"""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

UpperCAmelCase       :    Dict									=			180



def 				__a       (			_lowercase							, _lowercase						):







					"""simple docstring"""





					lowerCamelCase__		:   Optional[int]  				=					k_size // 2
					lowerCamelCase__				,						lowerCamelCase__		:   Optional[Any]  				=					np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
					lowerCamelCase__		:   Union[str, Any]  				=					(
					    1
					    / (2 * np.pi * sigma)
					    * np.exp(-(np.square(_lowercase						) + np.square(_lowercase						)) / (2 * np.square(_lowercase						))						)
					)
					return g



def 				__a       (			_lowercase							, _lowercase							, _lowercase						):







					"""simple docstring"""





					lowerCamelCase__		:   Dict  				=					np.zeros(_lowercase						)

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

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

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

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

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

					return destination



def 				__a       (			_lowercase							, _lowercase							, _lowercase							, _lowercase							, _lowercase							, _lowercase						):







					"""simple docstring"""





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



def 				__a       (			_lowercase							, _lowercase							, _lowercase							, _lowercase						):







					"""simple docstring"""





					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],
																				):
																									lowerCamelCase__		:   List[Any]  				=					strong
																				else:
																									lowerCamelCase__		:   Dict  				=					0



def 				__a       (			_lowercase							, _lowercase=15							, _lowercase=30							, _lowercase=128							, _lowercase=255						):







					"""simple docstring"""





					lowerCamelCase__		:   Union[str, Any]  				=					img_convolve(_lowercase							, gen_gaussian_kernel(9							, sigma=1.4						)						)
					# get the gradient and degree by sobel_filter
					lowerCamelCase__				,						lowerCamelCase__		:   List[str]  				=					sobel_filter(_lowercase						)
					lowerCamelCase__		:   Optional[Any]  				=					PI + np.radadeg(_lowercase						)

					lowerCamelCase__		:   Union[str, Any]  				=					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       :    Tuple									=			cva.imread(R"../image_data/lena.jpg", 0)
					# canny edge detection
					UpperCAmelCase       :    Dict									=			canny(lena)
					cva.imshow("canny", canny_destination)
					cva.waitKey(0)







"""simple docstring"""




import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
UpperCAmelCase       :    int									=			logging.get_logger(__name__)

UpperCAmelCase       :    Optional[int]									=			["UtteranceLevel"]



@torch.no_grad()
def 				__a       (			_lowercase							, _lowercase							, _lowercase							, _lowercase						):







					"""simple docstring"""





					lowerCamelCase__		:   List[str]  				=					torch.load(_lowercase							, map_location='''cpu'''						)
					if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
										raise NotImplementedError(f"""The supported s3prl models are {SUPPORTED_MODELS}"""						)

					lowerCamelCase__		:   List[Any]  				=					checkpoint['''Downstream''']

					lowerCamelCase__		:   int  				=					HubertConfig.from_pretrained(_lowercase						)
					lowerCamelCase__		:   Optional[Any]  				=					HubertForSequenceClassification.from_pretrained(_lowercase							, config=_lowercase						)
					lowerCamelCase__		:   Optional[Any]  				=					WavaVecaFeatureExtractor.from_pretrained(
					    _lowercase							, return_attention_mask=_lowercase							, do_normalize=_lowercase						)

					if hf_congfig.use_weighted_layer_sum:
										lowerCamelCase__		:   Dict  				=					checkpoint['''Featurizer''']['''weights''']

					lowerCamelCase__		:   Any  				=					downstream_dict['''projector.weight''']
					lowerCamelCase__		:   Optional[int]  				=					downstream_dict['''projector.bias''']
					lowerCamelCase__		:   Dict  				=					downstream_dict['''model.post_net.linear.weight''']
					lowerCamelCase__		:   str  				=					downstream_dict['''model.post_net.linear.bias''']

					hf_feature_extractor.save_pretrained(_lowercase						)
					hf_model.save_pretrained(_lowercase						)


if __name__ == "__main__":
					UpperCAmelCase       :    int									=			argparse.ArgumentParser()
					parser.add_argument(
					    "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
					)
					parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
					parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
					parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
					UpperCAmelCase       :    Dict									=			parser.parse_args()
					convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






