





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


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


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  Optional[int]:
    return (preds == labels).mean()

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


def  SCREAMING_SNAKE_CASE   ( )    ->  Optional[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.

    lowerCamelCase__					:   Optional[int]      				=	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'		,			_UpperCAmelCase				)

    # Set seed
    set_seed(training_args.seed				)

    try:
        lowerCamelCase__					:   Optional[Any]      				=	processors[data_args.task_name]()
        lowerCamelCase__					:   List[Any]      				=	processor.get_labels()
        lowerCamelCase__					:   List[Any]      				=	len(_UpperCAmelCase				)
    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=_UpperCAmelCase		,			finetuning_task=data_args.task_name		,			cache_dir=model_args.cache_dir		,			)
    lowerCamelCase__					:   List[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__					:   Union[str, Any]      				=	AutoModelForMultipleChoice.from_pretrained(
        model_args.model_name_or_path		,			from_tf=bool('.ckpt' in model_args.model_name_or_path				)		,			config=_UpperCAmelCase		,			cache_dir=model_args.cache_dir		,			)

    # Get datasets
    lowerCamelCase__					:   str      				=	(
        MultipleChoiceDataset(
            data_dir=data_args.data_dir		,			tokenizer=_UpperCAmelCase		,			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__					:   Optional[int]      				=	(
        MultipleChoiceDataset(
            data_dir=data_args.data_dir		,			tokenizer=_UpperCAmelCase		,			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(_UpperCAmelCase				) -> Dict:
        lowerCamelCase__					:   List[Any]      				=	np.argmax(p.predictions		,			axis=1				)
        return {"acc": simple_accuracy(_UpperCAmelCase		,			p.label_ids				)}

    # Data collator
    lowerCamelCase__					:   Optional[int]      				=	DataCollatorWithPadding(_UpperCAmelCase		,			pad_to_multiple_of=8				) if training_args.fpaa else None

    # Initialize our Trainer
    lowerCamelCase__					:   Optional[Any]      				=	Trainer(
        model=_UpperCAmelCase		,			args=_UpperCAmelCase		,			train_dataset=_UpperCAmelCase		,			eval_dataset=_UpperCAmelCase		,			compute_metrics=_UpperCAmelCase		,			data_collator=_UpperCAmelCase		,			)

    # 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__					:   Optional[Any]      				=	{}
    if training_args.do_eval:
        logger.info('*** Evaluate ***'				)

        lowerCamelCase__					:   str      				=	trainer.evaluate()

        lowerCamelCase__					:   Union[str, Any]      				=	os.path.join(training_args.output_dir		,			'eval_results.txt'				)
        if trainer.is_world_master():
            with open(_UpperCAmelCase		,			'w'				) as writer:
                logger.info('***** Eval results *****'				)
                for key, value in result.items():
                    logger.info('  %s = %s'		,			_UpperCAmelCase		,			_UpperCAmelCase				)
                    writer.write('%s = %s\n' % (key, value)				)

                results.update(_UpperCAmelCase				)

    return results


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  str:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
    main()












from collections import defaultdict
from math import ceil, sqrt


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase = 100_0000		,			_UpperCAmelCase = 10				)    ->  int:
    lowerCamelCase__					:   defaultdict      				=	defaultdict(_UpperCAmelCase				)

    for outer_width in range(3		,			(t_limit // 4) + 2				):
        if outer_width * outer_width > t_limit:
            lowerCamelCase__					:   List[str]      				=	max(
                ceil(sqrt(outer_width * outer_width - t_limit				)				)		,			1				)
        else:
            lowerCamelCase__					:   int      				=	1

        hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

        for hole_width in range(_UpperCAmelCase		,			outer_width - 1		,			2				):
            count[outer_width * outer_width - hole_width * hole_width] += 1

    return sum(1 for n in count.values() if 1 <= n <= 10				)


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





