




'''simple docstring'''




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

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


snake_case__ 							=    logging.getLogger(__name__)






@dataclass
class      UpperCamelCase_  :







						"""simple docstring"""


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






@dataclass
class      UpperCamelCase_  :







						"""simple docstring"""


						_lowerCAmelCase         =       field(
						    metadata={'help': 'The name of the task to train selected in the list: ' + ', '.join(hans_processors.keys()  )}  )
						_lowerCAmelCase         =       field(
						    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}  )
						_lowerCAmelCase         =       field(
						    default=1_2_8,   metadata={
						        'help': (
						            'The maximum total input sequence length after tokenization. Sequences longer '
						            'than this will be truncated, sequences shorter will be padded.'
						        )
						    },   )
						_lowerCAmelCase         =       field(
						    default=a__,   metadata={'help': 'Overwrite the cached training and evaluation sets'}  )






def     snake_case__     (			lowerCamelCase__					: List[InputFeatures]      ) -> Dict[str, torch.Tensor]:
					A_			:  Any			=				default_data_collator(lowerCamelCase__      )
					A_			:  List[Any]			=				batch.pop('''pairID'''      ,		lowerCamelCase__      )
					return batch




def     snake_case__     (			) -> List[str]:
					# See all possible arguments in src/transformers/training_args.py
					# or by passing the --help flag to this script.
					# We now keep distinct sets of args, for a cleaner separation of concerns.

					A_			:  List[Any]			=				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
					A_			,A_			,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.'''      )

					# 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'''      ,		lowerCamelCase__      )

					# Set seed
					set_seed(training_args.seed      )

					try:
										A_			:  List[str]			=				hans_tasks_num_labels[data_args.task_name]
					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_			:  Optional[Any]			=				AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,		num_labels=lowerCamelCase__      ,		finetuning_task=data_args.task_name      ,		cache_dir=model_args.cache_dir      ,		)
					A_			:  Optional[Any]			=				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_			:  Optional[Any]			=				AutoModelForSequenceClassification.from_pretrained(
					    model_args.model_name_or_path      ,		from_tf=bool('''.ckpt''' in model_args.model_name_or_path      )      ,		config=lowerCamelCase__      ,		cache_dir=model_args.cache_dir      ,		)

					# Get datasets
					A_			:  Any			=				(
					    HansDataset(
					        data_dir=data_args.data_dir      ,		tokenizer=lowerCamelCase__      ,		task=data_args.task_name      ,		max_seq_length=data_args.max_seq_length      ,		overwrite_cache=data_args.overwrite_cache      ,		)
					    if training_args.do_train
					    else None
					)
					A_			:  int			=				(
					    HansDataset(
					        data_dir=data_args.data_dir      ,		tokenizer=lowerCamelCase__      ,		task=data_args.task_name      ,		max_seq_length=data_args.max_seq_length      ,		overwrite_cache=data_args.overwrite_cache      ,		evaluate=lowerCamelCase__      ,		)
					    if training_args.do_eval
					    else None
					)

					# Initialize our Trainer
					A_			:  Dict			=				Trainer(
					    model=lowerCamelCase__      ,		args=lowerCamelCase__      ,		train_dataset=lowerCamelCase__      ,		eval_dataset=lowerCamelCase__      ,		data_collator=lowerCamelCase__      ,		)

					# 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
					if training_args.do_eval:
										logger.info('''*** Evaluate ***'''      )

										A_			:  Any			=				trainer.predict(lowerCamelCase__      )
										A_			:  int			=				output.predictions
										A_			:  Optional[int]			=				np.argmax(lowerCamelCase__      ,		axis=1      )

										A_			:  Tuple			=				[ex.pairID for ex in eval_dataset]
										A_			:  int			=				os.path.join(training_args.output_dir      ,		'''hans_predictions.txt'''      )
										A_			:  int			=				eval_dataset.get_labels()
										if trainer.is_world_master():
															with open(lowerCamelCase__      ,		'''w'''      ) as writer:
																				writer.write('''pairID,gold_label\n'''      )
																				for pid, pred in zip(lowerCamelCase__      ,		lowerCamelCase__      ):
																									writer.write('''ex''' + str(lowerCamelCase__      ) + ''',''' + label_list[int(lowerCamelCase__      )] + '''\n'''      )

										trainer._log(output.metrics      )




def     snake_case__     (			lowerCamelCase__					: List[Any]      ) -> Tuple:
					# For xla_spawn (TPUs)
					main()


if __name__ == "__main__":
							main()









'''simple docstring'''




import os




def     snake_case__     (			) -> int:
					A_			:  List[str]			=				os.path.dirname(os.path.realpath(lowerCamelCase__      )      )
					A_			:  str			=				os.path.join(lowerCamelCase__      ,		'''triangle.txt'''      )

					with open(lowerCamelCase__      ) as in_file:
										A_			:  List[str]			=				[[int(lowerCamelCase__      ) for i in line.split()] for line in in_file]

					while len(lowerCamelCase__      ) != 1:
										A_			:  List[str]			=				triangle.pop()
										A_			:  Optional[Any]			=				triangle[-1]
										for j in range(len(lowerCamelCase__      ) - 1      ):
															curr_row[j] += max(last_row[j]      ,		last_row[j + 1]      )
					return triangle[0][0]


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



