



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

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowerCamelCase__			= logging.getLogger(__name__)

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Any				,     _SCREAMING_SNAKE_CASE       :			List[str]    ):
				"""simple docstring"""







				return (preds == labels).mean()






@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	:	Optional[str]    =field(
							    default=lowerCamelCase__			,      metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}			,      )






@dataclass
class    SCREAMING_SNAKE_CASE			:
							__lowerCamelCase	:	str    =field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys()					)}					)
							__lowerCamelCase	:	str    =field(metadata={'help': 'Should contain the data files for the task.'}					)
							__lowerCamelCase	:	int    =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.'
							        )
							    }			,      )
							__lowerCamelCase	:	bool    =field(
							    default=lowerCamelCase__			,      metadata={'help': 'Overwrite the cached training and evaluation sets'}					)



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







				__a       =		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
				__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    )

				# Set seed
				set_seed(training_args.seed    )

				try:
								__a       =		processors[data_args.task_name]()
								__a       =		processor.get_labels()
								__a       =		len(_SCREAMING_SNAKE_CASE    )
				except KeyError:
								raise ValueError("""Task not found: %s""" % (data_args.task_name)    )

				# 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				,     num_labels=_SCREAMING_SNAKE_CASE				,     finetuning_task=data_args.task_name				,     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				,     )
				__a       =		AutoModelForMultipleChoice.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       =		(
				    MultipleChoiceDataset(
				        data_dir=data_args.data_dir				,     tokenizer=_SCREAMING_SNAKE_CASE				,     task=data_args.task_name				,     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       =		(
				    MultipleChoiceDataset(
				        data_dir=data_args.data_dir				,     tokenizer=_SCREAMING_SNAKE_CASE				,     task=data_args.task_name				,     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 compute_metrics(_SCREAMING_SNAKE_CASE       :			EvalPrediction    ) -> Dict:
								__a       =		np.argmax(p.predictions				,     axis=1    )
								return {"acc": simple_accuracy(_SCREAMING_SNAKE_CASE				,     p.label_ids    )}

				# 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				,     compute_metrics=_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    )

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

								__a       =		trainer.evaluate()

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

																results.update(_SCREAMING_SNAKE_CASE    )

				return results

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







				main()


if __name__ == "__main__":
						main()





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       =		[]
				for line in triangle:
								__a       =		[]
								for number in line.strip().split(""" """    ):
												numbers_from_line.append(int(_SCREAMING_SNAKE_CASE    )    )
								a.append(_SCREAMING_SNAKE_CASE    )

				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())
