import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")

snake_case      =    logging.getLogger(__name__)



@dataclass
class __A     :

							'''simple docstring'''

							a_     =					field(
							    default=1_28		,metadata={
							        '''help''': (
							            '''The maximum total input sequence length after tokenization. Sequences longer '''
							            '''than this will be truncated, sequences shorter will be padded.'''
							        )
							    }		,)
							a_     =					field(
							    default=snake_case__		,metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}						)
							a_     =					field(
							    default=snake_case__		,metadata={
							        '''help''': (
							            '''Whether to pad all samples to `max_seq_length`. '''
							            '''If False, will pad the samples dynamically when batching to the maximum length in the batch.'''
							        )
							    }		,)
							a_     =					field(
							    default=snake_case__		,metadata={
							        '''help''': (
							            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
							            '''value if set.'''
							        )
							    }		,)
							a_     =					field(
							    default=snake_case__		,metadata={
							        '''help''': (
							            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
							            '''value if set.'''
							        )
							    }		,)
							a_     =					field(
							    default=snake_case__		,metadata={
							        '''help''': (
							            '''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
							            '''value if set.'''
							        )
							    }		,)



@dataclass
class __A     :

							'''simple docstring'''

							a_     =					field(
							    default=snake_case__		,metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}						)
							a_     =					field(
							    default=snake_case__		,metadata={'''help''': '''Evaluation language. Also train language if `train_language` is set to None.'''}						)
							a_     =					field(
							    default=snake_case__		,metadata={'''help''': '''Train language if it is different from the evaluation language.'''}						)
							a_     =					field(
							    default=snake_case__		,metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}						)
							a_     =					field(
							    default=snake_case__		,metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}						)
							a_     =					field(
							    default=snake_case__		,metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}		,)
							a_     =					field(
							    default=snake_case__		,metadata={'''help''': '''arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'''}		,)
							a_     =					field(
							    default=snake_case__		,metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''}		,)
							a_     =					field(
							    default='''main'''		,metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}		,)
							a_     =					field(
							    default=snake_case__		,metadata={
							        '''help''': (
							            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
							            '''with private models).'''
							        )
							    }		,)
							a_     =					field(
							    default=snake_case__		,metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''}		,)






def        UpperCamelCase_							(			):



					"""simple docstring"""

					_lowerCAmelCase			:		str          = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
					_lowerCAmelCase			:		Optional[Any]          = parser.parse_args_into_dataclasses()

					# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
					# information sent is the one passed as arguments along with your Python/PyTorch versions.
					send_example_telemetry("run_xnli"						,	UpperCAmelCase_  )

					# Setup logging
					logging.basicConfig(
					    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"						,	datefmt="%m/%d/%Y %H:%M:%S"						,	handlers=[logging.StreamHandler(sys.stdout  )]						,	)

					if training_args.should_log:
										# The default of training_args.log_level is passive, so we set log level at info here to have that default.
										transformers.utils.logging.set_verbosity_info()

					_lowerCAmelCase			:		Tuple          = training_args.get_process_log_level()
					logger.setLevel(UpperCAmelCase_  )
					datasets.utils.logging.set_verbosity(UpperCAmelCase_  )
					transformers.utils.logging.set_verbosity(UpperCAmelCase_  )
					transformers.utils.logging.enable_default_handler()
					transformers.utils.logging.enable_explicit_format()

					# Log on each process the small summary:
					logger.warning(
					    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
					    + f"""distributed training: {bool(training_args.local_rank != -1  )}, 16-bits training: {training_args.fpaa}"""  )
					logger.info(f"""Training/evaluation parameters {training_args}"""  )

					# Detecting last checkpoint.
					_lowerCAmelCase			:		Any          = None
					if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
										_lowerCAmelCase			:		List[Any]          = get_last_checkpoint(training_args.output_dir  )
										if last_checkpoint is None and len(os.listdir(training_args.output_dir  )  ) > 0:
															raise ValueError(
															    f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
															    "Use --overwrite_output_dir to overcome."  )
										elif last_checkpoint is not None:
															logger.info(
															    f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
															    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."  )

    # Set seed before initializing model.
					set_seed(training_args.seed  )

					# In distributed training, the load_dataset function guarantees that only one local process can concurrently
					# download the dataset.
					# Downloading and loading xnli dataset from the hub.
					if training_args.do_train:
										if model_args.train_language is None:
															_lowerCAmelCase			:		Any          = load_dataset(
															    "xnli"						,	model_args.language						,	split="train"						,	cache_dir=model_args.cache_dir						,	use_auth_token=True if model_args.use_auth_token else None						,	)
										else:
															_lowerCAmelCase			:		Union[str, Any]          = load_dataset(
															    "xnli"						,	model_args.train_language						,	split="train"						,	cache_dir=model_args.cache_dir						,	use_auth_token=True if model_args.use_auth_token else None						,	)
										_lowerCAmelCase			:		str          = train_dataset.features['''label'''].names

					if training_args.do_eval:
										_lowerCAmelCase			:		List[Any]          = load_dataset(
										    "xnli"						,	model_args.language						,	split="validation"						,	cache_dir=model_args.cache_dir						,	use_auth_token=True if model_args.use_auth_token else None						,	)
										_lowerCAmelCase			:		Any          = eval_dataset.features['''label'''].names

					if training_args.do_predict:
										_lowerCAmelCase			:		List[Any]          = load_dataset(
										    "xnli"						,	model_args.language						,	split="test"						,	cache_dir=model_args.cache_dir						,	use_auth_token=True if model_args.use_auth_token else None						,	)
										_lowerCAmelCase			:		Tuple          = predict_dataset.features['''label'''].names

					# Labels
					_lowerCAmelCase			:		List[str]          = len(UpperCAmelCase_  )

					# Load pretrained model and tokenizer
					# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.
					_lowerCAmelCase			:		int          = AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path						,	num_labels=UpperCAmelCase_						,	idalabel={str(UpperCAmelCase_  ): label for i, label in enumerate(UpperCAmelCase_  )}						,	labelaid={label: i for i, label in enumerate(UpperCAmelCase_  )}						,	finetuning_task="xnli"						,	cache_dir=model_args.cache_dir						,	revision=model_args.model_revision						,	use_auth_token=True if model_args.use_auth_token else None						,	)
					_lowerCAmelCase			:		Any          = AutoTokenizer.from_pretrained(
					    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path						,	do_lower_case=model_args.do_lower_case						,	cache_dir=model_args.cache_dir						,	use_fast=model_args.use_fast_tokenizer						,	revision=model_args.model_revision						,	use_auth_token=True if model_args.use_auth_token else None						,	)
					_lowerCAmelCase			:		Tuple          = AutoModelForSequenceClassification.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						,	revision=model_args.model_revision						,	use_auth_token=True if model_args.use_auth_token else None						,	ignore_mismatched_sizes=model_args.ignore_mismatched_sizes						,	)

					# Preprocessing the datasets
					# Padding strategy
					if data_args.pad_to_max_length:
										_lowerCAmelCase			:		str          = '''max_length'''
					else:
										# We will pad later, dynamically at batch creation, to the max sequence length in each batch
										_lowerCAmelCase			:		List[str]          = False

					def preprocess_function(lowerCAmelCase__  ):
										# Tokenize the texts
										return tokenizer(
										    examples["premise"]						,	examples["hypothesis"]						,	padding=UpperCAmelCase_						,	max_length=data_args.max_seq_length						,	truncation=UpperCAmelCase_						,	)

					if training_args.do_train:
										if data_args.max_train_samples is not None:
															_lowerCAmelCase			:		List[Any]          = min(len(UpperCAmelCase_  )						,	data_args.max_train_samples  )
															_lowerCAmelCase			:		Tuple          = train_dataset.select(range(UpperCAmelCase_  )  )
										with training_args.main_process_first(desc="train dataset map pre-processing"  ):
															_lowerCAmelCase			:		Dict          = train_dataset.map(
															    UpperCAmelCase_						,	batched=UpperCAmelCase_						,	load_from_cache_file=not data_args.overwrite_cache						,	desc="Running tokenizer on train dataset"						,	)
										# Log a few random samples from the training set:
										for index in random.sample(range(len(UpperCAmelCase_  )  )						,	3  ):
															logger.info(f"""Sample {index} of the training set: {train_dataset[index]}."""  )

					if training_args.do_eval:
										if data_args.max_eval_samples is not None:
															_lowerCAmelCase			:		List[Any]          = min(len(UpperCAmelCase_  )						,	data_args.max_eval_samples  )
															_lowerCAmelCase			:		str          = eval_dataset.select(range(UpperCAmelCase_  )  )
										with training_args.main_process_first(desc="validation dataset map pre-processing"  ):
															_lowerCAmelCase			:		Optional[Any]          = eval_dataset.map(
															    UpperCAmelCase_						,	batched=UpperCAmelCase_						,	load_from_cache_file=not data_args.overwrite_cache						,	desc="Running tokenizer on validation dataset"						,	)

					if training_args.do_predict:
										if data_args.max_predict_samples is not None:
															_lowerCAmelCase			:		List[Any]          = min(len(UpperCAmelCase_  )						,	data_args.max_predict_samples  )
															_lowerCAmelCase			:		Any          = predict_dataset.select(range(UpperCAmelCase_  )  )
										with training_args.main_process_first(desc="prediction dataset map pre-processing"  ):
															_lowerCAmelCase			:		Tuple          = predict_dataset.map(
															    UpperCAmelCase_						,	batched=UpperCAmelCase_						,	load_from_cache_file=not data_args.overwrite_cache						,	desc="Running tokenizer on prediction dataset"						,	)

    # Get the metric function
					_lowerCAmelCase			:		Optional[int]          = evaluate.load("xnli"  )

					# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
					# predictions and label_ids field) and has to return a dictionary string to float.
					def compute_metrics(lowerCAmelCase__  ):
										_lowerCAmelCase			:		Dict          = p.predictions[0] if isinstance(p.predictions						,	UpperCAmelCase_  ) else p.predictions
										_lowerCAmelCase			:		Any          = np.argmax(UpperCAmelCase_						,	axis=1  )
										return metric.compute(predictions=UpperCAmelCase_						,	references=p.label_ids  )

					# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
					if data_args.pad_to_max_length:
										_lowerCAmelCase			:		Any          = default_data_collator
					elif training_args.fpaa:
										_lowerCAmelCase			:		str          = DataCollatorWithPadding(UpperCAmelCase_						,	pad_to_multiple_of=8  )
					else:
										_lowerCAmelCase			:		Tuple          = None

					# Initialize our Trainer
					_lowerCAmelCase			:		Any          = Trainer(
					    model=UpperCAmelCase_						,	args=UpperCAmelCase_						,	train_dataset=train_dataset if training_args.do_train else None						,	eval_dataset=eval_dataset if training_args.do_eval else None						,	compute_metrics=UpperCAmelCase_						,	tokenizer=UpperCAmelCase_						,	data_collator=UpperCAmelCase_						,	)

					# Training
					if training_args.do_train:
										_lowerCAmelCase			:		Dict          = None
										if training_args.resume_from_checkpoint is not None:
															_lowerCAmelCase			:		Dict          = training_args.resume_from_checkpoint
										elif last_checkpoint is not None:
															_lowerCAmelCase			:		Dict          = last_checkpoint
										_lowerCAmelCase			:		List[Any]          = trainer.train(resume_from_checkpoint=UpperCAmelCase_  )
										_lowerCAmelCase			:		Any          = train_result.metrics
										_lowerCAmelCase			:		int          = (
										    data_args.max_train_samples if data_args.max_train_samples is not None else len(UpperCAmelCase_  )
										)
										_lowerCAmelCase			:		List[Any]          = min(UpperCAmelCase_						,	len(UpperCAmelCase_  )  )

										trainer.save_model()  # Saves the tokenizer too for easy upload

										trainer.log_metrics("train"						,	UpperCAmelCase_  )
										trainer.save_metrics("train"						,	UpperCAmelCase_  )
										trainer.save_state()

					# Evaluation
					if training_args.do_eval:
										logger.info("*** Evaluate ***"  )
										_lowerCAmelCase			:		Dict          = trainer.evaluate(eval_dataset=UpperCAmelCase_  )

										_lowerCAmelCase			:		Optional[Any]          = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(UpperCAmelCase_  )
										_lowerCAmelCase			:		int          = min(UpperCAmelCase_						,	len(UpperCAmelCase_  )  )

										trainer.log_metrics("eval"						,	UpperCAmelCase_  )
										trainer.save_metrics("eval"						,	UpperCAmelCase_  )

					# Prediction
					if training_args.do_predict:
										logger.info("*** Predict ***"  )
										_lowerCAmelCase			:		Tuple          = trainer.predict(UpperCAmelCase_						,	metric_key_prefix="predict"  )

										_lowerCAmelCase			:		List[Any]          = (
										    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(UpperCAmelCase_  )
										)
										_lowerCAmelCase			:		Dict          = min(UpperCAmelCase_						,	len(UpperCAmelCase_  )  )

										trainer.log_metrics("predict"						,	UpperCAmelCase_  )
										trainer.save_metrics("predict"						,	UpperCAmelCase_  )

										_lowerCAmelCase			:		List[Any]          = np.argmax(UpperCAmelCase_						,	axis=1  )
										_lowerCAmelCase			:		Dict          = os.path.join(training_args.output_dir						,	"predictions.txt"  )
										if trainer.is_world_process_zero():
															with open(UpperCAmelCase_						,	"w"  ) as writer:
																				writer.write("index\tprediction\n"  )
																				for index, item in enumerate(UpperCAmelCase_  ):
																									_lowerCAmelCase			:		int          = label_list[item]
																									writer.write(f"""{index}\t{item}\n"""  )


if __name__ == "__main__":
						main()








def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int = 1_0_0_0				)			->						int:
       SCREAMING_SNAKE_CASE_							:				int		=2**power
       SCREAMING_SNAKE_CASE_							:				Tuple		=0
       while n:
              SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=r + n % 1_0, n // 1_0
       return r


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