




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()
UpperCamelCase__		=						logging.get_logger(__name__)


@dataclass
class     __SCREAMING_SNAKE_CASE							:
					_lowerCamelCase		  =			42
					_lowerCamelCase		  =			field(default_factory=lowerCamelCase  )
					_lowerCamelCase		  =			field(default_factory=lowerCamelCase  )

					def      lowercase_							(    self			,	__lowercase			,	__lowercase			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							len(list(m.modules() ) ) == 1 or isinstance(__lowercase			,	nn.Convad ) or isinstance(__lowercase			,	nn.BatchNormad )
						if has_not_submodules:
							if not isinstance(__lowercase			,	__lowercase ):
								self.traced.append(__lowercase )

					def __call__(    self			,	__lowercase ):







						'''simple docstring'''




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





					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




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


@dataclass
class     __SCREAMING_SNAKE_CASE							:
					_lowerCamelCase		  =			42
					_lowerCamelCase		  =			42
					_lowerCamelCase		  =			0
					_lowerCamelCase		  =			field(default_factory=lowerCamelCase  )
					_lowerCamelCase		  =			field(default_factory=lowerCamelCase  )

					def __call__(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							       =							Tracker(self.dest )(__lowercase ).parametrized
						lowerCamelCase_							:		Any							       =							Tracker(self.src )(__lowercase ).parametrized

						lowerCamelCase_							:		Optional[int]							       =							list(filter(lambda __lowercase : type(__lowercase ) not in self.src_skip			,	__lowercase ) )
						lowerCamelCase_							:		Union[str, Any]							       =							list(filter(lambda __lowercase : type(__lowercase ) not in self.dest_skip			,	__lowercase ) )

						if len(__lowercase ) != len(__lowercase ):
							raise Exception(
							    f"Numbers of operations are different. Source module has {len(__lowercase )} operations while"
							    f" destination module has {len(__lowercase )}." )

						for dest_m, src_m in zip(__lowercase			,	__lowercase ):
							dest_m.load_state_dict(src_m.state_dict() )
							if self.verbose == 1:
								print(f"Transfered from={src_m} to={dest_m}" )






def  snake_case (							A						,       A      )							->							nn.Module:




	'''simple docstring'''


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

			all_keys.append((from_key, to_key)      )
			lowerCamelCase_							:		Any							       =							f"block{stage_idx + 1}.{block_id}.layer_scale_2"
			lowerCamelCase_							:		Optional[int]							       =							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_							:		List[str]							       =							from_state_dict.pop(A      )

	our_model.load_state_dict(A      )
	return our_model






def  snake_case (							A						,       A						,       A						,       A						,       A						,       A = True						,       )							->							Tuple:




	'''simple docstring'''


	print(f"Downloading weights for {name}..."      )
	lowerCamelCase_							:		Tuple							       =							cached_download(A      )
	print(f"Converting {name}..."      )
	lowerCamelCase_							:		Tuple							       =							torch.load(A      )['state_dict']
	from_model.load_state_dict(A      )
	from_model.eval()
	with torch.no_grad():
		lowerCamelCase_							:		str							       =							VanForImageClassification(A      ).eval()
		lowerCamelCase_							:		int							       =							ModuleTransfer(src=A						,       dest=A      )
		lowerCamelCase_							:		Any							       =							torch.randn((1, 3, 224, 224)      )
		module_transfer(A      )
		lowerCamelCase_							:		Any							       =							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_							:		List[Any]							       =							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_							:		Any							       =							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  snake_case (							A						,       A = None						,       A = True      )							->							Optional[int]:




	'''simple docstring'''


	lowerCamelCase_							:		Any							       =							'imagenet-1k-id2label.json'
	lowerCamelCase_							:		Union[str, Any]							       =							1000

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

	lowerCamelCase_							:		str							       =							idalabel
	lowerCamelCase_							:		Union[str, Any]							       =							{v: k for k, v in idalabel.items()}

	lowerCamelCase_							:		List[str]							       =							partial(A						,       num_labels=A						,       idalabel=A						,       labelaid=A      )

	lowerCamelCase_							:		List[Any]							       =							{
	    '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_							:		Optional[Any]							       =							{
	    'van-tiny': van_tiny,
	    'van-small': van_small,
	    'van-base': van_base,
	    'van-large': van_large,
	}

	lowerCamelCase_							:		int							       =							{
	    '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__":
							UpperCamelCase__		=						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.',
							)

							UpperCamelCase__		=						parser.parse_args()
							UpperCamelCase__		=						args.pytorch_dump_folder_path
							pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
							UpperCamelCase__		=						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)






from typing import Callable






def  snake_case (							A      )							->							List[str]:




	'''simple docstring'''


	def wrapper(A      ):
		lowerCamelCase_							:		Dict							       =							function_with_docstring.__doc__
		return target_function

	return wrapper
