





'''simple docstring'''




from pathlib import Path

import numpy as np
from PIL import Image






def 	_lowerCAmelCase      (  __snake_case  :			np.ndarray      )					->					np.ndarray:
     __A						:			int								=	rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
     return 0.2_989 * r + 0.5_870 * g + 0.1_140 * b






def 	_lowerCAmelCase      (  __snake_case  :			np.ndarray      )					->					np.ndarray:
     return (gray > 1_27) & (gray <= 2_55)






def 	_lowerCAmelCase      (  __snake_case  :			np.ndarray			,							__snake_case  :			np.ndarray      )					->					np.ndarray:
     __A						:			Any								=	np.zeros_like(__snake_case      )
     __A						:			int								=	np.zeros(
         (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)      )

     # Copy image to padded image
     __A						:			int								=	image

     # Iterate over image & apply kernel
     for x in range(image.shape[1]      ):
          for y in range(image.shape[0]      ):
               __A						:			Tuple								=	(
                   kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
               ).sum()
               __A						:			Dict								=	int(summation > 0      )
     return output


if __name__ == "__main__":
  # read original image
  lowercase__   :				Union[str, Any]      =   Path(__file__).resolve().parent / '''image_data''' / '''lena.jpg'''
  lowercase__   :				Union[str, Any]      =   np.array(Image.open(lena_path))
  # kernel to be applied
  lowercase__   :				List[Any]      =   np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
  lowercase__   :				Union[str, Any]      =   dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
  # Save the output image
  lowercase__   :				str      =   Image.fromarray(output).convert('''RGB''')
  pil_img.save('''result_dilation.png''')






'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


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

lowercase__   :				str      =   {
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}


class 			SCREAMING_SNAKE_CASE   (a__							):
     lowerCAmelCase		      =	'''roberta-prelayernorm'''


     def __init__(						self						,     _UpperCAmelCase=5_0265						,     _UpperCAmelCase=768						,     _UpperCAmelCase=12						,     _UpperCAmelCase=12						,     _UpperCAmelCase=3072						,     _UpperCAmelCase="gelu"						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=512						,     _UpperCAmelCase=2						,     _UpperCAmelCase=0.02						,     _UpperCAmelCase=1e-1_2						,     _UpperCAmelCase=1						,     _UpperCAmelCase=0						,     _UpperCAmelCase=2						,     _UpperCAmelCase="absolute"						,     _UpperCAmelCase=True						,     _UpperCAmelCase=None						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






          super().__init__(pad_token_id=_UpperCAmelCase						,     bos_token_id=_UpperCAmelCase						,     eos_token_id=_UpperCAmelCase						,     **_UpperCAmelCase)

          __A						:			Union[str, Any]								=	vocab_size
          __A						:			List[str]								=	hidden_size
          __A						:			Tuple								=	num_hidden_layers
          __A						:			List[str]								=	num_attention_heads
          __A						:			Tuple								=	hidden_act
          __A						:			Optional[Any]								=	intermediate_size
          __A						:			List[Any]								=	hidden_dropout_prob
          __A						:			Union[str, Any]								=	attention_probs_dropout_prob
          __A						:			Dict								=	max_position_embeddings
          __A						:			Any								=	type_vocab_size
          __A						:			Optional[int]								=	initializer_range
          __A						:			List[str]								=	layer_norm_eps
          __A						:			Optional[int]								=	position_embedding_type
          __A						:			Dict								=	use_cache
          __A						:			List[str]								=	classifier_dropout




class 			SCREAMING_SNAKE_CASE   (a__							):


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          if self.task == "multiple-choice":
               __A						:			List[str]								=	{0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
               __A						:			Tuple								=	{0: 'batch', 1: 'sequence'}
          return OrderedDict(
              [
                  ('input_ids', dynamic_axis),
                  ('attention_mask', dynamic_axis),
              ])