


'''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()
UpperCAmelCase_		:       Any							       =		logging.get_logger(__name__)





@dataclass
class    lowercase__  :





							'''simple docstring'''


							A_							:				nn.Module
							A_							:				List[nn.Module]				     =  field(default_factory=_snake_case )
							A_							:				list				     =  field(default_factory=_snake_case )






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case   , __snake_case  ):
											_SCREAMING_SNAKE_CASE			:	Any							=				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   , __snake_case  ):
											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  ):
											# check the len of the state_dict keys to see if we have learnable params
											return list(filter(lambda __snake_case  : len(list(x.state_dict().keys()  )  ) > 0   , self.traced  )  )





@dataclass
class    lowercase__  :





							'''simple docstring'''


							A_							:				nn.Module
							A_							:				nn.Module
							A_							:				int				     =  0
							A_							:				List				     =  field(default_factory=_snake_case )
							A_							:				List				     =  field(default_factory=_snake_case )






							def __call__(   self   , __snake_case  ):
											_SCREAMING_SNAKE_CASE			:	Dict							=				Tracker(self.dest  )(__snake_case  ).parametrized
											_SCREAMING_SNAKE_CASE			:	Any							=				Tracker(self.src  )(__snake_case  ).parametrized

											_SCREAMING_SNAKE_CASE			:	List[Any]							=				list(filter(lambda __snake_case  : type(__snake_case  ) not in self.src_skip   , __snake_case  )  )
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				list(filter(lambda __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       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





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

												all_keys.append((from_key, to_key)					)
												_SCREAMING_SNAKE_CASE			:	List[str]							=				f"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
												_SCREAMING_SNAKE_CASE			:	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:
								_SCREAMING_SNAKE_CASE			:	str							=				from_state_dict.pop(SCREAMING_SNAKE_CASE__					)

				our_model.load_state_dict(SCREAMING_SNAKE_CASE__					)
				return our_model







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__ = True							,				):







				"""simple docstring"""





				print(f"""Downloading weights for {name}..."""					)
				_SCREAMING_SNAKE_CASE			:	str							=				cached_download(SCREAMING_SNAKE_CASE__					)
				print(f"""Converting {name}..."""					)
				_SCREAMING_SNAKE_CASE			:	Tuple							=				torch.load(SCREAMING_SNAKE_CASE__					)["""state_dict"""]
				from_model.load_state_dict(SCREAMING_SNAKE_CASE__					)
				from_model.eval()
				with torch.no_grad():
								_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				VanForImageClassification(SCREAMING_SNAKE_CASE__					).eval()
								_SCREAMING_SNAKE_CASE			:	Optional[int]							=				ModuleTransfer(src=SCREAMING_SNAKE_CASE__							,				dest=SCREAMING_SNAKE_CASE__					)
								_SCREAMING_SNAKE_CASE			:	str							=				torch.randn((1, 3, 224, 224)					)
								module_transfer(SCREAMING_SNAKE_CASE__					)
								_SCREAMING_SNAKE_CASE			:	List[str]							=				copy_parameters(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)

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

				_SCREAMING_SNAKE_CASE			:	Tuple							=				name
				print(SCREAMING_SNAKE_CASE__					)

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

								# we can use the convnext one
								_SCREAMING_SNAKE_CASE			:	Tuple							=				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=SCREAMING_SNAKE_CASE__							,				)

								print(f"""Pushed {checkpoint_name}"""					)







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__ = None							,				SCREAMING_SNAKE_CASE__ = True					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	str							=				"""imagenet-1k-id2label.json"""
				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				1000

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

				_SCREAMING_SNAKE_CASE			:	Dict							=				idalabel
				_SCREAMING_SNAKE_CASE			:	int							=				{v: k for k, v in idalabel.items()}

				_SCREAMING_SNAKE_CASE			:	int							=				partial(SCREAMING_SNAKE_CASE__							,				num_labels=SCREAMING_SNAKE_CASE__							,				idalabel=SCREAMING_SNAKE_CASE__							,				labelaid=SCREAMING_SNAKE_CASE__					)

				_SCREAMING_SNAKE_CASE			:	Tuple							=				{
				    """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]							,				),
				}

				_SCREAMING_SNAKE_CASE			:	List[Any]							=				{
				    """van-tiny""": van_tiny,
				    """van-small""": van_small,
				    """van-base""": van_base,
				    """van-large""": van_large,
				}

				_SCREAMING_SNAKE_CASE			:	Dict							=				{
				    """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(
								    SCREAMING_SNAKE_CASE__							,				names_to_config[model_name]							,				checkpoint=names_to_original_checkpoints[model_name]							,				from_model=names_to_original_models[model_name]()							,				save_directory=SCREAMING_SNAKE_CASE__							,				push_to_hub=SCREAMING_SNAKE_CASE__							,				)
				else:
								for model_name, config in names_to_config.items():
												convert_weight_and_push(
												    SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				checkpoint=names_to_original_checkpoints[model_name]							,				from_model=names_to_original_models[model_name]()							,				save_directory=SCREAMING_SNAKE_CASE__							,				push_to_hub=SCREAMING_SNAKE_CASE__							,				)


if __name__ == "__main__":
				UpperCAmelCase_		:       List[str]							       =		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_		:       Any							       =		parser.parse_args()
				UpperCAmelCase_		:       Path							       =		args.pytorch_dump_folder_path
				pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
				UpperCAmelCase_		:       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)








'''simple docstring'''


def       snake_case_       (  SCREAMING_SNAKE_CASE__ = 1000					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				[0, 3, 3, 5, 4, 4, 3, 5, 5, 4, 3, 6, 6, 8, 8, 7, 7, 9, 8, 8]
				# number of letters in twenty, thirty, ..., ninety (0 for numbers less than
				# 20 due to inconsistency in teens)
				_SCREAMING_SNAKE_CASE			:	str							=				[0, 0, 6, 6, 5, 5, 5, 7, 6, 6]

				_SCREAMING_SNAKE_CASE			:	Any							=				0

				for i in range(1							,				n + 1					):
								if i < 1000:
												if i >= 100:
																# add number of letters for "n hundred"
																count += ones_counts[i // 100] + 7

																if i % 100 != 0:
																				# add number of letters for "and" if number is not multiple
																				# of 100
																				count += 3

												if 0 < i % 100 < 20:
																# add number of letters for one, two, three, ..., nineteen
																# (could be combined with below if not for inconsistency in
																# teens)
																count += ones_counts[i % 100]
												else:
																# add number of letters for twenty, twenty one, ..., ninety
																# nine
																count += ones_counts[i % 10]
																count += tens_counts[(i % 100 - i % 10) // 10]
								else:
												count += ones_counts[i // 1000] + 8
				return count


if __name__ == "__main__":
				print(solution(int(input().strip())))




