



import os

def 					lowerCAmelCase__  (							):
				"""simple docstring"""







				__a					       =						os.path.dirname(os.path.realpath(_SCREAMING_SNAKE_CASE      )      )
				__a					       =						os.path.join(_SCREAMING_SNAKE_CASE			,       """triangle.txt"""      )

				with open(_SCREAMING_SNAKE_CASE      ) as f:
								__a					       =						f.readlines()

				__a					       =						[[int(_SCREAMING_SNAKE_CASE      ) for y in x.rstrip("""\r\n"""      ).split(""" """      )] for x in triangle]

				for i in range(1			,       len(_SCREAMING_SNAKE_CASE      )      ):
								for j in range(len(a[i]      )      ):
												__a					       =						a[i - 1][j] if j != len(a[i - 1]      ) else 0
												__a					       =						a[i - 1][j - 1] if j > 0 else 0
												a[i][j] += max(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE      )
				return max(a[-1]      )


if __name__ == "__main__":
						print(solution())





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

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


lowerCamelCase__     =	logging.getLogger(__name__)






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



def 					lowerCAmelCase__  (							):
				"""simple docstring"""







				__a					       =						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       ,   __a       ,   __a					       =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
				else:
								__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 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"""			,       _SCREAMING_SNAKE_CASE      )

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

				__a					       =						AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path			,       cache_dir=model_args.cache_dir			,       )
				__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			,       use_fast=_SCREAMING_SNAKE_CASE			,       )
				__a					       =						AutoModelForQuestionAnswering.from_pretrained(
				    model_args.model_name_or_path			,       from_tf=bool(""".ckpt""" in model_args.model_name_or_path      )			,       config=_SCREAMING_SNAKE_CASE			,       cache_dir=model_args.cache_dir			,       )

				# Get datasets
				__a					       =						hasattr(model.config			,       """lang2id"""      )
				__a					       =						(
				    SquadDataset(
				        _SCREAMING_SNAKE_CASE			,       tokenizer=_SCREAMING_SNAKE_CASE			,       is_language_sensitive=_SCREAMING_SNAKE_CASE			,       cache_dir=model_args.cache_dir      )
				    if training_args.do_train
				    else None
				)
				__a					       =						(
				    SquadDataset(
				        _SCREAMING_SNAKE_CASE			,       tokenizer=_SCREAMING_SNAKE_CASE			,       mode="""dev"""			,       is_language_sensitive=_SCREAMING_SNAKE_CASE			,       cache_dir=model_args.cache_dir			,       )
				    if training_args.do_eval
				    else None
				)

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

				# Initialize our Trainer
				__a					       =						Trainer(
				    model=_SCREAMING_SNAKE_CASE			,       args=_SCREAMING_SNAKE_CASE			,       train_dataset=_SCREAMING_SNAKE_CASE			,       eval_dataset=_SCREAMING_SNAKE_CASE			,       data_collator=_SCREAMING_SNAKE_CASE			,       )

				# 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_master():
												tokenizer.save_pretrained(training_args.output_dir      )

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Optional[Any]      ):
				"""simple docstring"""







				main()


if __name__ == "__main__":
						main()
