





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


__SCREAMING_SNAKE_CASE							:    str          =logging.getLogger(__name__)

@dataclass
class     A_		:
			_A   :Union[str, Any]  				=      field(
			    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}	)
			_A   :Union[str, Any]  				=      field(
			    default=__a      ,      metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}	)
			_A   :Optional[Any]  				=      field(
			    default='''NER'''      ,      metadata={'''help''': '''Task type to fine tune in training (e.g. NER, POS, etc)'''}	)
			_A   :Dict  				=      field(
			    default=__a      ,      metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}	)
			_A   :Any  				=      field(default=__a      ,      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.
			_A   :int  				=      field(
			    default=__a      ,      metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}      ,      )

@dataclass
class     A_		:
			_A   :Union[str, Any]  				=      field(
			    metadata={'''help''': '''The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'''}	)
			_A   :Tuple  				=      field(
			    default=__a      ,      metadata={'''help''': '''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'''}      ,      )
			_A   :List[Any]  				=      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.'''
			        )
			    }      ,      )
			_A   :str  				=      field(
			    default=__a      ,      metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}	)


def       UpperCamelCase__    (		):
				lowercase							=     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.
								lowercase					,    lowercase					,    lowercase							=     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
				else:
								lowercase					,    lowercase					,    lowercase							=     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.""" )

				lowercase							=     import_module("""tasks""" )
				try:
								lowercase							=     getattr(lowercase__       ,model_args.task_type )
								lowercase							=     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"""       ,lowercase__ )

				# Set seed
				set_seed(training_args.seed )

				# Prepare CONLL-2003 task
				lowercase							=     token_classification_task.get_labels(data_args.labels )
				lowercase							=     dict(enumerate(lowercase__ ) )
				lowercase							=     len(lowercase__ )

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

				lowercase							=     AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,num_labels=lowercase__       ,idalabel=lowercase__       ,labelaid={label: i for i, label in enumerate(lowercase__ )}       ,cache_dir=model_args.cache_dir       ,)
				lowercase							=     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       ,)
				lowercase							=     AutoModelForTokenClassification.from_pretrained(
				    model_args.model_name_or_path       ,from_tf=bool(""".ckpt""" in model_args.model_name_or_path )       ,config=lowercase__       ,cache_dir=model_args.cache_dir       ,)

				# Get datasets
				lowercase							=     (
				    TokenClassificationDataset(
				        token_classification_task=lowercase__       ,data_dir=data_args.data_dir       ,tokenizer=lowercase__       ,labels=lowercase__       ,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
				)
				lowercase							=     (
				    TokenClassificationDataset(
				        token_classification_task=lowercase__       ,data_dir=data_args.data_dir       ,tokenizer=lowercase__       ,labels=lowercase__       ,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]]:
								lowercase							=     np.argmax(lowercase__       ,axis=2 )

								lowercase					,    lowercase							=     preds.shape

								lowercase							=     [[] for _ in range(lowercase__ )]
								lowercase							=     [[] for _ in range(lowercase__ )]

								for i in range(lowercase__ ):
												for j in range(lowercase__ ):
																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:
								lowercase					,    lowercase							=     align_predictions(p.predictions       ,p.label_ids )
								return {
								    "accuracy_score": accuracy_score(lowercase__       ,lowercase__ ),
								    "precision": precision_score(lowercase__       ,lowercase__ ),
								    "recall": recall_score(lowercase__       ,lowercase__ ),
								    "f1": fa_score(lowercase__       ,lowercase__ ),
								}

				# Data collator
				lowercase							=     DataCollatorWithPadding(lowercase__       ,pad_to_multiple_of=8 ) if training_args.fpaa else None

				# Initialize our Trainer
				lowercase							=     Trainer(
				    model=lowercase__       ,args=lowercase__       ,train_dataset=lowercase__       ,eval_dataset=lowercase__       ,compute_metrics=lowercase__       ,data_collator=lowercase__       ,)

				# 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
				lowercase							=     {}
				if training_args.do_eval:
								logger.info("""*** Evaluate ***""" )

								lowercase							=     trainer.evaluate()

								lowercase							=     os.path.join(training_args.output_dir       ,"""eval_results.txt""" )
								if trainer.is_world_process_zero():
												with open(lowercase__       ,"""w""" ) as writer:
																logger.info("""***** Eval results *****""" )
																for key, value in result.items():
																				logger.info("""  %s = %s"""       ,lowercase__       ,lowercase__ )
																				writer.write("""%s = %s\n""" % (key, value) )

												results.update(lowercase__ )

    # Predict
				if training_args.do_predict:
								lowercase							=     TokenClassificationDataset(
								    token_classification_task=lowercase__       ,data_dir=data_args.data_dir       ,tokenizer=lowercase__       ,labels=lowercase__       ,model_type=config.model_type       ,max_seq_length=data_args.max_seq_length       ,overwrite_cache=data_args.overwrite_cache       ,mode=Split.test       ,)

								lowercase					,    lowercase					,    lowercase							=     trainer.predict(lowercase__ )
								lowercase					,    lowercase							=     align_predictions(lowercase__       ,lowercase__ )

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

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

				return results


def       UpperCamelCase__    (		lowerCAmelCase__ ):
				main()


if __name__ == "__main__":
					main()





'''simple docstring'''

def 					__UpperCamelCase				(						lowercase__    :	int			):






     '''simple docstring'''





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

          return sequence[n]

def 					__UpperCamelCase				(						lowercase__    :	int			):






     '''simple docstring'''





     __lowercase      		=0
     __lowercase      		=2

     while digits < n:
          index += 1
          __lowercase      		=len(str(fibonacci(lowercase__			)			)			)

     return index

def 					__UpperCamelCase				(						lowercase__    :	int = 10_00			):






     '''simple docstring'''





     return fibonacci_digits_index(lowercase__			)


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


