
def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				if b == 0:
								return 1
				if (b % 2) == 0:
								return actual_power(__lowerCamelCase	, int(b / 2    )    ) * actual_power(__lowerCamelCase	, int(b / 2    )    )
				else:
								return a * actual_power(__lowerCamelCase	, int(b / 2    )    ) * actual_power(__lowerCamelCase	, int(b / 2    )    )





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				if b < 0:
								return 1 / actual_power(__lowerCamelCase	, __lowerCamelCase    )
				return actual_power(__lowerCamelCase	, __lowerCamelCase    )


if __name__ == "__main__":
			print(power(-2, -3))




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 			UpperCamelCase					(    __lowerCamelCase     : str				,					__lowerCamelCase     : str				,					__lowerCamelCase     : str				,					__lowerCamelCase     : PreTrainedTokenizer				,					__lowerCamelCase     : int				,					__lowerCamelCase     : Optional[int] = None				,					):
       snake_case       :   int							=      {}

       if train_file is not None:
              snake_case       :   List[Any]							=      [train_file]
       if eval_file is not None:
              snake_case       :   Optional[int]							=      [eval_file]
       if test_file is not None:
              snake_case       :   Any							=      [test_file]

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

       if len(__lowerCamelCase    ) == 1:
              for k in files.keys():
                     snake_case       :   Tuple							=      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       :   List[Any]							=      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       :   Dict							=      {k: v for k, v in ex.items() if k in input_names}
                     snake_case       :   Union[str, Any]							=      labelaid[ex[label_name]]
                     yield (d, label)

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

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

       snake_case       :   int							=      (
           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       :   Optional[Any]							=      train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]    )    )    )

       snake_case       :   Tuple							=      (
           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       :   List[str]							=      val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]    )    )    )

       snake_case       :   Optional[int]							=      (
           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       :   str							=      test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]    )    )    )

       return train_ds, val_ds, test_ds, labelaid


__lowerCamelCase							=       logging.getLogger(__name__)






@dataclass
class   UpperCAmelCase		:
   A__					:		int						     =     field(metadata={"help": "Which column contains the label"} )
   A__					:		str						     =     field(default=A_       ,metadata={"help": "The path of the training file"} )
   A__					:		Optional[str]						     =     field(default=A_       ,metadata={"help": "The path of the development file"} )
   A__					:		Optional[str]						     =     field(default=A_       ,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=A_       ,metadata={"help": "Overwrite the cached training and evaluation sets"} )






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



def 			UpperCamelCase					(    ):
       # 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       :   List[Any]							=      HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)    )
       snake_case				,					snake_case				,					snake_case       :   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				,					)
       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       :   Tuple							=      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       :   Tuple							=      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       :   Optional[Any]							=      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       :   int							=      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:
              snake_case       :   Optional[int]							=      np.argmax(p.predictions				,					axis=1    )

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

       # Initialize our Trainer
       snake_case       :   int							=      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       :   int							=      {}
       if training_args.do_eval:
              logger.info("*** Evaluate ***"    )
              snake_case       :   Any							=      trainer.evaluate()
              snake_case       :   List[Any]							=      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()





