





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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ = None ,							)				->						int:
    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


_A					:				Any				=			logging.getLogger(__name__)



@dataclass
class 					__snake_case	:


    '''simple docstring'''







    lowerCamelCase__    :		int				=	field(metadata={"""help""": """Which column contains the label"""}    )
    lowerCamelCase__    :		str				=	field(default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """The path of the training file"""}    )
    lowerCamelCase__    :		Optional[str]				=	field(default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """The path of the development file"""}    )
    lowerCamelCase__    :		Optional[str]				=	field(default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """The path of the test file"""}    )
    lowerCamelCase__    :		int				=	field(
        default=1_2_8			,     metadata={
            """help""": (
                """The maximum total input sequence length after tokenization. Sequences longer """
                """than this will be truncated, sequences shorter will be padded."""
            )
        }			,     )
    lowerCamelCase__    :		bool				=	field(
        default=__SCREAMING_SNAKE_CASE			,     metadata={"""help""": """Overwrite the cached training and evaluation sets"""}    )





@dataclass
class 					__snake_case	:


    '''simple docstring'''







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





def 							__snake_case							(    )				->						List[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_	) -> 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()

import re






def _lowerCamelCase  (		snake_case  ):

							if len(re.findall('[ATCG]'     , snake_case  )  ) != len(snake_case  ):
														raise ValueError('Invalid Strand'  )

							return dna.translate(dna.maketrans('ATCG'     , 'TAGC'  )  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()






