






"""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							:						int   							=			logging.getLogger(__name__)







@dataclass
class 							_SCREAMING_SNAKE_CASE    :





							"""simple docstring"""





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







@dataclass
class 							_SCREAMING_SNAKE_CASE    :





							"""simple docstring"""





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





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_):
					a__ 							=	default_data_collator(lowerCamelCase_)
					a__ 							=	batch.pop('''pairID'''			,  lowerCamelCase_)
					return batch





def 							SCREAMING_SNAKE_CASE							(   ):
					# 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'''			,  lowerCamelCase_)

					# 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=lowerCamelCase_			,  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=lowerCamelCase_			,  cache_dir=model_args.cache_dir			,  )

					# Get datasets
					a__ 							=	(
					    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__ 							=	(
					    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__ 							=	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__ 							=	trainer.predict(lowerCamelCase_)
										a__ 							=	output.predictions
										a__ 							=	np.argmax(lowerCamelCase_			,  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(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 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_):
					# For xla_spawn (TPUs)
					main()


if __name__ == "__main__":
	main()










"""simple docstring"""







from __future__ import annotations





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_):

					# Base Case
					if counter == 0 or capacity == 0:
										return 0

					# If weight of the nth item is more than Knapsack of capacity,
					#   then this item cannot be included in the optimal solution,
					# else return the maximum of two cases:
					#   (1) nth item included
					#   (2) not included
					if weights[counter - 1] > capacity:
										return knapsack(lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  counter - 1)
					else:
										a__ 							=	capacity - weights[counter - 1]
										a__ 							=	values[counter - 1] + knapsack(
										    lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  counter - 1)
										a__ 							=	knapsack(lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  counter - 1)
										return max(lowerCamelCase_			,  lowerCamelCase_)


if __name__ == "__main__":
	import doctest

	doctest.testmod()


