




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      : Tuple					      =   logging.getLogger(__name__)

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







@dataclass
class 	__lowerCAmelCase			:
							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=__magic_name__   , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}				)







def 	__A	(			__lowerCamelCase					)   -> Dict[str, torch.Tensor]:
	a		       =							default_data_collator(__lowerCamelCase					)
	a		       =							batch.pop("""pairID"""							,    __lowerCamelCase					)
	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"""							,    __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 	__A	(			__lowerCamelCase					)   -> Tuple:
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
		main()







from itertools import combinations, product





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> bool:
	if xa == ya == 0 or xa == ya == 0:
		return False
	a		       =							xa * xa + ya * ya
	a		       =							xa * xa + ya * ya
	a		       =							(xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
	return (
	    a_square + b_square == c_square
	    or a_square + c_square == b_square
	    or b_square + c_square == a_square
	)





def 	__A	(			__lowerCamelCase = 50					)   -> int:
	return sum(
	    1
	    for pta, pta in combinations(product(range(limit + 1					)							,    repeat=2					)							,    2					)
	    if is_right(*__lowerCamelCase							,    *__lowerCamelCase					)					)


if __name__ == "__main__":
		print(F'{solution() = }')

