





'''simple docstring'''







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      :				Optional[int]											=					logging.get_logger(__name__)





@dataclass
class 					_UpperCAmelCase							:
		a     :     Union[str, Any]     						=42
		a     :     Optional[int]     						=field(default_factory=UpperCamelCase__     )
		a     :     Optional[int]     						=field(default_factory=UpperCamelCase__     )
		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       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,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					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 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					return list(filter(lambda __SCREAMING_SNAKE_CASE    : len(list(x.state_dict().keys()    )    ) > 0,self.traced    )    )





@dataclass
class 					_UpperCAmelCase							:
		a     :     List[str]     						=42
		a     :     Dict     						=42
		a     :     List[Any]     						=0
		a     :     Tuple     						=field(default_factory=UpperCamelCase__     )
		a     :     Union[str, Any]     						=field(default_factory=UpperCamelCase__     )
		def __call__(						self,__SCREAMING_SNAKE_CASE    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       Tracker(self.dest    )(snake_case_    ).parametrized
					__lowerCAmelCase      		=       Tracker(self.src    )(snake_case_    ).parametrized

					__lowerCAmelCase      		=       list(filter(lambda __SCREAMING_SNAKE_CASE    : type(snake_case_    ) not in self.src_skip,snake_case_    )    )
					__lowerCAmelCase      		=       list(filter(lambda __SCREAMING_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		(		lowercase      ,			lowercase			)      ->      nn.Module:
			__lowerCAmelCase      		=       from_model.state_dict()
			__lowerCAmelCase      		=       our_model.state_dict()
			__lowerCAmelCase      		=       our_model.config
			__lowerCAmelCase      		=       []
			for stage_idx in range(len(config.hidden_sizes			)			):
						for block_id in range(config.depths[stage_idx]			):
									__lowerCAmelCase      		=       f'block{stage_idx + 1}.{block_id}.layer_scale_1'
									__lowerCAmelCase      		=       f'van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'

									all_keys.append((from_key, to_key)			)
									__lowerCAmelCase      		=       f'block{stage_idx + 1}.{block_id}.layer_scale_2'
									__lowerCAmelCase      		=       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:
						__lowerCAmelCase      		=       from_state_dict.pop(__a			)

			our_model.load_state_dict(__a			)
			return our_model





def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase      ,			lowercase      ,			lowercase      ,			lowercase = True      ,			)      ->      Any:
			print(f'Downloading weights for {name}...'			)
			__lowerCAmelCase      		=       cached_download(__a			)
			print(f'Converting {name}...'			)
			__lowerCAmelCase      		=       torch.load(__a			)['''state_dict''']
			from_model.load_state_dict(__a			)
			from_model.eval()
			with torch.no_grad():
						__lowerCAmelCase      		=       VanForImageClassification(__a			).eval()
						__lowerCAmelCase      		=       ModuleTransfer(src=__a      ,			dest=__a			)
						__lowerCAmelCase      		=       torch.randn((1, 3, 224, 224)			)
						module_transfer(__a			)
						__lowerCAmelCase      		=       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."""			)

			__lowerCAmelCase      		=       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
						__lowerCAmelCase      		=       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		(		lowercase      ,			lowercase = None      ,			lowercase = True			)      ->      int:
			__lowerCAmelCase      		=       '''imagenet-1k-id2label.json'''
			__lowerCAmelCase      		=       1000

			__lowerCAmelCase      		=       '''huggingface/label-files'''
			__lowerCAmelCase      		=       num_labels
			__lowerCAmelCase      		=       json.load(open(hf_hub_download(__a      ,			__a      ,			repo_type="""dataset"""			)      ,			"""r"""			)			)
			__lowerCAmelCase      		=       {int(__a			): v for k, v in idalabel.items()}

			__lowerCAmelCase      		=       idalabel
			__lowerCAmelCase      		=       {v: k for k, v in idalabel.items()}

			__lowerCAmelCase      		=       partial(__a      ,			num_labels=__a      ,			idalabel=__a      ,			labelaid=__a			)

			__lowerCAmelCase      		=       {
			    '''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]      ,			),
			}

			__lowerCAmelCase      		=       {
			    '''van-tiny''': van_tiny,
			    '''van-small''': van_small,
			    '''van-base''': van_base,
			    '''van-large''': van_large,
			}

			__lowerCAmelCase      		=       {
			    '''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      :				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: 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      :				Optional[int]											=					parser.parse_args()
	_a      :				str											=					args.pytorch_dump_folder_path
	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
	_a      :				List[str]											=					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)






'''simple docstring'''







from cva import destroyAllWindows, imread, imshow, waitKey





def      _lowerCAmelCase		(		lowercase      ,			lowercase			)      ->      int:
			__lowerCAmelCase		, __lowerCAmelCase      		=       img.shape[0], img.shape[1]

			def to_grayscale(lowercase      ,			lowercase      ,			lowercase			):
						return 0.21_26 * red + 0.5_87 * green + 0.1_14 * blue

			def normalize(lowercase			):
						return min(lowercase      ,			255			)

			for i in range(lowercase			):
						for j in range(lowercase			):
									__lowerCAmelCase      		=       int(to_grayscale(*img[i][j]			)			)
									__lowerCAmelCase      		=       [
									    normalize(lowercase			),
									    normalize(greyscale + factor			),
									    normalize(greyscale + 2 * factor			),
									]

			return img


if __name__ == "__main__":
	# read original image
	_a      :				Any											=					{
	    percentage: imread("""image_data/lena.jpg""", 1) for percentage in (1_0, 2_0, 3_0, 4_0)
	}

	for percentage, img in images.items():
		make_sepia(img, percentage)

	for percentage, img in images.items():
		imshow(f'Original image with sepia (factor: {percentage})', img)

	waitKey(0)
	destroyAllWindows()

