

"""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


__UpperCamelCase						:   Optional[Any]							   =	logging.getLogger(__name__)







@dataclass
class        SCREAMING_SNAKE_CASE      :






					"""simple docstring"""



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







@dataclass
class        SCREAMING_SNAKE_CASE      :






					"""simple docstring"""



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

def 						__SCREAMING_SNAKE_CASE						(   A_				):
	lowerCAmelCase__			:		List[Any]						       =			default_data_collator(A_				)
	lowerCAmelCase__			:		Tuple						       =			batch.pop('''pairID'''		,  A_				)
	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.

	lowerCAmelCase__			:		Optional[int]						       =			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
	lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__			:		Optional[Any]						       =			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:
		lowerCAmelCase__			:		Tuple						       =			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.

	lowerCAmelCase__			:		Any						       =			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		,  )
	lowerCAmelCase__			:		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		,  )
	lowerCAmelCase__			:		List[str]						       =			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
	lowerCAmelCase__			:		List[Any]						       =			(
	    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
	)
	lowerCAmelCase__			:		Tuple						       =			(
	    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
	lowerCAmelCase__			:		Any						       =			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 ***'''				)

		lowerCAmelCase__			:		List[str]						       =			trainer.predict(A_				)
		lowerCAmelCase__			:		Tuple						       =			output.predictions
		lowerCAmelCase__			:		Tuple						       =			np.argmax(A_		,  axis=1				)

		lowerCAmelCase__			:		str						       =			[ex.pairID for ex in eval_dataset]
		lowerCAmelCase__			:		List[str]						       =			os.path.join(training_args.output_dir		,  '''hans_predictions.txt'''				)
		lowerCAmelCase__			:		Any						       =			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 						__SCREAMING_SNAKE_CASE						(   A_				):
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
							main()







"""simple docstring"""





from itertools import permutations

def 						__SCREAMING_SNAKE_CASE						(   A_ = 5				):
	if gon_side < 3 or gon_side > 5:
		raise ValueError('''gon_side must be in the range [3, 5]'''				)

	# Since it's 16, we know 10 is on the outer ring
	# Put the big numbers at the end so that they are never the first number
	lowerCAmelCase__			:		str						       =			list(range(gon_side + 1		,  0		,  -1				)				)
	lowerCAmelCase__			:		int						       =			list(range(gon_side + 2		,  gon_side * 2 + 1				)				)

	for perm in permutations(small_numbers + big_numbers				):
		lowerCAmelCase__			:		Optional[Any]						       =			generate_gon_ring(A_		,  list(A_				)				)
		if is_magic_gon(A_				):
			return int(''''''.join(str(A_				) for n in numbers				)				)

	lowerCAmelCase__			:		Tuple						       =			f'Magic {gon_side}-gon ring is impossible'
	raise ValueError(A_				)

def 						__SCREAMING_SNAKE_CASE						(   A_		,  A_				):
	lowerCAmelCase__			:		Dict						       =			[0] * (gon_side * 3)
	lowerCAmelCase__			:		Optional[int]						       =			perm[0:3]
	perm.append(perm[1]				)

	lowerCAmelCase__			:		int						       =			1 if gon_side < 5 else 2

	for i in range(1		,  len(A_				) // 3 + magic_number				):
		lowerCAmelCase__			:		List[str]						       =			perm[2 * i + 1]
		lowerCAmelCase__			:		Optional[int]						       =			result[3 * i - 1]
		lowerCAmelCase__			:		List[Any]						       =			perm[2 * i + 2]

	return result

def 						__SCREAMING_SNAKE_CASE						(   A_				):
	if len(A_				) % 3 != 0:
		raise ValueError('''a gon ring should have a length that is a multiple of 3'''				)

	if min(numbers[::3]				) != numbers[0]:
		return False

	lowerCAmelCase__			:		int						       =			sum(numbers[:3]				)

	return all(sum(numbers[i : i + 3]				) == total for i in range(3		,  len(A_				)		,  3				)				)


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




