






from __future__ import annotations


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  list[tuple[int, int]]:





						"""simple docstring"""



						_UpperCAmelCase					, _UpperCAmelCase									=			position
						_UpperCAmelCase									=			[
						    (y + 1, x + 2),
						    (y - 1, x + 2),
						    (y + 1, x - 2),
						    (y - 1, x - 2),
						    (y + 2, x + 1),
						    (y + 2, x - 1),
						    (y - 2, x + 1),
						    (y - 2, x - 1),
						]
						_UpperCAmelCase									=			[]

						for position in positions:
												_UpperCAmelCase					, _UpperCAmelCase									=			position
												if 0 <= y_test < n and 0 <= x_test < n:
																		permissible_positions.append(__lowerCAmelCase	)

						return permissible_positions


def __A    (      __lowerCAmelCase	)->  bool:





						"""simple docstring"""



						return not any(elem == 0 for row in board for elem in row	)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  bool:





						"""simple docstring"""



						if is_complete(__lowerCAmelCase	):
												return True

						for position in get_valid_pos(__lowerCAmelCase ,		len(__lowerCAmelCase	)	):
												_UpperCAmelCase					, _UpperCAmelCase									=			position

												if board[y][x] == 0:
																		_UpperCAmelCase									=			curr + 1
																		if open_knight_tour_helper(__lowerCAmelCase ,		__lowerCAmelCase ,		curr + 1	):
																								return True
																		_UpperCAmelCase									=			0

						return False


def __A    (      __lowerCAmelCase	)->  list[list[int]]:





						"""simple docstring"""



						_UpperCAmelCase									=			[[0 for i in range(__lowerCAmelCase	)] for j in range(__lowerCAmelCase	)]

						for i in range(__lowerCAmelCase	):
												for j in range(__lowerCAmelCase	):
																		_UpperCAmelCase									=			1
																		if open_knight_tour_helper(__lowerCAmelCase ,		(i, j) ,		1	):
																								return board
																		_UpperCAmelCase									=			0

						_UpperCAmelCase									=			F"""Open Kight Tour cannot be performed on a board of size {n}"""
						raise ValueError(__lowerCAmelCase	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




"""simple docstring"""





import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


snake_case__						:   Dict			 = logging.getLogger(__name__)


def    _snake_case  (  _snake_case :	Any       ,      _snake_case :	Any	):
      return (preds == labels).mean()



@dataclass
class 						snake_case_:
     __UpperCamelCase     =    field(
         metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}	)
     __UpperCamelCase     =    field(
         default=a__							,    metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}	)
     __UpperCamelCase     =    field(
         default=a__							,    metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}	)
     __UpperCamelCase     =    field(
         default=a__							,    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 on: ''' + ''', '''.join(processors.keys()	)}	)
     __UpperCamelCase     =    field(metadata={'''help''': '''Should contain the 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=a__							,    metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}	)



def    _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    :		str				=      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
      lowerCAmelCase,					lowerCAmelCase,					lowerCAmelCase    :		Optional[int]				=      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'''       ,      _snake_case	)

      # Set seed
      set_seed(training_args.seed	)

      try:
            lowerCAmelCase    :		Tuple				=      processors[data_args.task_name]()
            lowerCAmelCase    :		Any				=      processor.get_labels()
            lowerCAmelCase    :		Union[str, Any]				=      len(_snake_case	)
      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    :		List[Any]				=      AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path       ,      num_labels=_snake_case       ,      finetuning_task=data_args.task_name       ,      cache_dir=model_args.cache_dir       ,      )
      lowerCAmelCase    :		Optional[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]				=      AutoModelForMultipleChoice.from_pretrained(
          model_args.model_name_or_path       ,      from_tf=bool('''.ckpt''' in model_args.model_name_or_path	)       ,      config=_snake_case       ,      cache_dir=model_args.cache_dir       ,      )

      # Get datasets
      lowerCAmelCase    :		Dict				=      (
          MultipleChoiceDataset(
              data_dir=data_args.data_dir       ,      tokenizer=_snake_case       ,      task=data_args.task_name       ,      max_seq_length=data_args.max_seq_length       ,      overwrite_cache=data_args.overwrite_cache       ,      mode=Split.train       ,      )
          if training_args.do_train
          else None
      )
      lowerCAmelCase    :		Any				=      (
          MultipleChoiceDataset(
              data_dir=data_args.data_dir       ,      tokenizer=_snake_case       ,      task=data_args.task_name       ,      max_seq_length=data_args.max_seq_length       ,      overwrite_cache=data_args.overwrite_cache       ,      mode=Split.dev       ,      )
          if training_args.do_eval
          else None
      )

      def compute_metrics(_snake_case :	EvalPrediction	) -> Dict:
            lowerCAmelCase    :		int				=      np.argmax(p.predictions       ,      axis=1	)
            return {"acc": simple_accuracy(_snake_case       ,      p.label_ids	)}

      # Data collator
      lowerCAmelCase    :		List[Any]				=      DataCollatorWithPadding(_snake_case       ,      pad_to_multiple_of=8	) if training_args.fpaa else None

      # Initialize our Trainer
      lowerCAmelCase    :		Union[str, Any]				=      Trainer(
          model=_snake_case       ,      args=_snake_case       ,      train_dataset=_snake_case       ,      eval_dataset=_snake_case       ,      compute_metrics=_snake_case       ,      data_collator=_snake_case       ,      )

      # 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
      lowerCAmelCase    :		int				=      {}
      if training_args.do_eval:
            logger.info('''*** Evaluate ***'''	)

            lowerCAmelCase    :		Any				=      trainer.evaluate()

            lowerCAmelCase    :		int				=      os.path.join(training_args.output_dir       ,      '''eval_results.txt'''	)
            if trainer.is_world_master():
                  with open(_snake_case       ,      '''w'''	) as writer:
                        logger.info('''***** Eval results *****'''	)
                        for key, value in result.items():
                              logger.info('''  %s = %s'''       ,      _snake_case       ,      _snake_case	)
                              writer.write('''%s = %s\n''' % (key, value)	)

                        results.update(_snake_case	)

      return results


def    _snake_case  (  _snake_case :	List[str]	):
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
 main()


