


import functools
import operator

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


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

A   :					str	=      {
    "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     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "sew-d"





       def __init__(     self							:    List[str]      ,   __lowerCamelCase							:    Tuple=32      ,   __lowerCamelCase							:    Optional[int]=768      ,   __lowerCamelCase							:    Optional[Any]=12      ,   __lowerCamelCase							:    Optional[Any]=12      ,   __lowerCamelCase							:    Any=3072      ,   __lowerCamelCase							:    Tuple=2      ,   __lowerCamelCase							:    int=512      ,   __lowerCamelCase							:    Dict=256      ,   __lowerCamelCase							:    List[str]=True      ,   __lowerCamelCase							:    Union[str, Any]=True      ,   __lowerCamelCase							:    Dict=("p2c", "c2p")      ,   __lowerCamelCase							:    str="layer_norm"      ,   __lowerCamelCase							:    Optional[Any]="gelu_python"      ,   __lowerCamelCase							:    Optional[int]=0.1      ,   __lowerCamelCase							:    Optional[Any]=0.1      ,   __lowerCamelCase							:    Any=0.1      ,   __lowerCamelCase							:    str=0.0      ,   __lowerCamelCase							:    int=0.1      ,   __lowerCamelCase							:    List[str]=0.0_2      ,   __lowerCamelCase							:    List[str]=1E-7      ,   __lowerCamelCase							:    Dict=1E-5      ,   __lowerCamelCase							:    List[Any]="group"      ,   __lowerCamelCase							:    List[Any]="gelu"      ,   __lowerCamelCase							:    Dict=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)      ,   __lowerCamelCase							:    Tuple=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)      ,   __lowerCamelCase							:    List[str]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)      ,   __lowerCamelCase							:    int=False      ,   __lowerCamelCase							:    Optional[int]=128      ,   __lowerCamelCase							:    Optional[Any]=16      ,   __lowerCamelCase							:    int=True      ,   __lowerCamelCase							:    Tuple=0.0_5      ,   __lowerCamelCase							:    str=10      ,   __lowerCamelCase							:    Dict=2      ,   __lowerCamelCase							:    Union[str, Any]=0.0      ,   __lowerCamelCase							:    List[str]=10      ,   __lowerCamelCase							:    Optional[int]=0      ,   __lowerCamelCase							:    List[Any]="mean"      ,   __lowerCamelCase							:    List[Any]=False      ,   __lowerCamelCase							:    Dict=False      ,   __lowerCamelCase							:    Any=256      ,   __lowerCamelCase							:    Union[str, Any]=0      ,   __lowerCamelCase							:    Optional[Any]=1      ,   __lowerCamelCase							:    Union[str, Any]=2      ,   **__lowerCamelCase							:    List[Any]      ,   ):


             '''simple docstring'''



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

             # ctc loss
             lowerCamelCase__	:       Dict					=						ctc_loss_reduction
             lowerCamelCase__	:       List[Any]					=						ctc_zero_infinity

             # sequence classification
             lowerCamelCase__	:       Optional[Any]					=						use_weighted_layer_sum
             lowerCamelCase__	:       Union[str, Any]					=						classifier_proj_size





       @property
       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



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






import cva
import numpy as np







class     _lowercase				:


       """simple docstring"""




       def __init__(     self							:    Union[str, Any]      ,   __lowerCamelCase							:    float      ,   __lowerCamelCase							:    int ):


             '''simple docstring'''



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





       def __str__(     self							:    str ):


             '''simple docstring'''



             return str(self.k )





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    str ):


             '''simple docstring'''



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

                         lowerCamelCase__	:       Optional[Any]					=						(wxx * wyy) - (wxy**2)
                         lowerCamelCase__	:       List[str]					=						wxx + wyy
                         lowerCamelCase__	:       List[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__":
    A   :					Tuple	=      HarrisCorner(0.0_4, 3)
    A,						A   :					Optional[int]	=      edge_detect.detect("path_to_image")
    cva.imwrite("detect.png", color_img)


