


import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_UpperCAmelCase			:  List[Any]     =logging.getLogger(__name__)






@dataclass
class 				snake_case__:







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  str					=							field(
						    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}							)
						SCREAMING_SNAKE_CASE__      :  Optional[str]					=							field(
						    default=UpperCAmelCase__,						metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}							)
						SCREAMING_SNAKE_CASE__      :  Optional[str]					=							field(
						    default="""NER""",						metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}							)
						SCREAMING_SNAKE_CASE__      :  Optional[str]					=							field(
						    default=UpperCAmelCase__,						metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}							)
						SCREAMING_SNAKE_CASE__      :  bool					=							field(default=UpperCAmelCase__,						metadata={"""help""": """Set this flag to use fast tokenization."""}							)
						# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
						# or just modify its tokenizer_config.json.
						SCREAMING_SNAKE_CASE__      :  Optional[str]					=							field(
						    default=UpperCAmelCase__,						metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""},						)






@dataclass
class 				snake_case__:







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  str					=							field(
						    metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}							)
						SCREAMING_SNAKE_CASE__      :  Optional[str]					=							field(
						    default=UpperCAmelCase__,						metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""},						)
						SCREAMING_SNAKE_CASE__      :  int					=							field(
						    default=128,						metadata={
						        """help""": (
						            """The maximum total input sequence length after tokenization. Sequences longer """
						            """than this will be truncated, sequences shorter will be padded."""
						        )
						    },						)
						SCREAMING_SNAKE_CASE__      :  bool					=							field(
						    default=UpperCAmelCase__,						metadata={"""help""": """Overwrite the cached training and evaluation sets"""}							)






def 			lowerCAmelCase (		)-> str:
				lowerCAmelCase_ :      int    				=				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
				if len(sys.argv ) == 2 and sys.argv[1].endswith('''.json''' ):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								lowerCAmelCase_ :      str    				=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
				else:
								lowerCAmelCase_ :      List[Any]    				=				parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir )
				    and os.listdir(training_args.output_dir )
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
								    ''' --overwrite_output_dir to overcome.''' )

				lowerCAmelCase_ :      List[str]    				=				import_module('''tasks''' )
				try:
								lowerCAmelCase_ :      Optional[int]    				=				getattr(lowerCamelCase__ ,   model_args.task_type )
								lowerCAmelCase_ :      TokenClassificationTask    				=				token_classification_task_clazz()
				except AttributeError:
								raise ValueError(
								    f"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
								    f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )

				# Setup logging
				logging.basicConfig(
				    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' ,   datefmt='''%m/%d/%Y %H:%M:%S''' ,   level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN ,   )
				logger.warning(
				    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' ,   training_args.local_rank ,   training_args.device ,   training_args.n_gpu ,   bool(training_args.local_rank != -1 ) ,   training_args.fpaa ,   )
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank ):
								transformers.utils.logging.set_verbosity_info()
								transformers.utils.logging.enable_default_handler()
								transformers.utils.logging.enable_explicit_format()
				logger.info('''Training/evaluation parameters %s''' ,   lowerCamelCase__ )

				# Set seed
				set_seed(training_args.seed )

				# Prepare CONLL-2003 task
				lowerCAmelCase_ :      Any    				=				token_classification_task.get_labels(data_args.labels )
				lowerCAmelCase_ :      Dict[int, str]    				=				dict(enumerate(lowerCamelCase__ ) )
				lowerCAmelCase_ :      Any    				=				len(lowerCamelCase__ )

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				lowerCAmelCase_ :      List[str]    				=				AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path ,   num_labels=lowerCamelCase__ ,   idalabel=lowerCamelCase__ ,   labelaid={label: i for i, label in enumerate(lowerCamelCase__ )} ,   cache_dir=model_args.cache_dir ,   )
				lowerCAmelCase_ :      List[Any]    				=				AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,   cache_dir=model_args.cache_dir ,   use_fast=model_args.use_fast ,   )
				lowerCAmelCase_ :      Union[str, Any]    				=				AutoModelForTokenClassification.from_pretrained(
				    model_args.model_name_or_path ,   from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) ,   config=lowerCamelCase__ ,   cache_dir=model_args.cache_dir ,   )

				# Get datasets
				lowerCAmelCase_ :      Tuple    				=				(
				    TokenClassificationDataset(
				        token_classification_task=lowerCamelCase__ ,   data_dir=data_args.data_dir ,   tokenizer=lowerCamelCase__ ,   labels=lowerCamelCase__ ,   model_type=config.model_type ,   max_seq_length=data_args.max_seq_length ,   overwrite_cache=data_args.overwrite_cache ,   mode=Split.train ,   )
				    if training_args.do_train
				    else None
				)
				lowerCAmelCase_ :      int    				=				(
				    TokenClassificationDataset(
				        token_classification_task=lowerCamelCase__ ,   data_dir=data_args.data_dir ,   tokenizer=lowerCamelCase__ ,   labels=lowerCamelCase__ ,   model_type=config.model_type ,   max_seq_length=data_args.max_seq_length ,   overwrite_cache=data_args.overwrite_cache ,   mode=Split.dev ,   )
				    if training_args.do_eval
				    else None
				)

				def align_predictions(lowerCAmelCase_ ,   lowerCAmelCase_ ) -> Tuple[List[int], List[int]]:
								lowerCAmelCase_ :      Tuple    				=				np.argmax(lowerCamelCase__ ,   axis=2 )

								lowerCAmelCase_ :      str    				=				preds.shape

								lowerCAmelCase_ :      List[Any]    				=				[[] for _ in range(lowerCamelCase__ )]
								lowerCAmelCase_ :      Union[str, Any]    				=				[[] for _ in range(lowerCamelCase__ )]

								for i in range(lowerCamelCase__ ):
												for j in range(lowerCamelCase__ ):
																if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
																				out_label_list[i].append(label_map[label_ids[i][j]] )
																				preds_list[i].append(label_map[preds[i][j]] )

								return preds_list, out_label_list

				def compute_metrics(lowerCAmelCase_ ) -> Dict:
								lowerCAmelCase_ :      Any    				=				align_predictions(p.predictions ,   p.label_ids )
								return {
								    "accuracy_score": accuracy_score(lowerCamelCase__ ,   lowerCamelCase__ ),
								    "precision": precision_score(lowerCamelCase__ ,   lowerCamelCase__ ),
								    "recall": recall_score(lowerCamelCase__ ,   lowerCamelCase__ ),
								    "f1": fa_score(lowerCamelCase__ ,   lowerCamelCase__ ),
								}

				# Data collator
				lowerCAmelCase_ :      Dict    				=				DataCollatorWithPadding(lowerCamelCase__ ,   pad_to_multiple_of=8 ) if training_args.fpaa else None

				# Initialize our Trainer
				lowerCAmelCase_ :      List[str]    				=				Trainer(
				    model=lowerCamelCase__ ,   args=lowerCamelCase__ ,   train_dataset=lowerCamelCase__ ,   eval_dataset=lowerCamelCase__ ,   compute_metrics=lowerCamelCase__ ,   data_collator=lowerCamelCase__ ,   )

				# Training
				if training_args.do_train:
								trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
								trainer.save_model()
								# For convenience, we also re-save the tokenizer to the same directory,
								# so that you can share your model easily on huggingface.co/models =)
								if trainer.is_world_process_zero():
												tokenizer.save_pretrained(training_args.output_dir )

    # Evaluation
				lowerCAmelCase_ :      List[str]    				=				{}
				if training_args.do_eval:
								logger.info('''*** Evaluate ***''' )

								lowerCAmelCase_ :      Tuple    				=				trainer.evaluate()

								lowerCAmelCase_ :      List[str]    				=				os.path.join(training_args.output_dir ,   '''eval_results.txt''' )
								if trainer.is_world_process_zero():
												with open(lowerCamelCase__ ,   '''w''' ) as writer:
																logger.info('''***** Eval results *****''' )
																for key, value in result.items():
																				logger.info('''  %s = %s''' ,   lowerCamelCase__ ,   lowerCamelCase__ )
																				writer.write('''%s = %s\n''' % (key, value) )

												results.update(lowerCamelCase__ )

    # Predict
				if training_args.do_predict:
								lowerCAmelCase_ :      List[Any]    				=				TokenClassificationDataset(
								    token_classification_task=lowerCamelCase__ ,   data_dir=data_args.data_dir ,   tokenizer=lowerCamelCase__ ,   labels=lowerCamelCase__ ,   model_type=config.model_type ,   max_seq_length=data_args.max_seq_length ,   overwrite_cache=data_args.overwrite_cache ,   mode=Split.test ,   )

								lowerCAmelCase_ :      List[Any]    				=				trainer.predict(lowerCamelCase__ )
								lowerCAmelCase_ :      int    				=				align_predictions(lowerCamelCase__ ,   lowerCamelCase__ )

								lowerCAmelCase_ :      str    				=				os.path.join(training_args.output_dir ,   '''test_results.txt''' )
								if trainer.is_world_process_zero():
												with open(lowerCamelCase__ ,   '''w''' ) as writer:
																for key, value in metrics.items():
																				logger.info('''  %s = %s''' ,   lowerCamelCase__ ,   lowerCamelCase__ )
																				writer.write('''%s = %s\n''' % (key, value) )

        # Save predictions
								lowerCAmelCase_ :      Any    				=				os.path.join(training_args.output_dir ,   '''test_predictions.txt''' )
								if trainer.is_world_process_zero():
												with open(lowerCamelCase__ ,   '''w''' ) as writer:
																with open(os.path.join(data_args.data_dir ,   '''test.txt''' ) ,   '''r''' ) as f:
																				token_classification_task.write_predictions_to_file(lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ )

				return results



def 			lowerCAmelCase (		lowerCAmelCase_ )-> Optional[int]:
				main()


if __name__ == "__main__":
						main()



def 			lowerCAmelCase (		lowerCAmelCase_ = 1_000_000 )-> int:
				lowerCAmelCase_ :      Dict    				=				1
				lowerCAmelCase_ :      List[Any]    				=				1
				lowerCAmelCase_ :      Optional[Any]    				=				{1: 1}

				for inputa in range(2 ,   lowerCAmelCase_ ):
								lowerCAmelCase_ :      Tuple    				=				0
								lowerCAmelCase_ :      Dict    				=				inputa

								while True:
												if number in counters:
																counter += counters[number]
																break
												if number % 2 == 0:
																number //= 2
																counter += 1
												else:
																lowerCAmelCase_ :      Any    				=				(3 * number) + 1
																counter += 1

								if inputa not in counters:
												lowerCAmelCase_ :      Tuple    				=				counter

								if counter > pre_counter:
												lowerCAmelCase_ :      Optional[int]    				=				inputa
												lowerCAmelCase_ :      Union[str, Any]    				=				counter
				return largest_number


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