





'''simple docstring'''

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

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()





def lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    str					,       __lowerCamelCase :    str					,       __lowerCamelCase :    PreTrainedTokenizer					,       __lowerCamelCase :    int					,       __lowerCamelCase :    Optional[int] = None					,       )						->Dict:
    _SCREAMING_SNAKE_CASE									=			{}

    if train_file is not None:
        _SCREAMING_SNAKE_CASE									=			[train_file]
    if eval_file is not None:
        _SCREAMING_SNAKE_CASE									=			[eval_file]
    if test_file is not None:
        _SCREAMING_SNAKE_CASE									=			[test_file]

    _SCREAMING_SNAKE_CASE									=			datasets.load_dataset("""csv"""					,       data_files=__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			list(ds[list(files.keys()    )[0]].features.keys()    )
    _SCREAMING_SNAKE_CASE									=			features_name.pop(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			list(set(ds[list(files.keys()    )[0]][label_name]    )    )
    _SCREAMING_SNAKE_CASE									=			{label: i for i, label in enumerate(__lowerCamelCase    )}
    _SCREAMING_SNAKE_CASE									=			tokenizer.model_input_names
    _SCREAMING_SNAKE_CASE									=			{}

    if len(__lowerCamelCase    ) == 1:
        for k in files.keys():
            _SCREAMING_SNAKE_CASE									=			ds[k].map(
                lambda __lowerCamelCase    : tokenizer.batch_encode_plus(
                    example[features_name[0]]					,       truncation=__lowerCamelCase					,       max_length=__lowerCamelCase					,       padding="""max_length"""    )					,       batched=__lowerCamelCase					,       )
    elif len(__lowerCamelCase    ) == 2:
        for k in files.keys():
            _SCREAMING_SNAKE_CASE									=			ds[k].map(
                lambda __lowerCamelCase    : tokenizer.batch_encode_plus(
                    (example[features_name[0]], example[features_name[1]])					,       truncation=__lowerCamelCase					,       max_length=__lowerCamelCase					,       padding="""max_length"""					,       )					,       batched=__lowerCamelCase					,       )

    def gen_train():
        for ex in transformed_ds[datasets.Split.TRAIN]:
            _SCREAMING_SNAKE_CASE									=			{k: v for k, v in ex.items() if k in input_names}
            _SCREAMING_SNAKE_CASE									=			labelaid[ex[label_name]]
            yield (d, label)

    def gen_val():
        for ex in transformed_ds[datasets.Split.VALIDATION]:
            _SCREAMING_SNAKE_CASE									=			{k: v for k, v in ex.items() if k in input_names}
            _SCREAMING_SNAKE_CASE									=			labelaid[ex[label_name]]
            yield (d, label)

    def gen_test():
        for ex in transformed_ds[datasets.Split.TEST]:
            _SCREAMING_SNAKE_CASE									=			{k: v for k, v in ex.items() if k in input_names}
            _SCREAMING_SNAKE_CASE									=			labelaid[ex[label_name]]
            yield (d, label)

    _SCREAMING_SNAKE_CASE									=			(
        tf.data.Dataset.from_generator(
            __lowerCamelCase					,       ({k: tf.intaa for k in input_names}, tf.intaa)					,       ({k: tf.TensorShape([None]    ) for k in input_names}, tf.TensorShape([]    ))					,       )
        if datasets.Split.TRAIN in transformed_ds
        else None
    )

    if train_ds is not None:
        _SCREAMING_SNAKE_CASE									=			train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]    )    )    )

    _SCREAMING_SNAKE_CASE									=			(
        tf.data.Dataset.from_generator(
            __lowerCamelCase					,       ({k: tf.intaa for k in input_names}, tf.intaa)					,       ({k: tf.TensorShape([None]    ) for k in input_names}, tf.TensorShape([]    ))					,       )
        if datasets.Split.VALIDATION in transformed_ds
        else None
    )

    if val_ds is not None:
        _SCREAMING_SNAKE_CASE									=			val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]    )    )    )

    _SCREAMING_SNAKE_CASE									=			(
        tf.data.Dataset.from_generator(
            __lowerCamelCase					,       ({k: tf.intaa for k in input_names}, tf.intaa)					,       ({k: tf.TensorShape([None]    ) for k in input_names}, tf.TensorShape([]    ))					,       )
        if datasets.Split.TEST in transformed_ds
        else None
    )

    if test_ds is not None:
        _SCREAMING_SNAKE_CASE									=			test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]    )    )    )

    return train_ds, val_ds, test_ds, labelaid


lowercase_					= logging.getLogger(__name__)


@dataclass
class 					a_     :

    '''simple docstring'''
    UpperCamelCase     = field(metadata={'''help''': '''Which column contains the label'''}       )
    UpperCamelCase     = field(default=snake_case_      ,    metadata={'''help''': '''The path of the training file'''}       )
    UpperCamelCase     = field(default=snake_case_      ,    metadata={'''help''': '''The path of the development file'''}       )
    UpperCamelCase     = field(default=snake_case_      ,    metadata={'''help''': '''The path of the test file'''}       )
    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=snake_case_      ,    metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}       )




@dataclass
class 					a_     :

    '''simple docstring'''
    UpperCamelCase     = field(
        metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}       )
    UpperCamelCase     = field(
        default=snake_case_      ,    metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}       )
    UpperCamelCase     = field(
        default=snake_case_      ,    metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}       )
    UpperCamelCase     = field(default=snake_case_      ,    metadata={'''help''': '''Set this flag to use fast tokenization.'''}       )
    # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
    # or just modify its tokenizer_config.json.
    UpperCamelCase     = field(
        default=snake_case_      ,    metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}      ,    )





def lowerCamelCase						(     )						->Union[str, 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.
    _SCREAMING_SNAKE_CASE									=			HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)    )
    _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			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					,       )
    logger.info(
        F'n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1    )}, '
        F'16-bits training: {training_args.fpaa}'    )
    logger.info(F'Training/evaluation parameters {training_args}'    )

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    _SCREAMING_SNAKE_CASE									=			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					,       )

    _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			get_tfds(
        train_file=data_args.train_file					,       eval_file=data_args.dev_file					,       test_file=data_args.test_file					,       tokenizer=__lowerCamelCase					,       label_column_id=data_args.label_column_id					,       max_seq_length=data_args.max_seq_length					,       )

    _SCREAMING_SNAKE_CASE									=			AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path					,       num_labels=len(__lowerCamelCase    )					,       labelaid=__lowerCamelCase					,       idalabel={id: label for label, id in labelaid.items()}					,       finetuning_task="""text-classification"""					,       cache_dir=model_args.cache_dir					,       )

    with training_args.strategy.scope():
        _SCREAMING_SNAKE_CASE									=			TFAutoModelForSequenceClassification.from_pretrained(
            model_args.model_name_or_path					,       from_pt=bool(""".bin""" in model_args.model_name_or_path    )					,       config=__lowerCamelCase					,       cache_dir=model_args.cache_dir					,       )

    def compute_metrics(__lowerCamelCase :    EvalPrediction    ) -> Dict:
        _SCREAMING_SNAKE_CASE									=			np.argmax(p.predictions					,       axis=1    )

        return {"acc": (preds == p.label_ids).mean()}

    # Initialize our Trainer
    _SCREAMING_SNAKE_CASE									=			TFTrainer(
        model=__lowerCamelCase					,       args=__lowerCamelCase					,       train_dataset=__lowerCamelCase					,       eval_dataset=__lowerCamelCase					,       compute_metrics=__lowerCamelCase					,       )

    # Training
    if training_args.do_train:
        trainer.train()
        trainer.save_model()
        tokenizer.save_pretrained(training_args.output_dir    )

    # Evaluation
    _SCREAMING_SNAKE_CASE									=			{}
    if training_args.do_eval:
        logger.info("""*** Evaluate ***"""    )
        _SCREAMING_SNAKE_CASE									=			trainer.evaluate()
        _SCREAMING_SNAKE_CASE									=			os.path.join(training_args.output_dir					,       """eval_results.txt"""    )

        with open(__lowerCamelCase					,       """w"""    ) as writer:
            logger.info("""***** Eval results *****"""    )

            for key, value in result.items():
                logger.info(F'  {key} = {value}'    )
                writer.write(F'{key} = {value}\n'    )

            results.update(__lowerCamelCase    )

    return results


if __name__ == "__main__":
      main()








def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  int:



    """simple docstring"""






    while second != 0:
        A :       int          =   first & second
        first ^= second
        A :       Tuple          =   c << 1
    return first


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     SCREAMING_SNAKE_CASE_:int             =						int(input("""Enter the first number: """).strip())
     SCREAMING_SNAKE_CASE_:Optional[int]             =						int(input("""Enter the second number: """).strip())
     print(F"""{add(first, second) = }""")


