
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 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase = None     ,		):
    snake_case__    =					{}

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

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

    if len(__lowerCAmelCase				) == 1:
        for k in files.keys():
            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():
            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]:
            snake_case__    =					{k: v for k, v in ex.items() if k in input_names}
            snake_case__    =					labelaid[ex[label_name]]
            yield (d, label)

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

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

    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:
        snake_case__    =					train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]				)				)				)

    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:
        snake_case__    =					val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]				)				)				)

    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:
        snake_case__    =					test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]				)				)				)

    return train_ds, val_ds, test_ds, labelaid


__magic_name__  							=	logging.getLogger(__name__)




@dataclass
class        _SCREAMING_SNAKE_CASE	:
      _A    :		int     						=  field(metadata={'help': 'Which column contains the label'}			)
      _A    :		str     						=  field(default=__UpperCamelCase							,       metadata={'help': 'The path of the training file'}			)
      _A    :		Optional[str]     						=  field(default=__UpperCamelCase							,       metadata={'help': 'The path of the development file'}			)
      _A    :		Optional[str]     						=  field(default=__UpperCamelCase							,       metadata={'help': 'The path of the test file'}			)
      _A    :		int     						=  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.'
              )
          }							,       )
      _A    :		bool     						=  field(
          default=__UpperCamelCase							,       metadata={'help': 'Overwrite the cached training and evaluation sets'}			)





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





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.
    snake_case__    =					HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
    snake_case__    ,       snake_case__    ,       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.

    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     ,		)

    snake_case__    ,       snake_case__    ,       snake_case__    ,       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     ,		)

    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():
        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:
        snake_case__    =					np.argmax(p.predictions     ,		axis=1				)

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

    # Initialize our Trainer
    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
    snake_case__    =					{}
    if training_args.do_eval:
        logger.info("*** Evaluate ***"				)
        snake_case__    =					trainer.evaluate()
        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 os
from datetime import datetime as dt

from github import Github


__magic_name__  							=	[
    '''good first issue''',
    '''feature request''',
    '''wip''',
]





def 						SCREAMING_SNAKE_CASE__     (   ):
    snake_case__    =					Github(os.environ["GITHUB_TOKEN"]				)
    snake_case__    =					g.get_repo("huggingface/accelerate"				)
    snake_case__    =					repo.get_issues(state="open"				)

    for issue in open_issues:
        snake_case__    =					sorted([comment for comment in issue.get_comments()]     ,		key=lambda __lowerCAmelCase				: i.created_at     ,		reverse=__lowerCAmelCase				)
        snake_case__    =					comments[0] if len(__lowerCAmelCase				) > 0 else None
        snake_case__    =					dt.utcnow()
        snake_case__    =					(current_time - issue.updated_at).days
        snake_case__    =					(current_time - issue.created_at).days
        if (
            last_comment is not None
            and last_comment.user.login == "github-actions[bot]"
            and days_since_updated > 7
            and days_since_creation >= 30
            and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()				)
        ):
            # Close issue since it has been 7 days of inactivity since bot mention.
            issue.edit(state="closed"				)
        elif (
            days_since_updated > 23
            and days_since_creation >= 30
            and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()				)
        ):
            # Add stale comment
            issue.create_comment(
                "This issue has been automatically marked as stale because it has not had "
                "recent activity. If you think this still needs to be addressed "
                "please comment on this thread.\n\nPlease note that issues that do not follow the "
                "[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) "
                "are likely to be ignored."				)


if __name__ == "__main__":
     main()



