


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


lowerCAmelCase__   	=				logging.get_logger(__name__)

lowerCAmelCase__   	=				{
    'ZinengTang/tvlt-base': 'https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json',
}







class   lowerCAmelCase__ (       a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE						    =				"tvlt"


     def __init__(		self				,		__lowerCamelCase=2_2_4				,		__lowerCamelCase=2_0_4_8				,		__lowerCamelCase=1_2_8				,		__lowerCamelCase=[1_6, 1_6]				,		__lowerCamelCase=[1_6, 1_6]				,		__lowerCamelCase=3				,		__lowerCamelCase=1				,		__lowerCamelCase=8				,		__lowerCamelCase=7_6_8				,		__lowerCamelCase=1_2				,		__lowerCamelCase=1_2				,		__lowerCamelCase=3_0_7_2				,		__lowerCamelCase="gelu"				,		__lowerCamelCase=0.0				,		__lowerCamelCase=0.0				,		__lowerCamelCase=0.0_2				,		__lowerCamelCase=1e-6				,		__lowerCamelCase=True				,		__lowerCamelCase=False				,		__lowerCamelCase=1_6				,		__lowerCamelCase=5_1_2				,		__lowerCamelCase=8				,		__lowerCamelCase=2_0_4_8				,		__lowerCamelCase=0.7_5				,		__lowerCamelCase=0.1_5				,		__lowerCamelCase="frame-level"				,		__lowerCamelCase=True				,		__lowerCamelCase=True				,		__lowerCamelCase="classification"				,		**__lowerCamelCase				,		)    ->       List[str]:
            super().__init__(**__lowerCamelCase)

            if audio_mask_type not in ("frame-level", "patch_level"):
                   raise ValueError(
                       "audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') "
                       F"got {audio_mask_type}")

            _A   : int       					=				image_size
            _A   : List[str]       					=				spectrogram_length
            _A   : Any       					=				frequency_length
            _A   : Optional[Any]       					=				image_patch_size
            _A   : Tuple       					=				audio_patch_size
            _A   : List[Any]       					=				num_image_channels
            _A   : List[str]       					=				num_audio_channels
            _A   : Optional[Any]       					=				num_frames

            _A   : Optional[int]       					=				hidden_size
            _A   : Any       					=				num_hidden_layers
            _A   : Dict       					=				num_attention_heads
            _A   : str       					=				intermediate_size
            _A   : Optional[int]       					=				hidden_act
            _A   : Tuple       					=				hidden_dropout_prob
            _A   : Union[str, Any]       					=				attention_probs_dropout_prob
            _A   : Optional[Any]       					=				initializer_range
            _A   : Tuple       					=				layer_norm_eps
            _A   : Optional[Any]       					=				qkv_bias
            _A   : List[Any]       					=				use_mean_pooling

            _A   : Dict       					=				decoder_num_attention_heads
            _A   : Union[str, Any]       					=				decoder_hidden_size
            _A   : Dict       					=				decoder_num_hidden_layers
            _A   : Tuple       					=				decoder_intermediate_size
            _A   : Dict       					=				pixel_mask_ratio
            _A   : Union[str, Any]       					=				audio_mask_ratio
            _A   : List[str]       					=				audio_mask_type

            _A   : List[Any]       					=				task_matching
            _A   : Union[str, Any]       					=				task_mae
            _A   : Optional[Any]       					=				loss_type




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)
