"""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							=logging.get_logger(__name__)






@dataclass
class        __UpperCAmelCase						:
				A__				:       nn.Module
				A__				:       List[nn.Module]  							=	field(default_factory=__lowerCAmelCase     )
				A__				:       list  							=	field(default_factory=__lowerCAmelCase     )

				def 		_a     (				self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase				):
										lowerCamelCase__ =len(list(m.modules()				)				) == 1 or isinstance(_lowerCamelCase     ,			nn.Convad				) or isinstance(_lowerCamelCase     ,			nn.BatchNormad				)
										if has_not_submodules:
																if not isinstance(_lowerCamelCase     ,			_lowerCamelCase				):
																						self.traced.append(_lowerCamelCase				)

				def __call__(				self     ,			_lowerCamelCase				):
										for m in self.module.modules():
																self.handles.append(m.register_forward_hook(self._forward_hook				)				)
										self.module(_lowerCamelCase				)
										[x.remove() for x in self.handles]
										return self






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






@dataclass
class        __UpperCAmelCase						:
				A__				:       nn.Module
				A__				:       nn.Module
				A__				:       int  							=	0
				A__				:       List  							=	field(default_factory=__lowerCAmelCase     )
				A__				:       List  							=	field(default_factory=__lowerCAmelCase     )

				def __call__(				self     ,			_lowerCamelCase				):
										lowerCamelCase__ =Tracker(self.dest				)(_lowerCamelCase				).parametrized
										lowerCamelCase__ =Tracker(self.src				)(_lowerCamelCase				).parametrized

										lowerCamelCase__ =list(filter(lambda _lowerCamelCase				: type(_lowerCamelCase				) not in self.src_skip     ,			_lowerCamelCase				)				)
										lowerCamelCase__ =list(filter(lambda _lowerCamelCase				: type(_lowerCamelCase				) not in self.dest_skip     ,			_lowerCamelCase				)				)

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

										for dest_m, src_m in zip(_lowerCamelCase     ,			_lowerCamelCase				):
																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       ,   __lowerCAmelCase    )							-> nn.Module:




						'''simple docstring'''



						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(__lowerCAmelCase    )

						our_model.load_state_dict(__lowerCAmelCase    )
						return our_model





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase = True       ,   )							-> Tuple:




						'''simple docstring'''



						print(F'''Downloading weights for {name}...'''    )
						lowerCamelCase__ =cached_download(__lowerCAmelCase    )
						print(F'''Converting {name}...'''    )
						lowerCamelCase__ =torch.load(__lowerCAmelCase    )["state_dict"]
						from_model.load_state_dict(__lowerCAmelCase    )
						from_model.eval()
						with torch.no_grad():
												lowerCamelCase__ =VanForImageClassification(__lowerCAmelCase    ).eval()
												lowerCamelCase__ =ModuleTransfer(src=__lowerCAmelCase       ,   dest=__lowerCAmelCase    )
												lowerCamelCase__ =torch.randn((1, 3, 224, 224)    )
												module_transfer(__lowerCAmelCase    )
												lowerCamelCase__ =copy_parameters(__lowerCAmelCase       ,   __lowerCAmelCase    )

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

						lowerCamelCase__ =name
						print(__lowerCAmelCase    )

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

												# 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=__lowerCAmelCase       ,   )

												print(F'''Pushed {checkpoint_name}'''    )





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase = None       ,   __lowerCAmelCase = True    )							-> List[str]:




						'''simple docstring'''



						lowerCamelCase__ ="imagenet-1k-id2label.json"
						lowerCamelCase__ =1000

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

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

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

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


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)



"""simple docstring"""


class        __UpperCAmelCase						:

				def __init__(				self     ,			_lowerCamelCase				):
										lowerCamelCase__ =data
										lowerCamelCase__ =None
										lowerCamelCase__ =None





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> BinaryTreeNode | None:




						'''simple docstring'''



						if node is None:
												lowerCamelCase__ =BinaryTreeNode(__lowerCAmelCase    )
												return node

						# binary search tree is not empty,
						# so we will insert it into the tree
						# if new_value is less than value of data in node,
						#  add it to left subtree and proceed recursively
						if new_value < node.data:
												lowerCamelCase__ =insert(node.left_child       ,   __lowerCAmelCase    )
						else:
												# if new_value is greater than value of data in node,
												#  add it to right subtree and proceed recursively
												lowerCamelCase__ =insert(node.right_child       ,   __lowerCAmelCase    )
						return node





def       lowerCamelCase_			( __lowerCAmelCase    )							-> list[int]:  # if node is None,return




						'''simple docstring'''



						if node:
												lowerCamelCase__ =inorder(node.left_child    )
												lowerCamelCase__ =[*inorder_array, node.data]
												lowerCamelCase__ =inorder_array + inorder(node.right_child    )
						else:
												lowerCamelCase__ =[]
						return inorder_array





def       lowerCamelCase_			( )							-> BinaryTreeNode | None:




						'''simple docstring'''



						lowerCamelCase__ =insert(__lowerCAmelCase       ,   15    )
						insert(__lowerCAmelCase       ,   10    )
						insert(__lowerCAmelCase       ,   25    )
						insert(__lowerCAmelCase       ,   6    )
						insert(__lowerCAmelCase       ,   14    )
						insert(__lowerCAmelCase       ,   20    )
						insert(__lowerCAmelCase       ,   60    )
						return root





def       lowerCamelCase_			( )							-> None:




						'''simple docstring'''



						lowerCamelCase__ =make_tree()
						print("Printing values of binary search tree in Inorder Traversal."    )
						inorder(__lowerCAmelCase    )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()


