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

UpperCAmelCase     						=			1_80


def SCREAMING_SNAKE_CASE			(       lowercase_:		Any						,  lowercase_:		Optional[Any]			):
       _UpperCamelCase       :    Dict					  =					k_size // 2
       _UpperCamelCase  , _UpperCamelCase       :    int					  =					np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
       _UpperCamelCase       :    Tuple					  =					(
           1
           / (2 * np.pi * sigma)
           * np.exp(-(np.square(lowercase_			) + np.square(lowercase_			)) / (2 * np.square(lowercase_			))			)
       )
       return g


def SCREAMING_SNAKE_CASE			(       lowercase_:		List[str]						,  lowercase_:		Dict						,  lowercase_:		Any			):
       _UpperCamelCase       :    str					  =					np.zeros(lowercase_			)

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

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

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

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

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

       return destination


def SCREAMING_SNAKE_CASE			(       lowercase_:		str						,  lowercase_:		str						,  lowercase_:		int						,  lowercase_:		Tuple						,  lowercase_:		List[Any]						,  lowercase_:		Optional[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:
                            _UpperCamelCase       :    Optional[Any]					  =					strong
                     elif destination[row, col] <= threshold_low:
                            _UpperCamelCase       :    int					  =					0
                     else:
                            _UpperCamelCase       :    Any					  =					weak


def SCREAMING_SNAKE_CASE			(       lowercase_:		Tuple						,  lowercase_:		int						,  lowercase_:		List[Any]						,  lowercase_:		List[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],
                            ):
                                   _UpperCamelCase       :    Dict					  =					strong
                            else:
                                   _UpperCamelCase       :    Dict					  =					0


def SCREAMING_SNAKE_CASE			(       lowercase_:		str						,  lowercase_:		Any=15						,  lowercase_:		Optional[Any]=30						,  lowercase_:		Optional[Any]=128						,  lowercase_:		Tuple=255			):
       # gaussian_filter
       _UpperCamelCase       :    Dict					  =					img_convolve(lowercase_						,  gen_gaussian_kernel(9						,  sigma=1.4			)			)
       # get the gradient and degree by sobel_filter
       _UpperCamelCase  , _UpperCamelCase       :    int					  =					sobel_filter(lowercase_			)
       _UpperCamelCase       :    Tuple					  =					PI + np.radadeg(lowercase_			)

       _UpperCamelCase       :    Optional[int]					  =					suppress_non_maximum(image.shape						,  lowercase_						,  lowercase_			)

       detect_high_low_threshold(
           image.shape						,  lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						,  lowercase_			)

       track_edge(image.shape						,  lowercase_						,  lowercase_						,  lowercase_			)

       return destination


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




import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel




class 				__lowerCAmelCase					(    utils.Parser  ):



 '''simple docstring'''
 _lowerCamelCase  :		str			      =	"halfcheetah-medium-expert-v2"
 _lowerCamelCase  :		str			      =	"config.offline"







def SCREAMING_SNAKE_CASE			(       lowercase_:		Optional[Any]						,  lowercase_:		Tuple						,  lowercase_:		Dict						,  lowercase_:		Union[str, Any]						,  lowercase_:		List[Any]			):
       _UpperCamelCase  , _UpperCamelCase       :    List[Any]					  =					utils.load_model(lowercase_						,  lowercase_						,  lowercase_						,  epoch=lowercase_						,  device=lowercase_			)
       _UpperCamelCase       :    Tuple					  =					TrajectoryTransformerModel(gpt.config			)

       trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()			)
       _UpperCamelCase       :    Any					  =					gpt.pos_emb
       trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()			)
       trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()			)
       trajectory_transformer.head.load_state_dict(gpt.head.state_dict()			)

       for i, block in enumerate(gpt.blocks			):
              trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()			)
              trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()			)
              trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()			)

              trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()			)
              trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()			)
              trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()			)
              trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()			)

       torch.save(trajectory_transformer.state_dict()						,  'pytorch_model.bin'			)


if __name__ == "__main__":
 UpperCAmelCase     						=			Parser().parse_args('''plan''')
 convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
     args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
 )



