




"""simple docstring"""

import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A       						=	logging.get_logger(__name__)

__A       						=	{
    "asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class 	lowerCamelCase__			(     snake_case_	):
			a__       :					Dict	    =   '''sew-d'''


			def __init__(   self ,       SCREAMING_SNAKE_CASE=32 ,       SCREAMING_SNAKE_CASE=768 ,       SCREAMING_SNAKE_CASE=12 ,       SCREAMING_SNAKE_CASE=12 ,       SCREAMING_SNAKE_CASE=3_072 ,       SCREAMING_SNAKE_CASE=2 ,       SCREAMING_SNAKE_CASE=512 ,       SCREAMING_SNAKE_CASE=256 ,       SCREAMING_SNAKE_CASE=True ,       SCREAMING_SNAKE_CASE=True ,       SCREAMING_SNAKE_CASE=("p2c", "c2p") ,       SCREAMING_SNAKE_CASE="layer_norm" ,       SCREAMING_SNAKE_CASE="gelu_python" ,       SCREAMING_SNAKE_CASE=0.1 ,       SCREAMING_SNAKE_CASE=0.1 ,       SCREAMING_SNAKE_CASE=0.1 ,       SCREAMING_SNAKE_CASE=0.0 ,       SCREAMING_SNAKE_CASE=0.1 ,       SCREAMING_SNAKE_CASE=0.02 ,       SCREAMING_SNAKE_CASE=1E-7 ,       SCREAMING_SNAKE_CASE=1E-5 ,       SCREAMING_SNAKE_CASE="group" ,       SCREAMING_SNAKE_CASE="gelu" ,       SCREAMING_SNAKE_CASE=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) ,       SCREAMING_SNAKE_CASE=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) ,       SCREAMING_SNAKE_CASE=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) ,       SCREAMING_SNAKE_CASE=False ,       SCREAMING_SNAKE_CASE=128 ,       SCREAMING_SNAKE_CASE=16 ,       SCREAMING_SNAKE_CASE=True ,       SCREAMING_SNAKE_CASE=0.05 ,       SCREAMING_SNAKE_CASE=10 ,       SCREAMING_SNAKE_CASE=2 ,       SCREAMING_SNAKE_CASE=0.0 ,       SCREAMING_SNAKE_CASE=10 ,       SCREAMING_SNAKE_CASE=0 ,       SCREAMING_SNAKE_CASE="mean" ,       SCREAMING_SNAKE_CASE=False ,       SCREAMING_SNAKE_CASE=False ,       SCREAMING_SNAKE_CASE=256 ,       SCREAMING_SNAKE_CASE=0 ,       SCREAMING_SNAKE_CASE=1 ,       SCREAMING_SNAKE_CASE=2 ,       **SCREAMING_SNAKE_CASE ,       ):



					"""simple docstring"""







					super().__init__(**UpperCAmelCase__ ,       pad_token_id=UpperCAmelCase__ ,       bos_token_id=UpperCAmelCase__ ,       eos_token_id=UpperCAmelCase__							)
					snake_case						:  int    	= hidden_size
					snake_case						:  Optional[int]    	= feat_extract_norm
					snake_case						:  Union[str, Any]    	= feat_extract_activation
					snake_case						:  Union[str, Any]    	= list(UpperCAmelCase__							)
					snake_case						:  str    	= list(UpperCAmelCase__							)
					snake_case						:  Tuple    	= list(UpperCAmelCase__							)
					snake_case						:  Union[str, Any]    	= conv_bias
					snake_case						:  Optional[Any]    	= num_conv_pos_embeddings
					snake_case						:  Any    	= num_conv_pos_embedding_groups
					snake_case						:  Any    	= len(self.conv_dim							)
					snake_case						:  str    	= num_hidden_layers
					snake_case						:  Dict    	= intermediate_size
					snake_case						:  Optional[Any]    	= squeeze_factor
					snake_case						:  Tuple    	= max_position_embeddings
					snake_case						:  int    	= position_buckets
					snake_case						:  Optional[int]    	= share_att_key
					snake_case						:  Any    	= relative_attention
					snake_case						:  str    	= norm_rel_ebd
					snake_case						:  Dict    	= list(UpperCAmelCase__							)
					snake_case						:  List[Any]    	= hidden_act
					snake_case						:  Tuple    	= num_attention_heads
					snake_case						:  Optional[int]    	= hidden_dropout
					snake_case						:  Optional[Any]    	= attention_dropout
					snake_case						:  str    	= activation_dropout
					snake_case						:  Optional[Any]    	= feat_proj_dropout
					snake_case						:  str    	= final_dropout
					snake_case						:  Optional[int]    	= layer_norm_eps
					snake_case						:  List[str]    	= feature_layer_norm_eps
					snake_case						:  Dict    	= initializer_range
					snake_case						:  int    	= vocab_size

					if (
					    (len(self.conv_stride							) != self.num_feat_extract_layers)
					    or (len(self.conv_kernel							) != self.num_feat_extract_layers)
					    or (len(self.conv_dim							) != self.num_feat_extract_layers)
					):
							raise ValueError(
							    "Configuration for convolutional layers is incorrect."
							    "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
							    F'''but is `len(config.conv_dim) = {len(self.conv_dim							)}`, `len(config.conv_stride)'''
							    F'''= {len(self.conv_stride							)}`, `len(config.conv_kernel) = {len(self.conv_kernel							)}`.'''							)

					# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
					snake_case						:  List[str]    	= apply_spec_augment
					snake_case						:  Any    	= mask_time_prob
					snake_case						:  int    	= mask_time_length
					snake_case						:  int    	= mask_time_min_masks
					snake_case						:  Dict    	= mask_feature_prob
					snake_case						:  int    	= mask_feature_length
					snake_case						:  str    	= mask_feature_min_masks

					# ctc loss
					snake_case						:  Union[str, Any]    	= ctc_loss_reduction
					snake_case						:  int    	= ctc_zero_infinity

					# sequence classification
					snake_case						:  int    	= use_weighted_layer_sum
					snake_case						:  str    	= classifier_proj_size



			@property
			def 							lowerCamelCase_ (   self							):



					"""simple docstring"""







					return functools.reduce(operator.mul ,       self.conv_stride ,       1							)









"""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)

