



"""simple docstring"""
def 			lowercase	(_snake_case = 10**9			)       ->    str:




  '''simple docstring'''







  __UpperCamelCase  						=							1
  __UpperCamelCase  						=							2

  __UpperCamelCase  						=							0
  __UpperCamelCase  						=							0
  __UpperCamelCase  						=							0
  while perimeter <= max_perimeter:
    perimeters_sum += perimeter

    prev_value += 2 * value
    value += prev_value

    __UpperCamelCase  						=							2 * value + 2 if i % 2 == 0 else 2 * value - 2
    i += 1

  return perimeters_sum


if __name__ == "__main__":
 print(f"""{solution() = }""")





'''simple docstring'''


import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()
def 						snake_case_       (UpperCamelCase			:    str					,		UpperCamelCase			:    str					,		UpperCamelCase			:    str					,		UpperCamelCase			:    PreTrainedTokenizer					,		UpperCamelCase			:    int					,		UpperCamelCase			:    Optional[int] = None					,		):





							'''simple docstring'''


							_a      	=							{}

							if train_file is not None:
														_a      	=							[train_file]
							if eval_file is not None:
														_a      	=							[eval_file]
							if test_file is not None:
														_a      	=							[test_file]

							_a      	=							datasets.load_dataset('''csv'''					,		data_files=UpperCamelCase )
							_a      	=							list(ds[list(files.keys() )[0]].features.keys() )
							_a      	=							features_name.pop(UpperCamelCase )
							_a      	=							list(set(ds[list(files.keys() )[0]][label_name] ) )
							_a      	=							{label: i for i, label in enumerate(UpperCamelCase )}
							_a      	=							tokenizer.model_input_names
							_a      	=							{}

							if len(UpperCamelCase ) == 1:
														for k in files.keys():
																					_a      	=							ds[k].map(
																					    lambda UpperCamelCase : tokenizer.batch_encode_plus(
																					        example[features_name[0]]					,		truncation=UpperCamelCase					,		max_length=UpperCamelCase					,		padding='''max_length''' )					,		batched=UpperCamelCase					,		)
							elif len(UpperCamelCase ) == 2:
														for k in files.keys():
																					_a      	=							ds[k].map(
																					    lambda UpperCamelCase : tokenizer.batch_encode_plus(
																					        (example[features_name[0]], example[features_name[1]])					,		truncation=UpperCamelCase					,		max_length=UpperCamelCase					,		padding='''max_length'''					,		)					,		batched=UpperCamelCase					,		)

							def gen_train():
														for ex in transformed_ds[datasets.Split.TRAIN]:
																					_a      	=							{k: v for k, v in ex.items() if k in input_names}
																					_a      	=							labelaid[ex[label_name]]
																					yield (d, label)

							def gen_val():
														for ex in transformed_ds[datasets.Split.VALIDATION]:
																					_a      	=							{k: v for k, v in ex.items() if k in input_names}
																					_a      	=							labelaid[ex[label_name]]
																					yield (d, label)

							def gen_test():
														for ex in transformed_ds[datasets.Split.TEST]:
																					_a      	=							{k: v for k, v in ex.items() if k in input_names}
																					_a      	=							labelaid[ex[label_name]]
																					yield (d, label)

							_a      	=							(
							    tf.data.Dataset.from_generator(
							        UpperCamelCase					,		({k: tf.intaa for k in input_names}, tf.intaa)					,		({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] ))					,		)
							    if datasets.Split.TRAIN in transformed_ds
							    else None
							)

							if train_ds is not None:
														_a      	=							train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN] ) ) )

							_a      	=							(
							    tf.data.Dataset.from_generator(
							        UpperCamelCase					,		({k: tf.intaa for k in input_names}, tf.intaa)					,		({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] ))					,		)
							    if datasets.Split.VALIDATION in transformed_ds
							    else None
							)

							if val_ds is not None:
														_a      	=							val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION] ) ) )

							_a      	=							(
							    tf.data.Dataset.from_generator(
							        UpperCamelCase					,		({k: tf.intaa for k in input_names}, tf.intaa)					,		({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] ))					,		)
							    if datasets.Split.TEST in transformed_ds
							    else None
							)

							if test_ds is not None:
														_a      	=							test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST] ) ) )

							return train_ds, val_ds, test_ds, labelaid


_snake_case : str  				=						logging.getLogger(__name__)

@dataclass
class      A       :
							lowercase_							     =							field(metadata={'help': 'Which column contains the label'}       )
							lowercase_							     =							field(default=_a	,metadata={'help': 'The path of the training file'}       )
							lowercase_							     =							field(default=_a	,metadata={'help': 'The path of the development file'}       )
							lowercase_							     =							field(default=_a	,metadata={'help': 'The path of the test file'}       )
							lowercase_							     =							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.'
							        )
							    }	,)
							lowercase_							     =							field(
							    default=_a	,metadata={'help': 'Overwrite the cached training and evaluation sets'}       )



@dataclass
class      A       :
							lowercase_							     =							field(
							    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}       )
							lowercase_							     =							field(
							    default=_a	,metadata={'help': 'Pretrained config name or path if not the same as model_name'}       )
							lowercase_							     =							field(
							    default=_a	,metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}       )
							lowercase_							     =							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.
							lowercase_							     =							field(
							    default=_a	,metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}	,)



def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
							_a	,				_a	,				_a      	=							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.''' )

							# Setup logging
							logging.basicConfig(
							    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''					,		datefmt='''%m/%d/%Y %H:%M:%S'''					,		level=logging.INFO					,		)
							logger.info(
							    f'n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1 )}, '
							    f'16-bits training: {training_args.fpaa}' )
							logger.info(f'Training/evaluation parameters {training_args}' )

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

							_a      	=							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					,		)

							_a	,				_a	,				_a	,				_a      	=							get_tfds(
							    train_file=data_args.train_file					,		eval_file=data_args.dev_file					,		test_file=data_args.test_file					,		tokenizer=UpperCamelCase					,		label_column_id=data_args.label_column_id					,		max_seq_length=data_args.max_seq_length					,		)

							_a      	=							AutoConfig.from_pretrained(
							    model_args.config_name if model_args.config_name else model_args.model_name_or_path					,		num_labels=len(UpperCamelCase )					,		labelaid=UpperCamelCase					,		idalabel={id: label for label, id in labelaid.items()}					,		finetuning_task='''text-classification'''					,		cache_dir=model_args.cache_dir					,		)

							with training_args.strategy.scope():
														_a      	=							TFAutoModelForSequenceClassification.from_pretrained(
														    model_args.model_name_or_path					,		from_pt=bool('''.bin''' in model_args.model_name_or_path )					,		config=UpperCamelCase					,		cache_dir=model_args.cache_dir					,		)

							def compute_metrics(UpperCamelCase			:    EvalPrediction ) -> Dict:
														_a      	=							np.argmax(p.predictions					,		axis=1 )

														return {"acc": (preds == p.label_ids).mean()}

							# Initialize our Trainer
							_a      	=							TFTrainer(
							    model=UpperCamelCase					,		args=UpperCamelCase					,		train_dataset=UpperCamelCase					,		eval_dataset=UpperCamelCase					,		compute_metrics=UpperCamelCase					,		)

							# Training
							if training_args.do_train:
														trainer.train()
														trainer.save_model()
														tokenizer.save_pretrained(training_args.output_dir )

							# Evaluation
							_a      	=							{}
							if training_args.do_eval:
														logger.info('''*** Evaluate ***''' )
														_a      	=							trainer.evaluate()
														_a      	=							os.path.join(training_args.output_dir					,		'''eval_results.txt''' )

														with open(UpperCamelCase					,		'''w''' ) as writer:
																					logger.info('''***** Eval results *****''' )

																					for key, value in result.items():
																												logger.info(f'  {key} = {value}' )
																												writer.write(f'{key} = {value}\n' )

																					results.update(UpperCamelCase )

							return results


if __name__ == "__main__":
				main()

