

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



@dataclass
class    A__:
						"""simple docstring"""






						_A  :							Any   			=	42
						_A  :							List[Any]   			=	field(default_factory=lowerCAmelCase_    )
						_A  :							Optional[int]   			=	field(default_factory=lowerCAmelCase_    )





						def 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase  )      ->				str:
									a_		:		int				    =				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       ,     _lowercase  )      ->				Optional[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 	UpperCamelCase__			(  self  )      ->				Any:
									# check the len of the state_dict keys to see if we have learnable params
									return list(filter(lambda _lowercase  : len(list(x.state_dict().keys()  )  ) > 0       ,     self.traced  )  )







@dataclass
class    A__:
						"""simple docstring"""






						_A  :							str   			=	42
						_A  :							List[str]   			=	42
						_A  :							str   			=	0
						_A  :							Tuple   			=	field(default_factory=lowerCAmelCase_    )
						_A  :							Any   			=	field(default_factory=lowerCAmelCase_    )





						def __call__(  self       ,     _lowercase  )      ->				str:
									a_		:		Optional[Any]				    =				Tracker(self.dest  )(_snake_case  ).parametrized
									a_		:		str				    =				Tracker(self.src  )(_snake_case  ).parametrized

									a_		:		Dict				    =				list(filter(lambda _lowercase  : type(_snake_case  ) not in self.src_skip       ,     _snake_case  )  )
									a_		:		List[Any]				    =				list(filter(lambda _lowercase  : 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 			_UpperCAmelCase							(		a__       ,					a__):

			'''simple docstring'''



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

									all_keys.append((from_key, to_key))
									a_		:		Dict				    =				f'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
									a_		:		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:
						a_		:		Union[str, Any]				    =				from_state_dict.pop(a__)

			our_model.load_state_dict(a__)
			return our_model


def 			_UpperCAmelCase							(		a__       ,					a__       ,					a__       ,					a__       ,					a__       ,					a__ = True       ,					):

			'''simple docstring'''



			print(f'''Downloading weights for {name}...''')
			a_		:		Dict				    =				cached_download(a__)
			print(f'''Converting {name}...''')
			a_		:		Optional[Any]				    =				torch.load(a__)['state_dict']
			from_model.load_state_dict(a__)
			from_model.eval()
			with torch.no_grad():
						a_		:		Union[str, Any]				    =				VanForImageClassification(a__).eval()
						a_		:		Tuple				    =				ModuleTransfer(src=a__       ,					dest=a__)
						a_		:		Optional[Any]				    =				torch.randn((1, 3, 2_2_4, 2_2_4))
						module_transfer(a__)
						a_		:		int				    =				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.""")

			a_		:		int				    =				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
						a_		:		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 			_UpperCAmelCase							(		a__       ,					a__ = None       ,					a__ = True):

			'''simple docstring'''



			a_		:		Dict				    =				'imagenet-1k-id2label.json'
			a_		:		List[str]				    =				1_0_0_0

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

			a_		:		List[Any]				    =				idalabel
			a_		:		int				    =				{v: k for k, v in idalabel.items()}

			a_		:		Union[str, Any]				    =				partial(a__       ,					num_labels=a__       ,					idalabel=a__       ,					labelaid=a__)

			a_		:		Union[str, Any]				    =				{
			    'van-tiny': ImageNetPreTrainedConfig(
			        hidden_sizes=[3_2, 6_4, 1_6_0, 2_5_6]       ,					depths=[3, 3, 5, 2]       ,					mlp_ratios=[8, 8, 4, 4]       ,					),
			    'van-small': ImageNetPreTrainedConfig(
			        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]       ,					depths=[2, 2, 4, 2]       ,					mlp_ratios=[8, 8, 4, 4]       ,					),
			    'van-base': ImageNetPreTrainedConfig(
			        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]       ,					depths=[3, 3, 1_2, 3]       ,					mlp_ratios=[8, 8, 4, 4]       ,					),
			    'van-large': ImageNetPreTrainedConfig(
			        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]       ,					depths=[3, 5, 2_7, 3]       ,					mlp_ratios=[8, 8, 4, 4]       ,					),
			}

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

			a_		:		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__":
	__snake_case							:     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.""",
	)

	__snake_case							:     Dict 						=	parser.parse_args()
	__snake_case							:     Path 						=	args.pytorch_dump_folder_path
	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
	__snake_case							:     Optional[int] 						=	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)









def 			_UpperCAmelCase							(		a__):

			'''simple docstring'''



			return 1 if input_a == 0 else 0


def 			_UpperCAmelCase							(		):

			'''simple docstring'''



			assert not_gate(0) == 1
			assert not_gate(1) == 0


if __name__ == "__main__":
	print(not_gate(0))
	print(not_gate(1))


