




from __future__ import annotations







def lowerCamelCase__   (			_lowercase      ):

     '''simple docstring'''


     if len(__lowercase      ) == 0:
          raise ValueError('''find_min() arg is an empty sequence'''      )
     UpperCAmelCase_			:							Tuple   =			nums[0]
     for num in nums:
          UpperCAmelCase_			:							Optional[int]   =			min(__lowercase ,						__lowercase      )
     return min_num


if __name__ == "__main__":
     import doctest

     doctest.testmod(verbose=True)



"""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


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





@dataclass
class snake_case					:
		__UpperCamelCase							=	field(
		    metadata={'help': 'The name of the task to train selected in the list: ' + ', '.join(hans_processors.keys())})
		__UpperCamelCase							=	field(
		    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'})
		__UpperCamelCase							=	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.'
		        )
		    }   , )
		__UpperCamelCase							=	field(
		    default=_UpperCamelCase   , metadata={'help': 'Overwrite the cached training and evaluation sets'})






def    a__       ( __lowercase      )		->		Dict[str, torch.Tensor]:
				_A         =	default_data_collator(__lowercase      )
				_A         =	batch.pop("pairID"   ,	__lowercase      )
				return batch






def    a__       ( )		->		Optional[Any]:
				# 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         =	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"   ,	__lowercase      )

				# Set seed
				set_seed(training_args.seed      )

				try:
								_A         =	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         =	AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,	num_labels=__lowercase   ,	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         =	AutoModelForSequenceClassification.from_pretrained(
				    model_args.model_name_or_path   ,	from_tf=bool(".ckpt" in model_args.model_name_or_path      )   ,	config=__lowercase   ,	cache_dir=model_args.cache_dir   ,	)

				# Get datasets
				_A         =	(
				    HansDataset(
				        data_dir=data_args.data_dir   ,	tokenizer=__lowercase   ,	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         =	(
				    HansDataset(
				        data_dir=data_args.data_dir   ,	tokenizer=__lowercase   ,	task=data_args.task_name   ,	max_seq_length=data_args.max_seq_length   ,	overwrite_cache=data_args.overwrite_cache   ,	evaluate=__lowercase   ,	)
				    if training_args.do_eval
				    else None
				)

				# Initialize our Trainer
				_A         =	Trainer(
				    model=__lowercase   ,	args=__lowercase   ,	train_dataset=__lowercase   ,	eval_dataset=__lowercase   ,	data_collator=__lowercase   ,	)

				# 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         =	trainer.predict(__lowercase      )
								_A         =	output.predictions
								_A         =	np.argmax(__lowercase   ,	axis=1      )

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

								trainer._log(output.metrics      )






def    a__       ( __lowercase      )		->		Dict:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
		main()