
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE    :      int          =			logging.get_logger()







@dataclass
class  UpperCamelCase__		:







	'''simple docstring'''
	_lowerCAmelCase							       =				42
	_lowerCAmelCase							       =				field(default_factory=SCREAMING_SNAKE_CASE_   )
	_lowerCAmelCase							       =				field(default_factory=SCREAMING_SNAKE_CASE_   )





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	List[str]         =							len(list(m.modules()		)		) == 1 or isinstance(UpperCamelCase__				,       nn.Convad		) or isinstance(UpperCamelCase__				,       nn.BatchNormad		)
					if has_not_submodules:
									self.traced.append(UpperCamelCase__		)





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





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







@dataclass
class  UpperCamelCase__		:







	'''simple docstring'''
	_lowerCAmelCase							       =				42
	_lowerCAmelCase							       =				42
	_lowerCAmelCase							       =				0
	_lowerCAmelCase							       =				field(default_factory=SCREAMING_SNAKE_CASE_   )
	_lowerCAmelCase							       =				field(default_factory=SCREAMING_SNAKE_CASE_   )





	def __call__(			self				,       UpperCamelCase__		):
					A__  :	Any         =							Tracker(self.dest		)(UpperCamelCase__		).parametrized
					A__  :	Optional[Any]         =							Tracker(self.src		)(UpperCamelCase__		).parametrized

					A__  :	Tuple         =							list(filter(lambda UpperCamelCase__		: type(UpperCamelCase__		) not in self.src_skip				,       UpperCamelCase__		)		)
					A__  :	Union[str, Any]         =							list(filter(lambda UpperCamelCase__		: type(UpperCamelCase__		) not in self.dest_skip				,       UpperCamelCase__		)		)

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

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




def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						str							,						__UpperCamelCase :						ResNetConfig							,						__UpperCamelCase :						Path							,						__UpperCamelCase :						bool = True       ) ->       Union[str, Any]:

				"""simple docstring"""







				print(F"Converting {name}..."       )
				with torch.no_grad():
								A__  :	List[str]         =							timm.create_model(__UpperCamelCase							,						pretrained=__UpperCamelCase       ).eval()
								A__  :	List[str]         =							ResNetForImageClassification(__UpperCamelCase       ).eval()
								A__  :	List[Any]         =							ModuleTransfer(src=__UpperCamelCase							,						dest=__UpperCamelCase       )
								A__  :	Any         =							torch.randn((1, 3, 2_24, 2_24)       )
								module_transfer(__UpperCamelCase       )

				assert torch.allclose(from_model(__UpperCamelCase       )							,						our_model(__UpperCamelCase       ).logits       ), "The model logits don't match the original one."

				A__  :	Union[str, Any]         =							F"resnet{'-'.join(name.split('resnet'       )       )}"
				print(__UpperCamelCase       )

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

								# we can use the convnext one
								A__  :	int         =							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=__UpperCamelCase							,						)

								print(F"Pushed {checkpoint_name}"       )

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Path							,						__UpperCamelCase :						str = None							,						__UpperCamelCase :						bool = True       ) ->       Dict:

				"""simple docstring"""







				A__  :	Optional[int]         =							'''imagenet-1k-id2label.json'''
				A__  :	Optional[int]         =							10_00
				A__  :	Optional[int]         =							(1, num_labels)

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

				A__  :	str         =							idalabel
				A__  :	List[str]         =							{v: k for k, v in idalabel.items()}

				A__  :	Dict         =							partial(__UpperCamelCase							,						num_labels=__UpperCamelCase							,						idalabel=__UpperCamelCase							,						labelaid=__UpperCamelCase       )

				A__  :	str         =							{
				    '''resnet18''': ImageNetPreTrainedConfig(
				        depths=[2, 2, 2, 2]							,						hidden_sizes=[64, 1_28, 2_56, 5_12]							,						layer_type='''basic'''       ),
				    '''resnet26''': ImageNetPreTrainedConfig(
				        depths=[2, 2, 2, 2]							,						hidden_sizes=[2_56, 5_12, 10_24, 20_48]							,						layer_type='''bottleneck'''       ),
				    '''resnet34''': ImageNetPreTrainedConfig(
				        depths=[3, 4, 6, 3]							,						hidden_sizes=[64, 1_28, 2_56, 5_12]							,						layer_type='''basic'''       ),
				    '''resnet50''': ImageNetPreTrainedConfig(
				        depths=[3, 4, 6, 3]							,						hidden_sizes=[2_56, 5_12, 10_24, 20_48]							,						layer_type='''bottleneck'''       ),
				    '''resnet101''': ImageNetPreTrainedConfig(
				        depths=[3, 4, 23, 3]							,						hidden_sizes=[2_56, 5_12, 10_24, 20_48]							,						layer_type='''bottleneck'''       ),
				    '''resnet152''': ImageNetPreTrainedConfig(
				        depths=[3, 8, 36, 3]							,						hidden_sizes=[2_56, 5_12, 10_24, 20_48]							,						layer_type='''bottleneck'''       ),
				}

				if model_name:
								convert_weight_and_push(__UpperCamelCase							,						names_to_config[model_name]							,						__UpperCamelCase							,						__UpperCamelCase       )
				else:
								for model_name, config in names_to_config.items():
												convert_weight_and_push(__UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase							,						__UpperCamelCase       )
				return config, expected_shape


if __name__ == "__main__":
					_SCREAMING_SNAKE_CASE    :      Dict          =			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: resnet18,26,34,50,101,152. 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(
					    '--push_to_hub',
					    default=True,
					    type=bool,
					    required=False,
					    help='If True, push model and image processor to the hub.',
					)

					_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			parser.parse_args()
					_SCREAMING_SNAKE_CASE    :      Path          =			args.pytorch_dump_folder_path
					pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
					convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int       ) ->       int:

				"""simple docstring"""







				if n == 1 or not isinstance(__UpperCamelCase							,						__UpperCamelCase       ):
								return 0
				elif n == 2:
								return 1
				else:
								A__  :	Any         =							[0, 1]
								for i in range(2							,						n + 1       ):
												sequence.append(sequence[i - 1] + sequence[i - 2]       )

								return sequence[n]

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int       ) ->       int:

				"""simple docstring"""







				A__  :	Dict         =							0
				A__  :	Optional[int]         =							2

				while digits < n:
								index += 1
								A__  :	Dict         =							len(str(fibonacci(__UpperCamelCase       )       )       )

				return index

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						int = 10_00       ) ->       int:

				"""simple docstring"""







				return fibonacci_digits_index(__UpperCamelCase       )


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