




import numpy as np
from PIL import Image
def  snake_case_					(		SCREAMING_SNAKE_CASE_    )      ->			np.array:
							lowercase__  :	List[str]        =							rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
							return 0.2989 * r + 0.5870 * g + 0.1140 * b
def  snake_case_					(		SCREAMING_SNAKE_CASE_    )      ->			np.array:
							return (gray > 1_27) & (gray <= 2_55)
def  snake_case_					(		SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_    )      ->			np.array:
							lowercase__  :	List[Any]        =							np.zeros_like(_lowerCamelCase    )
							lowercase__  :	Dict        =							np.zeros(
							    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)    )

							# Copy image to padded image
							lowercase__  :	Dict        =							image

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


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

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












from collections import OrderedDict
from typing import Mapping

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


__a  :		Any		     =   logging.get_logger(__name__)

__a  :		Union[str, Any]		     =   {
    '''squeezebert/squeezebert-uncased''': (
        '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'''
    ),
    '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json''',
    '''squeezebert/squeezebert-mnli-headless''': (
        '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'''
    ),
}







class 				UpperCAmelCase(					snake_case_					):



	"""simple docstring"""






	a     : str	  =					SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
	a     : int	  =					"""squeezebert"""

	def __init__(					self	,				lowerCamelCase=30522	,				lowerCamelCase=768	,				lowerCamelCase=12	,				lowerCamelCase=12	,				lowerCamelCase=3072	,				lowerCamelCase="gelu"	,				lowerCamelCase=0.1	,				lowerCamelCase=0.1	,				lowerCamelCase=512	,				lowerCamelCase=2	,				lowerCamelCase=0.02	,				lowerCamelCase=1E-12	,				lowerCamelCase=0	,				lowerCamelCase=768	,				lowerCamelCase=4	,				lowerCamelCase=4	,				lowerCamelCase=4	,				lowerCamelCase=1	,				lowerCamelCase=4	,				lowerCamelCase=4	,				**lowerCamelCase	,				) ->      str:





								"""simple docstring"""


								super().__init__(pad_token_id=lowerCamelCase	,				**lowerCamelCase    )

								lowercase__  :	Any        =							vocab_size
								lowercase__  :	Tuple        =							hidden_size
								lowercase__  :	str        =							num_hidden_layers
								lowercase__  :	Optional[int]        =							num_attention_heads
								lowercase__  :	List[Any]        =							hidden_act
								lowercase__  :	Union[str, Any]        =							intermediate_size
								lowercase__  :	int        =							hidden_dropout_prob
								lowercase__  :	List[Any]        =							attention_probs_dropout_prob
								lowercase__  :	Any        =							max_position_embeddings
								lowercase__  :	List[Any]        =							type_vocab_size
								lowercase__  :	Any        =							initializer_range
								lowercase__  :	Optional[Any]        =							layer_norm_eps
								lowercase__  :	Any        =							embedding_size
								lowercase__  :	Optional[Any]        =							q_groups
								lowercase__  :	List[str]        =							k_groups
								lowercase__  :	List[Any]        =							v_groups
								lowercase__  :	str        =							post_attention_groups
								lowercase__  :	Optional[int]        =							intermediate_groups
								lowercase__  :	Optional[int]        =							output_groups







class 				UpperCAmelCase(					snake_case_					):



	"""simple docstring"""






	@property
	def    __a     (					self    ) ->      Mapping[str, Mapping[int, str]]:





								"""simple docstring"""


								if self.task == "multiple-choice":
															lowercase__  :	Union[str, Any]        =							{0: "batch", 1: "choice", 2: "sequence"}
								else:
															lowercase__  :	Union[str, Any]        =							{0: "batch", 1: "sequence"}
								return OrderedDict(
								    [
								        ("input_ids", dynamic_axis),
								        ("attention_mask", dynamic_axis),
								        ("token_type_ids", dynamic_axis),
								    ]    )