





'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

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


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

__magic_name__   :						Optional[int]    =			{
    """kssteven/ibert-roberta-base""": """https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json""",
    """kssteven/ibert-roberta-large""": """https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json""",
    """kssteven/ibert-roberta-large-mnli""": (
        """https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"""
    ),
}


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						List[Any]      =   '''ibert'''

     def __init__(					self						,   lowerCamelCase=30_522						,   lowerCamelCase=768						,   lowerCamelCase=12						,   lowerCamelCase=12						,   lowerCamelCase=3_072						,   lowerCamelCase="gelu"						,   lowerCamelCase=0.1						,   lowerCamelCase=0.1						,   lowerCamelCase=512						,   lowerCamelCase=2						,   lowerCamelCase=0.02						,   lowerCamelCase=1e-12						,   lowerCamelCase=1						,   lowerCamelCase=0						,   lowerCamelCase=2						,   lowerCamelCase="absolute"						,   lowerCamelCase=False						,   lowerCamelCase="none"						,   **lowerCamelCase						,   ):
       super().__init__(pad_token_id=lowerCamelCase						,   bos_token_id=lowerCamelCase						,   eos_token_id=lowerCamelCase						,   **lowerCamelCase	)

       _snake_case     		=				vocab_size
       _snake_case     		=				hidden_size
       _snake_case     		=				num_hidden_layers
       _snake_case     		=				num_attention_heads
       _snake_case     		=				hidden_act
       _snake_case     		=				intermediate_size
       _snake_case     		=				hidden_dropout_prob
       _snake_case     		=				attention_probs_dropout_prob
       _snake_case     		=				max_position_embeddings
       _snake_case     		=				type_vocab_size
       _snake_case     		=				initializer_range
       _snake_case     		=				layer_norm_eps
       _snake_case     		=				position_embedding_type
       _snake_case     		=				quant_mode
       _snake_case     		=				force_dequant







class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     @property
     def 	UpperCamelCase(					self	):
       if self.task == "multiple-choice":
         _snake_case     		=				{0: "batch", 1: "choice", 2: "sequence"}
       else:
         _snake_case     		=				{0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]	)









'''simple docstring'''



import cva
import numpy as np


class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase	):

       if k in (0.04, 0.06):
         _snake_case     		=				k
         _snake_case     		=				window_size
       else:
         raise ValueError("invalid k value"	)

     def __str__(					self	):
       return str(self.k	)






     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				cva.imread(lowerCamelCase						,   0	)
       _snake_case		,		_snake_case     		=				img.shape
       _snake_case     		=				[]
       _snake_case     		=				img.copy()
       _snake_case     		=				cva.cvtColor(lowerCamelCase						,   cva.COLOR_GRAY2RGB	)
       _snake_case		,		_snake_case     		=				np.gradient(lowerCamelCase	)
       _snake_case     		=				dx**2
       _snake_case     		=				dy**2
       _snake_case     		=				dx * dy
       _snake_case     		=				0.04
       _snake_case     		=				self.window_size // 2
       for y in range(lowerCamelCase						,   h - offset	):
         for x in range(lowerCamelCase						,   w - offset	):
           _snake_case     		=				ixx[
               y - offset : y + offset + 1, x - offset : x + offset + 1
           ].sum()
           _snake_case     		=				iyy[
               y - offset : y + offset + 1, x - offset : x + offset + 1
           ].sum()
           _snake_case     		=				ixy[
               y - offset : y + offset + 1, x - offset : x + offset + 1
           ].sum()

           _snake_case     		=				(wxx * wyy) - (wxy**2)
           _snake_case     		=				wxx + wyy
           _snake_case     		=				det - k * (trace**2)
           # Can change the value
           if r > 0.5:
             corner_list.append([x, y, r]	)
             color_img.itemset((y, x, 0)						,   0	)
             color_img.itemset((y, x, 1)						,   0	)
             color_img.itemset((y, x, 2)						,   255	)
       return color_img, corner_list


if __name__ == "__main__":
 __magic_name__   :						Tuple    =			HarrisCorner(0.04, 3)
 __magic_name__   ,    __magic_name__   :						Any    =			edge_detect.detect("""path_to_image""")
 cva.imwrite("""detect.png""", color_img)


