



"""simple docstring"""







import cva
import numpy as np

from digital_image_processing.filters.convolve import img_convolve
from digital_image_processing.filters.sobel_filter import sobel_filter

A     =      1_8_0

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__      )			->				Optional[int]:
							A    =       k_size // 2
							A    =       np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
							A    =       (
							    1
							    / (2 * np.pi * sigma)
							    * np.exp(-(np.square(__lowerCamelCase      ) + np.square(__lowerCamelCase      )) / (2 * np.square(__lowerCamelCase      ))      )
							)
							return g

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )			->				str:
							A    =       np.zeros(__lowerCamelCase      )

							for row in range(1					,   image_shape[0] - 1      ):
														for col in range(1					,   image_shape[1] - 1      ):
																					A    =       gradient_direction[row, col]

																					if (
																					    0 <= direction < PI / 8
																					    or 15 * PI / 8 <= direction <= 2 * PI
																					    or 7 * PI / 8 <= direction <= 9 * PI / 8
																					):
																												A    =       sobel_grad[row, col - 1]
																												A    =       sobel_grad[row, col + 1]
																												if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
																																			A    =       sobel_grad[row, col]

																					elif (
																					    PI / 8 <= direction < 3 * PI / 8
																					    or 9 * PI / 8 <= direction < 11 * PI / 8
																					):
																												A    =       sobel_grad[row + 1, col - 1]
																												A    =       sobel_grad[row - 1, col + 1]
																												if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
																																			A    =       sobel_grad[row, col]

																					elif (
																					    3 * PI / 8 <= direction < 5 * PI / 8
																					    or 11 * PI / 8 <= direction < 13 * PI / 8
																					):
																												A    =       sobel_grad[row - 1, col]
																												A    =       sobel_grad[row + 1, col]
																												if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
																																			A    =       sobel_grad[row, col]

																					elif (
																					    5 * PI / 8 <= direction < 7 * PI / 8
																					    or 13 * PI / 8 <= direction < 15 * PI / 8
																					):
																												A    =       sobel_grad[row - 1, col - 1]
																												A    =       sobel_grad[row + 1, col + 1]
																												if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
																																			A    =       sobel_grad[row, col]

							return destination

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )			->				Union[str, Any]:
							for row in range(1					,   image_shape[0] - 1      ):
														for col in range(1					,   image_shape[1] - 1      ):
																					if destination[row, col] >= threshold_high:
																												A    =       strong
																					elif destination[row, col] <= threshold_low:
																												A    =       0
																					else:
																												A    =       weak

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )			->				Optional[int]:
							for row in range(1					,   image_shape[0]      ):
														for col in range(1					,   image_shape[1]      ):
																					if destination[row, col] == weak:
																												if 255 in (
																												    destination[row, col + 1],
																												    destination[row, col - 1],
																												    destination[row - 1, col],
																												    destination[row + 1, col],
																												    destination[row - 1, col - 1],
																												    destination[row + 1, col - 1],
																												    destination[row - 1, col + 1],
																												    destination[row + 1, col + 1],
																												):
																																			A    =       strong
																												else:
																																			A    =       0

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__=15					,   lowerCamelCase__=30					,   lowerCamelCase__=128					,   lowerCamelCase__=255      )			->				List[str]:
							A    =       img_convolve(__lowerCamelCase					,   gen_gaussian_kernel(9					,   sigma=1.4      )      )
							# get the gradient and degree by sobel_filter
							A    =       sobel_filter(__lowerCamelCase      )
							A    =       PI + np.radadeg(__lowerCamelCase      )

							A    =       suppress_non_maximum(image.shape					,   __lowerCamelCase					,   __lowerCamelCase      )

							detect_high_low_threshold(
							    image.shape					,   __lowerCamelCase					,   __lowerCamelCase					,   __lowerCamelCase					,   __lowerCamelCase					,   __lowerCamelCase      )

							track_edge(image.shape					,   __lowerCamelCase					,   __lowerCamelCase					,   __lowerCamelCase      )

							return destination


if __name__ == "__main__":
							# read original image in gray mode
							A     =      cva.imread(r'../image_data/lena.jpg', 0)
							# canny edge detection
							A     =      canny(lena)
							cva.imshow('canny', canny_destination)
							cva.waitKey(0)







import functools
import operator

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


a					:				Dict       =      logging.get_logger(__name__)

a					:				str       =      {
    'facebook/hubert-base-ls960': 'https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}

class   lowerCamelCase						(	SCREAMING_SNAKE_CASE  ):
       _UpperCamelCase     		=   """hubert"""

       def __init__(      self						,    a__=32						,    a__=7_68						,    a__=12						,    a__=12						,    a__=30_72						,    a__="gelu"						,    a__=0.1						,    a__=0.1						,    a__=0.1						,    a__=True						,    a__=0.0						,    a__=0.1						,    a__=0.1						,    a__=0.02						,    a__=1e-5						,    a__="group"						,    a__="gelu"						,    a__=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)						,    a__=(5, 2, 2, 2, 2, 2, 2)						,    a__=(10, 3, 3, 3, 3, 2, 2)						,    a__=False						,    a__=1_28						,    a__=16						,    a__=False						,    a__=True						,    a__=0.05						,    a__=10						,    a__=2						,    a__=0.0						,    a__=10						,    a__=0						,    a__="sum"						,    a__=False						,    a__=False						,    a__=2_56						,    a__=0						,    a__=1						,    a__=2						,    **a__						,    ):






        '''simple docstring'''






        super().__init__(**a__						,    pad_token_id=a__						,    bos_token_id=a__						,    eos_token_id=a__       )
        snake_case :			List[str]							=   hidden_size
        snake_case :			List[str]							=   feat_extract_norm
        snake_case :			List[str]							=   feat_extract_activation
        snake_case :			Dict							=   list(a__       )
        snake_case :			Tuple							=   list(a__       )
        snake_case :			Dict							=   list(a__       )
        snake_case :			str							=   conv_bias
        snake_case :			Dict							=   num_conv_pos_embeddings
        snake_case :			Union[str, Any]							=   num_conv_pos_embedding_groups
        snake_case :			Optional[int]							=   len(self.conv_dim       )
        snake_case :			Tuple							=   num_hidden_layers
        snake_case :			Any							=   intermediate_size
        snake_case :			Optional[Any]							=   hidden_act
        snake_case :			str							=   num_attention_heads
        snake_case :			List[Any]							=   hidden_dropout
        snake_case :			Union[str, Any]							=   attention_dropout
        snake_case :			Dict							=   activation_dropout
        snake_case :			List[str]							=   feat_proj_layer_norm
        snake_case :			List[str]							=   feat_proj_dropout
        snake_case :			Union[str, Any]							=   final_dropout
        snake_case :			List[str]							=   layerdrop
        snake_case :			str							=   layer_norm_eps
        snake_case :			Tuple							=   initializer_range
        snake_case :			str							=   vocab_size
        snake_case :			Tuple							=   do_stable_layer_norm
        snake_case :			Dict							=   use_weighted_layer_sum
        snake_case :			Optional[Any]							=   classifier_proj_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)`, but is `len(config.conv_dim) ='''
             f''' {len(self.conv_dim       )}`, `len(config.conv_stride) = {len(self.conv_stride       )}`,'''
             f''' `len(config.conv_kernel) = {len(self.conv_kernel       )}`.'''       )

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

        # ctc loss
        snake_case :			List[str]							=   ctc_loss_reduction
        snake_case :			Dict							=   ctc_zero_infinity



       @property
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






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






