






"""simple docstring"""



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


A_    =							logging.getLogger(__name__)






@dataclass
class 							__SCREAMING_SNAKE_CASE						:
		snake_case_     =							field(
		    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}		)
		snake_case_     =							field(
		    default=UpperCamelCase_       ,		metadata={'help': 'Pretrained config name or path if not the same as model_name'}		)
		snake_case_     =							field(
		    default='NER'       ,		metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'}		)
		snake_case_     =							field(
		    default=UpperCamelCase_       ,		metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}		)
		snake_case_     =							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.
		snake_case_     =							field(
		    default=UpperCamelCase_       ,		metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}       ,		)






@dataclass
class 							__SCREAMING_SNAKE_CASE						:
		snake_case_     =							field(
		    metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'}		)
		snake_case_     =							field(
		    default=UpperCamelCase_       ,		metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}       ,		)
		snake_case_     =							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.'
		        )
		    }       ,		)
		snake_case_     =							field(
		    default=UpperCamelCase_       ,		metadata={'help': 'Overwrite the cached training and evaluation sets'}		)




def      _lowerCAmelCase		(							)		->int:
						A__						:  List[Any]		  =  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.
												A__						:  List[str]		  =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
						else:
												A__						:  int		  =  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."""      )

						A__						:  List[str]		  =  import_module("""tasks"""      )
						try:
												A__						:  Any		  =  getattr(UpperCAmelCase__,						model_args.task_type      )
												A__						:  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""",						UpperCAmelCase__      )

						# Set seed
						set_seed(training_args.seed      )

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

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

						A__						:  Any		  =  AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path,						num_labels=UpperCAmelCase__,						idalabel=UpperCAmelCase__,						labelaid={label: i for i, label in enumerate(UpperCAmelCase__      )},						cache_dir=model_args.cache_dir,						)
						A__						:  List[str]		  =  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,						)
						A__						:  Optional[int]		  =  AutoModelForTokenClassification.from_pretrained(
						    model_args.model_name_or_path,						from_tf=bool(""".ckpt""" in model_args.model_name_or_path      ),						config=UpperCAmelCase__,						cache_dir=model_args.cache_dir,						)

						# Get datasets
						A__						:  List[Any]		  =  (
						    TokenClassificationDataset(
						        token_classification_task=UpperCAmelCase__,						data_dir=data_args.data_dir,						tokenizer=UpperCAmelCase__,						labels=UpperCAmelCase__,						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
						)
						A__						:  int		  =  (
						    TokenClassificationDataset(
						        token_classification_task=UpperCAmelCase__,						data_dir=data_args.data_dir,						tokenizer=UpperCAmelCase__,						labels=UpperCAmelCase__,						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(UpperCAmelCase__       :					np.ndarray,						UpperCAmelCase__       :					np.ndarray      ) -> Tuple[List[int], List[int]]:
												A__						:  str		  =  np.argmax(UpperCAmelCase__,						axis=2      )

												A__						:  int		  =  preds.shape

												A__						:  Dict		  =  [[] for _ in range(UpperCAmelCase__      )]
												A__						:  Union[str, Any]		  =  [[] for _ in range(UpperCAmelCase__      )]

												for i in range(UpperCAmelCase__      ):
																		for j in range(UpperCAmelCase__      ):
																								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(UpperCAmelCase__       :					EvalPrediction      ) -> Dict:
												A__						:  Any		  =  align_predictions(p.predictions,						p.label_ids      )
												return {
												    "accuracy_score": accuracy_score(UpperCAmelCase__,						UpperCAmelCase__      ),
												    "precision": precision_score(UpperCAmelCase__,						UpperCAmelCase__      ),
												    "recall": recall_score(UpperCAmelCase__,						UpperCAmelCase__      ),
												    "f1": fa_score(UpperCAmelCase__,						UpperCAmelCase__      ),
												}

						# Data collator
						A__						:  Optional[int]		  =  DataCollatorWithPadding(UpperCAmelCase__,						pad_to_multiple_of=8      ) if training_args.fpaa else None

						# Initialize our Trainer
						A__						:  Optional[Any]		  =  Trainer(
						    model=UpperCAmelCase__,						args=UpperCAmelCase__,						train_dataset=UpperCAmelCase__,						eval_dataset=UpperCAmelCase__,						compute_metrics=UpperCAmelCase__,						data_collator=UpperCAmelCase__,						)

						# 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
						A__						:  List[Any]		  =  {}
						if training_args.do_eval:
												logger.info("""*** Evaluate ***"""      )

												A__						:  List[str]		  =  trainer.evaluate()

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

																		results.update(UpperCAmelCase__      )

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

												A__						:  str		  =  trainer.predict(UpperCAmelCase__      )
												A__						:  List[str]		  =  align_predictions(UpperCAmelCase__,						UpperCAmelCase__      )

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

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

						return results




def      _lowerCAmelCase		(							UpperCAmelCase__       :					int      )		->Any:
						main()


if __name__ == "__main__":
						main()














"""simple docstring"""



def      _lowerCAmelCase		(							UpperCAmelCase__       :					int = 1_0_0_0      )		->int:
						A__						:  Optional[int]		  =  2**power
						A__						:  Dict		  =  0
						while n:
												A__  ,  A__						:  Tuple		  =  r + n % 1_0, n // 1_0
						return r


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






