




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							    =	180
def 						lowerCAmelCase_       (	__a	,	__a )       ->			int:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: str	     =k_size // 2
							SCREAMING_SNAKE_CASE			: str	     =np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
							SCREAMING_SNAKE_CASE			: Any	     =(
							    1
							    / (2 * np.pi * sigma)
							    * np.exp(-(np.square(_SCREAMING_SNAKE_CASE ) + np.square(_SCREAMING_SNAKE_CASE )) / (2 * np.square(_SCREAMING_SNAKE_CASE )) )
							)
							return g
def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			List[Any]:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: List[Any]	     =np.zeros(_SCREAMING_SNAKE_CASE )

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

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

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

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

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

							return destination
def 						lowerCAmelCase_       (	__a	,	__a	,	__a	,	__a	,	__a	,	__a )       ->			Union[str, Any]:





							"""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:
																												SCREAMING_SNAKE_CASE			: Tuple	     =strong
																					elif destination[row, col] <= threshold_low:
																												SCREAMING_SNAKE_CASE			: Optional[Any]	     =0
																					else:
																												SCREAMING_SNAKE_CASE			: int	     =weak
def 						lowerCAmelCase_       (	__a	,	__a	,	__a	,	__a )       ->			Dict:





							"""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],
																												):
																																			SCREAMING_SNAKE_CASE			: List[Any]	     =strong
																												else:
																																			SCREAMING_SNAKE_CASE			: Optional[Any]	     =0
def 						lowerCAmelCase_       (	__a	,	__a=15	,	__a=30	,	__a=128	,	__a=255 )       ->			int:





							"""simple docstring"""





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

							SCREAMING_SNAKE_CASE			: Tuple	     =suppress_non_maximum(image.shape	,	_SCREAMING_SNAKE_CASE	,	_SCREAMING_SNAKE_CASE )

							detect_high_low_threshold(
							    image.shape	,	_SCREAMING_SNAKE_CASE	,	_SCREAMING_SNAKE_CASE	,	_SCREAMING_SNAKE_CASE	,	_SCREAMING_SNAKE_CASE	,	_SCREAMING_SNAKE_CASE )

							track_edge(image.shape	,	_SCREAMING_SNAKE_CASE	,	_SCREAMING_SNAKE_CASE	,	_SCREAMING_SNAKE_CASE )

							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 argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
_A							    =	logging.get_logger(__name__)


@dataclass
class 							_lowerCAmelCase		:
				lowerCamelCase__									=    42
				lowerCamelCase__									=    field(default_factory=UpperCamelCase__	)
				lowerCamelCase__									=    field(default_factory=UpperCamelCase__	)




				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      Tuple:
											SCREAMING_SNAKE_CASE			: Tuple	     =len(list(m.modules()							)							) == 1 or isinstance(snake_case_		,		nn.Convad							) or isinstance(snake_case_		,		nn.BatchNormad							)
											if has_not_submodules:
																		if not isinstance(snake_case_		,		snake_case_							):
																									self.traced.append(snake_case_							)




				def __call__( self		,		snake_case_							)    ->      List[Any]:
											for m in self.module.modules():
																		self.handles.append(m.register_forward_hook(self._forward_hook							)							)
											self.module(snake_case_							)
											[x.remove() for x in self.handles]
											return self







				@property
				def     __a		( self							)    ->      int:
											# check the len of the state_dict keys to see if we have learnable params
											return list(filter(lambda snake_case_							: len(list(x.state_dict().keys()							)							) > 0		,		self.traced							)							)







@dataclass
class 							_lowerCAmelCase		:
				lowerCamelCase__									=    42
				lowerCamelCase__									=    42
				lowerCamelCase__									=    0
				lowerCamelCase__									=    field(default_factory=UpperCamelCase__	)
				lowerCamelCase__									=    field(default_factory=UpperCamelCase__	)




				def __call__( self		,		snake_case_							)    ->      List[str]:
											SCREAMING_SNAKE_CASE			: List[Any]	     =Tracker(self.dest							)(snake_case_							).parametrized
											SCREAMING_SNAKE_CASE			: Optional[Any]	     =Tracker(self.src							)(snake_case_							).parametrized

											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =list(filter(lambda snake_case_							: type(snake_case_							) not in self.src_skip		,		snake_case_							)							)
											SCREAMING_SNAKE_CASE			: Dict	     =list(filter(lambda snake_case_							: type(snake_case_							) not in self.dest_skip		,		snake_case_							)							)

											if len(snake_case_							) != len(snake_case_							):
																		raise Exception(
																		    F'Numbers of operations are different. Source module has {len(snake_case_							)} operations while'
																		    F' destination module has {len(snake_case_							)}.'							)

											for dest_m, src_m in zip(snake_case_		,		snake_case_							):
																		dest_m.load_state_dict(src_m.state_dict()							)
																		if self.verbose == 1:
																									print(F'Transfered from={src_m} to={dest_m}'							)







def 						lowerCAmelCase_       (	__a	,	__a )       ->			nn.Module:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: str	     =from_model.state_dict()
							SCREAMING_SNAKE_CASE			: List[str]	     =our_model.state_dict()
							SCREAMING_SNAKE_CASE			: int	     =our_model.config
							SCREAMING_SNAKE_CASE			: Dict	     =[]
							for stage_idx in range(len(config.hidden_sizes ) ):
														for block_id in range(config.depths[stage_idx] ):
																					SCREAMING_SNAKE_CASE			: Optional[Any]	     =f'block{stage_idx + 1}.{block_id}.layer_scale_1'
																					SCREAMING_SNAKE_CASE			: List[str]	     =f'van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'

																					all_keys.append((from_key, to_key) )
																					SCREAMING_SNAKE_CASE			: int	     =f'block{stage_idx + 1}.{block_id}.layer_scale_2'
																					SCREAMING_SNAKE_CASE			: List[str]	     =f'van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight'

																					all_keys.append((from_key, to_key) )

							for from_key, to_key in all_keys:
														SCREAMING_SNAKE_CASE			: List[Any]	     =from_state_dict.pop(__a )

							our_model.load_state_dict(__a )
							return our_model
def 						lowerCAmelCase_       (	__a	,	__a	,	__a	,	__a	,	__a	,	__a = True	,	)       ->			Any:





							"""simple docstring"""





							print(f'Downloading weights for {name}...' )
							SCREAMING_SNAKE_CASE			: Dict	     =cached_download(__a )
							print(f'Converting {name}...' )
							SCREAMING_SNAKE_CASE			: Dict	     =torch.load(__a )['''state_dict''']
							from_model.load_state_dict(__a )
							from_model.eval()
							with torch.no_grad():
														SCREAMING_SNAKE_CASE			: int	     =VanForImageClassification(__a ).eval()
														SCREAMING_SNAKE_CASE			: Tuple	     =ModuleTransfer(src=__a	,	dest=__a )
														SCREAMING_SNAKE_CASE			: Any	     =torch.randn((1, 3, 224, 224) )
														module_transfer(__a )
														SCREAMING_SNAKE_CASE			: str	     =copy_parameters(__a	,	__a )

							if not torch.allclose(from_model(__a )	,	our_model(__a ).logits ):
														raise ValueError('''The model logits don\'t match the original one.''' )

							SCREAMING_SNAKE_CASE			: Tuple	     =name
							print(__a )

							if push_to_hub:
														our_model.push_to_hub(
														    repo_path_or_name=save_directory / checkpoint_name	,	commit_message='''Add model'''	,	use_temp_dir=__a	,	)

														# we can use the convnext one
														SCREAMING_SNAKE_CASE			: Tuple	     =AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''' )
														image_processor.push_to_hub(
														    repo_path_or_name=save_directory / checkpoint_name	,	commit_message='''Add image processor'''	,	use_temp_dir=__a	,	)

														print(f'Pushed {checkpoint_name}' )
def 						lowerCAmelCase_       (	__a	,	__a = None	,	__a = True )       ->			int:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: Tuple	     ='''imagenet-1k-id2label.json'''
							SCREAMING_SNAKE_CASE			: Tuple	     =1000

							SCREAMING_SNAKE_CASE			: Optional[Any]	     ='''huggingface/label-files'''
							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =num_labels
							SCREAMING_SNAKE_CASE			: Optional[Any]	     =json.load(open(hf_hub_download(__a	,	__a	,	repo_type='''dataset''' )	,	'''r''' ) )
							SCREAMING_SNAKE_CASE			: Union[str, Any]	     ={int(__a ): v for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE			: Optional[int]	     =idalabel
							SCREAMING_SNAKE_CASE			: Tuple	     ={v: k for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE			: Tuple	     =partial(__a	,	num_labels=__a	,	idalabel=__a	,	labelaid=__a )

							SCREAMING_SNAKE_CASE			: int	     ={
							    '''van-tiny''': ImageNetPreTrainedConfig(
							        hidden_sizes=[32, 64, 160, 256]	,	depths=[3, 3, 5, 2]	,	mlp_ratios=[8, 8, 4, 4]	,	),
							    '''van-small''': ImageNetPreTrainedConfig(
							        hidden_sizes=[64, 128, 320, 512]	,	depths=[2, 2, 4, 2]	,	mlp_ratios=[8, 8, 4, 4]	,	),
							    '''van-base''': ImageNetPreTrainedConfig(
							        hidden_sizes=[64, 128, 320, 512]	,	depths=[3, 3, 12, 3]	,	mlp_ratios=[8, 8, 4, 4]	,	),
							    '''van-large''': ImageNetPreTrainedConfig(
							        hidden_sizes=[64, 128, 320, 512]	,	depths=[3, 5, 27, 3]	,	mlp_ratios=[8, 8, 4, 4]	,	),
							}

							SCREAMING_SNAKE_CASE			: Union[str, Any]	     ={
							    '''van-tiny''': van_tiny,
							    '''van-small''': van_small,
							    '''van-base''': van_base,
							    '''van-large''': van_large,
							}

							SCREAMING_SNAKE_CASE			: List[Any]	     ={
							    '''van-tiny''': (
							        '''https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar'''
							    ),
							    '''van-small''': (
							        '''https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar'''
							    ),
							    '''van-base''': (
							        '''https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar'''
							    ),
							    '''van-large''': (
							        '''https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar'''
							    ),
							}

							if model_name:
														convert_weight_and_push(
														    __a	,	names_to_config[model_name]	,	checkpoint=names_to_original_checkpoints[model_name]	,	from_model=names_to_original_models[model_name]()	,	save_directory=__a	,	push_to_hub=__a	,	)
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(
																					    __a	,	__a	,	checkpoint=names_to_original_checkpoints[model_name]	,	from_model=names_to_original_models[model_name]()	,	save_directory=__a	,	push_to_hub=__a	,	)


if __name__ == "__main__":
							_A							    =	argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--model-name""",
							    default=None,
							    type=str,
							    help=(
							        """The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
							        """ currently: van-tiny/small/base/large. If `None`, all of them will the converted."""
							    ),
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""",
							    default=None,
							    type=Path,
							    required=True,
							    help="""Path to the output PyTorch model directory.""",
							)
							parser.add_argument(
							    """--van_dir""",
							    required=True,
							    type=Path,
							    help=(
							        """A path to VAN's original implementation directory. You can download from here:"""
							        """ https://github.com/Visual-Attention-Network/VAN-Classification"""
							    ),
							)
							parser.add_argument(
							    """--push_to_hub""",
							    default=True,
							    type=bool,
							    required=False,
							    help="""If True, push model and image processor to the hub.""",
							)

							_A							    =	parser.parse_args()
							_A							    =	args.pytorch_dump_folder_path
							pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
							_A							    =	args.van_dir
							# append the path to the parents to maskformer dir
							sys.path.append(str(van_dir.parent))
							from van.models.van import van_base, van_large, van_small, van_tiny

							convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)






