






"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

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


_snake_case          =   logging.get_logger(__name__)

_snake_case          =   {
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}






class        UpperCamelCase			(	snake_case_      ):
 UpperCamelCase					:       Optional[Any]			=					'''roformer'''


 def __init__(     self		:						int							,						UpperCAmelCase__		:						Any=50000							,						UpperCAmelCase__		:						Union[str, Any]=None							,						UpperCAmelCase__		:						Dict=768							,						UpperCAmelCase__		:						int=12							,						UpperCAmelCase__		:						List[Any]=12							,						UpperCAmelCase__		:						List[str]=3072							,						UpperCAmelCase__		:						int="gelu"							,						UpperCAmelCase__		:						str=0.1							,						UpperCAmelCase__		:						Union[str, Any]=0.1							,						UpperCAmelCase__		:						Tuple=1536							,						UpperCAmelCase__		:						List[Any]=2							,						UpperCAmelCase__		:						Any=0.0_2							,						UpperCAmelCase__		:						List[str]=1E-12							,						UpperCAmelCase__		:						List[str]=0							,						UpperCAmelCase__		:						Union[str, Any]=False							,						UpperCAmelCase__		:						Dict=True							,						**UpperCAmelCase__		:						Tuple							,						)					->       Any:
   super().__init__(pad_token_id=UpperCAmelCase__							,						**UpperCAmelCase__							)

   _a  :       str             =	vocab_size
   _a  :       Optional[Any]             =	hidden_size if embedding_size is None else embedding_size
   _a  :       int             =	hidden_size
   _a  :       Dict             =	num_hidden_layers
   _a  :       List[Any]             =	num_attention_heads
   _a  :       int             =	hidden_act
   _a  :       List[Any]             =	intermediate_size
   _a  :       int             =	hidden_dropout_prob
   _a  :       Tuple             =	attention_probs_dropout_prob
   _a  :       List[Any]             =	max_position_embeddings
   _a  :       str             =	type_vocab_size
   _a  :       Dict             =	initializer_range
   _a  :       List[str]             =	layer_norm_eps
   _a  :       List[str]             =	rotary_value
   _a  :       List[str]             =	use_cache







class        UpperCamelCase			(	snake_case_      ):


 @property
 def _lowercase	(     self		:						Optional[Any]							)					->       Mapping[str, Mapping[int, str]]:
   if self.task == "multiple-choice":
     _a  :       List[Any]             =	{0: """batch""", 1: """choice""", 2: """sequence"""}
   else:
     _a  :       Optional[Any]             =	{0: """batch""", 1: """sequence"""}
   _a  :       Optional[Any]             =	{0: """batch""", 1: """sequence"""}
   return OrderedDict(
       [
           ("""input_ids""", dynamic_axis),
           ("""attention_mask""", dynamic_axis),
           ("""token_type_ids""", dynamic_axis),
       ]							)









"""simple docstring"""




import cva
import numpy as np






class        UpperCamelCase			:


 def __init__(     self		:						Optional[int]							,						UpperCAmelCase__		:						float							,						UpperCAmelCase__		:						int							)					->       Dict:

   if k in (0.0_4, 0.0_6):
     _a  :       List[str]             =	k
     _a  :       List[Any]             =	window_size
   else:
     raise ValueError("""invalid k value"""							)


 def __str__(     self		:						Dict							)					->       str:
   return str(self.k							)







 def _lowercase	(     self		:						int							,						UpperCAmelCase__		:						str							)					->       tuple[cva.Mat, list[list[int]]]:
   _a  :       Dict             =	cva.imread(UpperCAmelCase__							,						0							)
   _a      ,  _a  :       List[Any]             =	img.shape
   _a  :       list[list[int]]             =	[]
   _a  :       List[Any]             =	img.copy()
   _a  :       int             =	cva.cvtColor(UpperCAmelCase__							,						cva.COLOR_GRAY2RGB							)
   _a      ,  _a  :       Any             =	np.gradient(UpperCAmelCase__							)
   _a  :       Tuple             =	dx**2
   _a  :       Union[str, Any]             =	dy**2
   _a  :       Union[str, Any]             =	dx * dy
   _a  :       int             =	0.0_4
   _a  :       List[str]             =	self.window_size // 2
   for y in range(UpperCAmelCase__							,						h - offset							):
     for x in range(UpperCAmelCase__							,						w - offset							):
       _a  :       str             =	ixx[
           y - offset : y + offset + 1, x - offset : x + offset + 1
       ].sum()
       _a  :       List[Any]             =	iyy[
           y - offset : y + offset + 1, x - offset : x + offset + 1
       ].sum()
       _a  :       Tuple             =	ixy[
           y - offset : y + offset + 1, x - offset : x + offset + 1
       ].sum()

       _a  :       Any             =	(wxx * wyy) - (wxy**2)
       _a  :       Tuple             =	wxx + wyy
       _a  :       Any             =	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__":
 _snake_case          =   HarrisCorner(0.04, 3)
 _snake_case      , _snake_case          =   edge_detect.detect('path_to_image')
 cva.imwrite('detect.png', color_img)

