


import functools
import operator

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


lowerCAmelCase__   	=				logging.get_logger(__name__)

lowerCAmelCase__   	=				{
    '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__ (       a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE						    =				"hubert"


     def __init__(		self				,		__lowerCamelCase=3_2				,		__lowerCamelCase=7_6_8				,		__lowerCamelCase=1_2				,		__lowerCamelCase=1_2				,		__lowerCamelCase=3_0_7_2				,		__lowerCamelCase="gelu"				,		__lowerCamelCase=0.1				,		__lowerCamelCase=0.1				,		__lowerCamelCase=0.1				,		__lowerCamelCase=True				,		__lowerCamelCase=0.0				,		__lowerCamelCase=0.1				,		__lowerCamelCase=0.1				,		__lowerCamelCase=0.0_2				,		__lowerCamelCase=1e-5				,		__lowerCamelCase="group"				,		__lowerCamelCase="gelu"				,		__lowerCamelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)				,		__lowerCamelCase=(5, 2, 2, 2, 2, 2, 2)				,		__lowerCamelCase=(1_0, 3, 3, 3, 3, 2, 2)				,		__lowerCamelCase=False				,		__lowerCamelCase=1_2_8				,		__lowerCamelCase=1_6				,		__lowerCamelCase=False				,		__lowerCamelCase=True				,		__lowerCamelCase=0.0_5				,		__lowerCamelCase=1_0				,		__lowerCamelCase=2				,		__lowerCamelCase=0.0				,		__lowerCamelCase=1_0				,		__lowerCamelCase=0				,		__lowerCamelCase="sum"				,		__lowerCamelCase=False				,		__lowerCamelCase=False				,		__lowerCamelCase=2_5_6				,		__lowerCamelCase=0				,		__lowerCamelCase=1				,		__lowerCamelCase=2				,		**__lowerCamelCase				,		)    ->       List[Any]:
            super().__init__(**__lowerCamelCase				,		pad_token_id=__lowerCamelCase				,		bos_token_id=__lowerCamelCase				,		eos_token_id=__lowerCamelCase)
            _A   : Tuple       					=				hidden_size
            _A   : Tuple       					=				feat_extract_norm
            _A   : Tuple       					=				feat_extract_activation
            _A   : Tuple       					=				list(__lowerCamelCase)
            _A   : List[str]       					=				list(__lowerCamelCase)
            _A   : List[str]       					=				list(__lowerCamelCase)
            _A   : str       					=				conv_bias
            _A   : Dict       					=				num_conv_pos_embeddings
            _A   : Dict       					=				num_conv_pos_embedding_groups
            _A   : List[str]       					=				len(self.conv_dim)
            _A   : Any       					=				num_hidden_layers
            _A   : Dict       					=				intermediate_size
            _A   : str       					=				hidden_act
            _A   : Optional[int]       					=				num_attention_heads
            _A   : Optional[Any]       					=				hidden_dropout
            _A   : int       					=				attention_dropout
            _A   : Optional[Any]       					=				activation_dropout
            _A   : int       					=				feat_proj_layer_norm
            _A   : Union[str, Any]       					=				feat_proj_dropout
            _A   : Union[str, Any]       					=				final_dropout
            _A   : str       					=				layerdrop
            _A   : Tuple       					=				layer_norm_eps
            _A   : Dict       					=				initializer_range
            _A   : Optional[int]       					=				vocab_size
            _A   : List[str]       					=				do_stable_layer_norm
            _A   : Optional[int]       					=				use_weighted_layer_sum
            _A   : Tuple       					=				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
            _A   : Optional[Any]       					=				apply_spec_augment
            _A   : Optional[Any]       					=				mask_time_prob
            _A   : Tuple       					=				mask_time_length
            _A   : Optional[Any]       					=				mask_time_min_masks
            _A   : List[str]       					=				mask_feature_prob
            _A   : Dict       					=				mask_feature_length
            _A   : Optional[Any]       					=				mask_feature_min_masks

            # ctc loss
            _A   : Optional[Any]       					=				ctc_loss_reduction
            _A   : Any       					=				ctc_zero_infinity


     @property
     def 				_lowerCamelCase				(		self)    ->       int:
            return functools.reduce(operator.mul				,		self.conv_stride				,		1)




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

lowerCAmelCase__   	=				1_80
def 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[Any]		):
       _A   : int       					=				k_size // 2
       _A					,    _A   : Tuple       					=				np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       _A   : Union[str, Any]       					=				(
           1
           / (2 * np.pi * sigma)
           * np.exp(-(np.square(UpperCamelCase__		) + np.square(UpperCamelCase__		)) / (2 * np.square(UpperCamelCase__		))		)
       )
       return g
def 							_UpperCAmelCase       (UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Tuple		):
       _A   : Optional[int]       					=				np.zeros(UpperCamelCase__		)

       for row in range(1       ,							image_shape[0] - 1		):
              for col in range(1       ,							image_shape[1] - 1		):
                     _A   : Dict       					=				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   : Dict       					=				sobel_grad[row, col - 1]
                            _A   : Any       					=				sobel_grad[row, col + 1]
                            if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                                   _A   : List[Any]       					=				sobel_grad[row, col]

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

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

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

       return destination
def 							_UpperCAmelCase       (UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						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   : Optional[int]       					=				strong
                     elif destination[row, col] <= threshold_low:
                            _A   : Optional[int]       					=				0
                     else:
                            _A   : Union[str, Any]       					=				weak
def 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[int]       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Any		):
       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   : str       					=				strong
                            else:
                                   _A   : Any       					=				0
def 							_UpperCAmelCase       (UpperCamelCase__  :						Dict       ,							UpperCamelCase__  :						int=15       ,							UpperCamelCase__  :						Dict=30       ,							UpperCamelCase__  :						int=128       ,							UpperCamelCase__  :						Union[str, Any]=255		):
       # gaussian_filter
       _A   : Union[str, Any]       					=				img_convolve(UpperCamelCase__       ,							gen_gaussian_kernel(9       ,							sigma=1.4		)		)
       # get the gradient and degree by sobel_filter
       _A					,    _A   : Dict       					=				sobel_filter(UpperCamelCase__		)
       _A   : Tuple       					=				PI + np.radadeg(UpperCamelCase__		)

       _A   : Optional[int]       					=				suppress_non_maximum(image.shape       ,							UpperCamelCase__       ,							UpperCamelCase__		)

       detect_high_low_threshold(
           image.shape       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)

       track_edge(image.shape       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)

       return destination


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