


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_				(				__a			,				__a			,				__a			,				__a			,				__a			,				__a = None			,				)	->   str:
      a__	:  int     =   {}

      if train_file is not None:
            a__	:  int     =   [train_file]
      if eval_file is not None:
            a__	:  Union[str, Any]     =   [eval_file]
      if test_file is not None:
            a__	:  str     =   [test_file]

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

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

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

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

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

      a__	:  Optional[Any]     =   (
          tf.data.Dataset.from_generator(
              __a			,				({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:
            a__	:  Optional[int]     =   train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]      )      )      )

      a__	:  Union[str, Any]     =   (
          tf.data.Dataset.from_generator(
              __a			,				({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:
            a__	:  Optional[Any]     =   val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]      )      )      )

      a__	:  Union[str, Any]     =   (
          tf.data.Dataset.from_generator(
              __a			,				({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:
            a__	:  Tuple     =   test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]      )      )      )

      return train_ds, val_ds, test_ds, labelaid


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



@dataclass
class A__     :



  """simple docstring"""



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




@dataclass
class A__     :



  """simple docstring"""



  _lowercase									=		field(
      metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}  )
  _lowercase									=		field(
      default=A__				,		metadata={'help': 'Pretrained config name or path if not the same as model_name'}  )
  _lowercase									=		field(
      default=A__				,		metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}  )
  _lowercase									=		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.
  _lowercase									=		field(
      default=A__				,		metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}				,		)




def UpperCamelCase_				(				)	->   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.
      a__	:  str     =   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)      )
      a__,	a__,	a__	:  str     =   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.

      a__	:  Union[str, 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			,				)

      a__,	a__,	a__,	a__	:  Optional[Any]     =   get_tfds(
          train_file=data_args.train_file			,				eval_file=data_args.dev_file			,				test_file=data_args.test_file			,				tokenizer=__a			,				label_column_id=data_args.label_column_id			,				max_seq_length=data_args.max_seq_length			,				)

      a__	:  Optional[int]     =   AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path			,				num_labels=len(__a      )			,				labelaid=__a			,				idalabel={id: label for label, id in labelaid.items()}			,				finetuning_task="text-classification"			,				cache_dir=model_args.cache_dir			,				)

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

      def compute_metrics(__a      ) -> Dict:
            a__	:  Union[str, Any]     =   np.argmax(p.predictions			,				axis=1      )

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

      # Initialize our Trainer
      a__	:  Dict     =   TFTrainer(
          model=__a			,				args=__a			,				train_dataset=__a			,				eval_dataset=__a			,				compute_metrics=__a			,				)

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

      # Evaluation
      a__	:  Optional[Any]     =   {}
      if training_args.do_eval:
            logger.info("*** Evaluate ***"      )
            a__	:  Dict     =   trainer.evaluate()
            a__	:  int     =   os.path.join(training_args.output_dir			,				"eval_results.txt"      )

            with open(__a			,				"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(__a      )

      return results


if __name__ == "__main__":
    main()






'''simple docstring'''
def A__       (	UpperCAmelCase_ = 1_0_0_0				):
						_UpperCamelCase   :						List[str]												=  3
						_UpperCamelCase   :						Any												=  0
						while a < n:
												if a % 3 == 0 or a % 5 == 0:
																		result += a
												elif a % 1_5 == 0:
																		result -= a
												a += 1
						return result


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


