		,    __lowerCAmelCase=True		,    __lowerCAmelCase=None		,    __lowerCAmelCase=0		,    __lowerCAmelCase="gelu"		,    **__lowerCAmelCase		,    ):







      '''simple docstring'''


      super().__init__(**__lowerCAmelCase   )

      _a							:       str       					=			hidden_size
      _a							:       Optional[Any]       					=			num_hidden_layers
      _a							:       List[str]       					=			num_attention_heads
      _a							:       str       					=			intermediate_size
      _a							:       List[str]       					=			hidden_act
      _a							:       int       					=			hidden_dropout_prob
      _a							:       List[str]       					=			attention_probs_dropout_prob
      _a							:       List[str]       					=			max_position_embeddings
      _a							:       Tuple       					=			type_vocab_size
      _a							:       List[Any]       					=			initializer_range
      _a							:       Optional[Any]       					=			relative_attention
      _a							:       Any       					=			max_relative_positions
      _a							:       List[str]       					=			pad_token_id
      _a							:       str       					=			position_biased_input

      # Backwards compatibility
      if type(__lowerCAmelCase   ) == str:
          _a							:       List[Any]       					=			[x.strip() for x in pos_att_type.lower().split("""|"""   )]

      _a							:       Union[str, Any]       					=			pos_att_type
      _a							:       Optional[Any]       					=			vocab_size
      _a							:       Tuple       					=			layer_norm_eps

      _a							:       Union[str, Any]       					=			kwargs.get("""pooler_hidden_size"""		,    __lowerCAmelCase   )
      _a							:       Tuple       					=			pooler_dropout
      _a							:       Union[str, Any]       					=			pooler_hidden_act






class     UpperCamelCase					(  snake_case__					):



  @property
  def 						a_       ( self   ):







      '''simple docstring'''


      if self.task == "multiple-choice":
          _a							:       List[str]       					=			{0: """batch""", 1: """choice""", 2: """sequence"""}
      else:
          _a							:       Dict       					=			{0: """batch""", 1: """sequence"""}
      if self._config.type_vocab_size > 0:
          return OrderedDict(
              [("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)]   )
      else:
          return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)]   )



  @property
  def 						a_       ( self   ):







      '''simple docstring'''


      return 12



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase = -1		,    __lowerCAmelCase = -1		,    __lowerCAmelCase = -1		,    __lowerCAmelCase = False		,    __lowerCAmelCase = None		,    __lowerCAmelCase = 3		,    __lowerCAmelCase = 40		,    __lowerCAmelCase = 40		,    __lowerCAmelCase = None		,    ):







      '''simple docstring'''


      _a							:       int       					=			super().generate_dummy_inputs(preprocessor=__lowerCAmelCase		,    framework=__lowerCAmelCase   )
      if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
          del dummy_inputs["token_type_ids"]
      return dummy_inputs








'''simple docstring'''





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





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


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

  # get values with two different mask size
  a		:  str   			=			median_filter(gray, 3)
  a		:  Optional[Any]   			=			median_filter(gray, 5)

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


