






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


UpperCAmelCase__            =	logging.getLogger(__name__)






@dataclass
class 	lowercase_      :






						'''simple docstring'''




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






@dataclass
class 	lowercase_      :






						'''simple docstring'''




						__snake_case  						=						field(
						    metadata={'''help''': '''The name of the task to train selected in the list: ''' + ''', '''.join(hans_processors.keys()     )}     )
						__snake_case  						=						field(
						    metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}     )
						__snake_case  						=						field(
						    default=1_28				,						metadata={
						        '''help''': (
						            '''The maximum total input sequence length after tokenization. Sequences longer '''
						            '''than this will be truncated, sequences shorter will be padded.'''
						        )
						    }				,						)
						__snake_case  						=						field(
						    default=lowercase				,						metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}     )
def 		_a					(   a				:List[InputFeatures]				)							->    Dict[str, torch.Tensor]:
	a							=   default_data_collator(a				)
	a							=   batch.pop('''pairID'''			,							a				)
	return batch
def 		_a					(   )							->    int:
	# 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'''			,							a				)

	# 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=a			,							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=a			,							cache_dir=model_args.cache_dir			,							)

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

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

	# 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(a				)
		a							=   output.predictions
		a							=   np.argmax(a			,							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(a			,							'''w'''				) as writer:
				writer.write('''pairID,gold_label\n'''				)
				for pid, pred in zip(a			,							a				):
					writer.write('''ex''' + str(a				) + ''',''' + label_list[int(a				)] + '''\n'''				)

		trainer._log(output.metrics				)
def 		_a					(   a				:str				)							->    Optional[Any]:
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
					main()










def 		_a					(   a				:float				)							->    float:
 return -num if num < 0 else num
def 		_a					(   a				:list[int]				)							->    int:
 if len(a				) == 0:
  raise ValueError('''abs_min() arg is an empty sequence'''				)
 a							=   x[0]
 for i in x:
  if abs_val(a				) < abs_val(a				):
   a							=   i
 return j
def 		_a					(   a				:list[int]				)							->    int:
 if len(a				) == 0:
  raise ValueError('''abs_max() arg is an empty sequence'''				)
 a							=   x[0]
 for i in x:
  if abs(a				) > abs(a				):
   a							=   i
 return j
def 		_a					(   a				:list[int]				)							->    int:
 if len(a				) == 0:
  raise ValueError('''abs_max_sort() arg is an empty sequence'''				)
 return sorted(a			,							key=a				)[-1]
def 		_a					(   )							->    int:
 assert abs_val(0				) == 0
 assert abs_val(34				) == 34
 assert abs_val(-100_000_000_000				) == 100_000_000_000

 a							=   [-3, -1, 2, -11]
 assert abs_max(a				) == -11
 assert abs_max_sort(a				) == -11
 assert abs_min(a				) == -1


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     test_abs_val()
     print(abs_val(-34))  # --> 34


