


'''simple docstring'''

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

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase      :Dict    	=			logging.get_logger()
@dataclass
class _lowerCamelCase       :



		'''simple docstring'''







		A_	:			str          =				42
		A_	:			Optional[Any]          =				field(default_factory=__UpperCamelCase							)
		A_	:			List[str]          =				field(default_factory=__UpperCamelCase							)







		def    __lowerCAmelCase		(					self		:						List[str]  ,     _A		:						Dict  ,     _A		:						Tensor  ,     _A		:						Tensor			)  -> Optional[Any]:
			__magic_name__       :    Dict     	=     len(list(m.modules()			)			) == 1 or isinstance(a_  ,     nn.Convad			) or isinstance(a_  ,     nn.BatchNormad			)
			if has_not_submodules:
				self.traced.append(a_			)







		def __call__(					self		:						List[Any]  ,     _A		:						Tensor			)  -> Any:





			for m in self.module.modules():
				self.handles.append(m.register_forward_hook(self._forward_hook			)			)
			self.module(a_			)
			[x.remove() for x in self.handles]
			return self







		@property
		def    __lowerCAmelCase		(					self		:						Optional[int]			)  -> str:
			return list(filter(lambda _A			: len(list(x.state_dict().keys()			)			) > 0  ,     self.traced			)			)







@dataclass
class _lowerCamelCase       :



		'''simple docstring'''







		A_	:			int          =				42
		A_	:			Any          =				42
		A_	:			List[str]          =				0
		A_	:			Optional[Any]          =				field(default_factory=__UpperCamelCase							)
		A_	:			Dict          =				field(default_factory=__UpperCamelCase							)







		def __call__(					self		:						str  ,     _A		:						Tensor			)  -> Union[str, Any]:
			__magic_name__       :    List[str]     	=     Tracker(self.dest			)(a_			).parametrized
			__magic_name__       :    Tuple     	=     Tracker(self.src			)(a_			).parametrized

			__magic_name__       :    int     	=     list(filter(lambda _A			: type(a_			) not in self.src_skip  ,     a_			)			)
			__magic_name__       :    str     	=     list(filter(lambda _A			: type(a_			) not in self.dest_skip  ,     a_			)			)

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

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







def lowerCamelCase			(			lowerCAmelCase				:	str		,		lowerCAmelCase				:	ResNetConfig		,		lowerCAmelCase				:	Path		,		lowerCAmelCase				:	bool = True		):





	"""simple docstring"""







	print(f'Converting {name}...'		)
	with torch.no_grad():
		__magic_name__       :    Optional[Any]     	=     timm.create_model(_UpperCAmelCase		,		pretrained=_UpperCAmelCase		).eval()
		__magic_name__       :    int     	=     ResNetForImageClassification(_UpperCAmelCase		).eval()
		__magic_name__       :    List[Any]     	=     ModuleTransfer(src=_UpperCAmelCase		,		dest=_UpperCAmelCase		)
		__magic_name__       :    Union[str, Any]     	=     torch.randn((1, 3, 224, 224)		)
		module_transfer(_UpperCAmelCase		)

	assert torch.allclose(from_model(_UpperCAmelCase		)		,		our_model(_UpperCAmelCase		).logits		), "The model logits don't match the original one."

	__magic_name__       :    Optional[int]     	=     f'resnet{"-".join(name.split("resnet"		)		)}'
	print(_UpperCAmelCase		)

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

		# we can use the convnext one
		__magic_name__       :    Optional[int]     	=     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=_UpperCAmelCase		,		)

		print(f'Pushed {checkpoint_name}'		)





def lowerCamelCase			(			lowerCAmelCase				:	Path		,		lowerCAmelCase				:	str = None		,		lowerCAmelCase				:	bool = True		):





	"""simple docstring"""







	__magic_name__       :    str     	=     '''imagenet-1k-id2label.json'''
	__magic_name__       :    int     	=     1000
	__magic_name__       :    Optional[int]     	=     (1, num_labels)

	__magic_name__       :    Any     	=     '''huggingface/label-files'''
	__magic_name__       :    Tuple     	=     num_labels
	__magic_name__       :    Tuple     	=     json.load(open(hf_hub_download(_UpperCAmelCase		,		_UpperCAmelCase		,		repo_type='dataset'		)		,		'r'		)		)
	__magic_name__       :    str     	=     {int(_UpperCAmelCase		): v for k, v in idalabel.items()}

	__magic_name__       :    Any     	=     idalabel
	__magic_name__       :    str     	=     {v: k for k, v in idalabel.items()}

	__magic_name__       :    List[str]     	=     partial(_UpperCAmelCase		,		num_labels=_UpperCAmelCase		,		idalabel=_UpperCAmelCase		,		labelaid=_UpperCAmelCase		)

	__magic_name__       :    Any     	=     {
	    '''resnet18''': ImageNetPreTrainedConfig(
	        depths=[2, 2, 2, 2]		,		hidden_sizes=[64, 128, 256, 512]		,		layer_type='basic'		),
	    '''resnet26''': ImageNetPreTrainedConfig(
	        depths=[2, 2, 2, 2]		,		hidden_sizes=[256, 512, 1024, 2048]		,		layer_type='bottleneck'		),
	    '''resnet34''': ImageNetPreTrainedConfig(
	        depths=[3, 4, 6, 3]		,		hidden_sizes=[64, 128, 256, 512]		,		layer_type='basic'		),
	    '''resnet50''': ImageNetPreTrainedConfig(
	        depths=[3, 4, 6, 3]		,		hidden_sizes=[256, 512, 1024, 2048]		,		layer_type='bottleneck'		),
	    '''resnet101''': ImageNetPreTrainedConfig(
	        depths=[3, 4, 23, 3]		,		hidden_sizes=[256, 512, 1024, 2048]		,		layer_type='bottleneck'		),
	    '''resnet152''': ImageNetPreTrainedConfig(
	        depths=[3, 8, 36, 3]		,		hidden_sizes=[256, 512, 1024, 2048]		,		layer_type='bottleneck'		),
	}

	if model_name:
		convert_weight_and_push(_UpperCAmelCase		,		names_to_config[model_name]		,		_UpperCAmelCase		,		_UpperCAmelCase		)
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		)
	return config, expected_shape


if __name__ == "__main__":
							lowerCAmelCase      :Optional[int]    	=			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: resnet18,26,34,50,101,152. 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(
							    '''--push_to_hub''',
							    default=True,
							    type=bool,
							    required=False,
							    help='''If True, push model and image processor to the hub.''',
							)

							lowerCAmelCase      :List[Any]    	=			parser.parse_args()
							lowerCAmelCase      :str    	=			args.pytorch_dump_folder_path
							pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
							convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros

def        a						(      _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					Optional[int]						):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	k_size // 2
		__UpperCAmelCase							,					__UpperCAmelCase    :     Optional[Any]													=	mgrid[0 - center : k_size - center, 0 - center : k_size - center]
		__UpperCAmelCase    :     Any													=	1 / (2 * pi * sigma) * exp(-(square(_UpperCAmelCase						) + square(_UpperCAmelCase						)) / (2 * square(_UpperCAmelCase						))						)
		return g

def        a						(      _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					Any ,   _UpperCAmelCase							:					List[Any]						):







		'''simple docstring'''





		__UpperCAmelCase							,					__UpperCAmelCase    :     Union[str, Any]													=	image.shape[0], image.shape[1]
		# dst image height and width
		__UpperCAmelCase    :     str													=	height - k_size + 1
		__UpperCAmelCase    :     Optional[int]													=	width - k_size + 1

		# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
		__UpperCAmelCase    :     str													=	zeros((dst_height * dst_width, k_size * k_size)						)
		__UpperCAmelCase    :     Optional[Any]													=	0
		for i, j in product(range(_UpperCAmelCase						) ,   range(_UpperCAmelCase						)						):
				__UpperCAmelCase    :     int													=	ravel(image[i : i + k_size, j : j + k_size]						)
				__UpperCAmelCase    :     Union[str, Any]													=	window
				row += 1

		#  turn the kernel into shape(k*k, 1)
		__UpperCAmelCase    :     Tuple													=	gen_gaussian_kernel(_UpperCAmelCase ,   _UpperCAmelCase						)
		__UpperCAmelCase    :     List[Any]													=	ravel(_UpperCAmelCase						)

		# reshape and get the dst image
		__UpperCAmelCase    :     Optional[Any]													=	dot(_UpperCAmelCase ,   _UpperCAmelCase						).reshape(_UpperCAmelCase ,   _UpperCAmelCase						).astype(_UpperCAmelCase						)

		return dst


if __name__ == "__main__":
				# read original image
				__A   			=imread(R"../image_data/lena.jpg")
				# turn image in gray scale value
				__A   			=cvtColor(img, COLOR_BGR2GRAY)

				# get values with two different mask size
				__A   			=gaussian_filter(gray, 3, sigma=1)
				__A   			=gaussian_filter(gray, 5, sigma=0.8)

				# show result images
				imshow("gaussian filter with 3x3 mask", gaussianaxa)
				imshow("gaussian filter with 5x5 mask", gaussianaxa)
				waitKey()





