




'''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


lowercase	:      Optional[Any]				=       logging.get_logger(__name__)

lowercase	:      Optional[int]				=       {
    '''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   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_				):
 __snake_case	:     str			   =      "deberta"



 def __init__(  self   ,		A=50265   ,		A=768   ,		A=12   ,		A=12   ,		A=3072   ,		A="gelu"   ,		A=0.1   ,		A=0.1   ,		A=512   ,		A=0   ,		A=0.0_2   ,		A=1E-7   ,		A=False   ,		A=-1   ,		A=0   ,		A=True   ,		A=None   ,		A=0   ,		A="gelu"   ,		**A   ,		):






        '''simple docstring'''




        super().__init__(**A )

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

        # Backwards compatibility
        if type(A ) == str:
               lowercase  :		Optional[Any]						=      [x.strip() for x in pos_att_type.lower().split('''|''' )]

        lowercase  :		Tuple						=      pos_att_type
        lowercase  :		List[Any]						=      vocab_size
        lowercase  :		int						=      layer_norm_eps

        lowercase  :		Optional[Any]						=      kwargs.get('''pooler_hidden_size'''   ,		A )
        lowercase  :		List[Any]						=      pooler_dropout
        lowercase  :		Tuple						=      pooler_hidden_act






class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_				):



 @property
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        if self.task == "multiple-choice":
               lowercase  :		Any						=      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
        else:
               lowercase  :		List[Any]						=      {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    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        return 12





 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A = -1   ,		A = -1   ,		A = -1   ,		A = False   ,		A = None   ,		A = 3   ,		A = 40   ,		A = 40   ,		A = None   ,		):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      super().generate_dummy_inputs(preprocessor=A   ,		framework=A )
        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'''






import numpy as np
from PIL import Image




def 		a_( lowerCamelCase      )				->np.array:






       '''simple docstring'''




       lowercase,     lowercase,     lowercase  :		Union[str, Any]						=      rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
       return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b




def 		a_( lowerCamelCase      )				->np.array:






       '''simple docstring'''




       return (gray > 127) & (gray <= 255)




def 		a_( lowerCamelCase   ,				lowerCamelCase      )				->np.array:






       '''simple docstring'''




       lowercase  :		Optional[Any]						=      np.zeros_like(lowerCamelCase      )
       lowercase  :		Any						=      np.zeros(
           (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)      )

       # Copy image to padded image
       lowercase  :		List[str]						=      image

       # Iterate over image & apply kernel
       for x in range(image.shape[1]      ):
              for y in range(image.shape[0]      ):
                     lowercase  :		Union[str, Any]						=      (
                         kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
                     ).sum()
                     lowercase  :		Any						=      int(summation == 5      )
       return output


# kernel to be applied
lowercase	:      List[Any]				=       np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
      # read original image
      lowercase	:      Dict				=       np.array(Image.open(r'''..\image_data\lena.jpg'''))
      # Apply erosion operation to a binary image
      lowercase	:      List[str]				=       erosion(grayabinary(rgbagray(image)), structuring_element)
      # Save the output image
      lowercase	:      Optional[Any]				=       Image.fromarray(output).convert('''RGB''')
      pil_img.save('''result_erosion.png''')




