






'''simple docstring'''







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

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


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






@dataclass
class        a__     :
     '''simple docstring'''







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







@dataclass
class        a__     :
     '''simple docstring'''







     _snake_case							   =	field(
         metadata={'help': 'The name of the task to train selected in the list: ' + ', '.join(hans_processors.keys()    )}    )
     _snake_case							   =	field(
         metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}    )
     _snake_case							   =	field(
         default=128			,    metadata={
             'help': (
                 'The maximum total input sequence length after tokenization. Sequences longer '
                 'than this will be truncated, sequences shorter will be padded.'
             )
         }			,    )
     _snake_case							   =	field(
         default=lowerCamelCase			,    metadata={'help': 'Overwrite the cached training and evaluation sets'}    )







def     _UpperCamelCase				(  snake_case__				):
      __A		 =   default_data_collator(snake_case__				)
      __A		 =   batch.pop("pairID"				,      snake_case__				)
      return batch


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.

      __A		 =   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
      __A ,					__A ,					__A		 =   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 if training_args.local_rank in [-1, 0] else logging.WARN				,      )
      logger.warning(
          "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"				,      training_args.local_rank				,      training_args.device				,      training_args.n_gpu				,      bool(training_args.local_rank != -1				)				,      training_args.fpaa				,      )
      # Set the verbosity to info of the Transformers logger (on main process only):
      if is_main_process(training_args.local_rank				):
            transformers.utils.logging.set_verbosity_info()
            transformers.utils.logging.enable_default_handler()
            transformers.utils.logging.enable_explicit_format()
      logger.info("Training/evaluation parameters %s"				,      snake_case__				)

      # Set seed
      set_seed(training_args.seed				)

      try:
            __A		 =   hans_tasks_num_labels[data_args.task_name]
      except KeyError:
            raise ValueError("Task not found: %s" % (data_args.task_name)				)

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

      __A		 =   AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path				,      num_labels=snake_case__				,      finetuning_task=data_args.task_name				,      cache_dir=model_args.cache_dir				,      )
      __A		 =   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		 =   AutoModelForSequenceClassification.from_pretrained(
          model_args.model_name_or_path				,      from_tf=bool(".ckpt" in model_args.model_name_or_path				)				,      config=snake_case__				,      cache_dir=model_args.cache_dir				,      )

      # Get datasets
      __A		 =   (
          HansDataset(
              data_dir=data_args.data_dir				,      tokenizer=snake_case__				,      task=data_args.task_name				,      max_seq_length=data_args.max_seq_length				,      overwrite_cache=data_args.overwrite_cache				,      )
          if training_args.do_train
          else None
      )
      __A		 =   (
          HansDataset(
              data_dir=data_args.data_dir				,      tokenizer=snake_case__				,      task=data_args.task_name				,      max_seq_length=data_args.max_seq_length				,      overwrite_cache=data_args.overwrite_cache				,      evaluate=snake_case__				,      )
          if training_args.do_eval
          else None
      )

      # Initialize our Trainer
      __A		 =   Trainer(
          model=snake_case__				,      args=snake_case__				,      train_dataset=snake_case__				,      eval_dataset=snake_case__				,      data_collator=snake_case__				,      )

      # Training
      if training_args.do_train:
            trainer.train(
                model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path				) else None				)
            trainer.save_model()
            # For convenience, we also re-save the tokenizer to the same directory,
            # so that you can share your model easily on huggingface.co/models =)
            if trainer.is_world_master():
                  tokenizer.save_pretrained(training_args.output_dir				)

    # Evaluation
      if training_args.do_eval:
            logger.info("*** Evaluate ***"				)

            __A		 =   trainer.predict(snake_case__				)
            __A		 =   output.predictions
            __A		 =   np.argmax(snake_case__				,      axis=1				)

            __A		 =   [ex.pairID for ex in eval_dataset]
            __A		 =   os.path.join(training_args.output_dir				,      "hans_predictions.txt"				)
            __A		 =   eval_dataset.get_labels()
            if trainer.is_world_master():
                  with open(snake_case__				,      "w"				) as writer:
                        writer.write("pairID,gold_label\n"				)
                        for pid, pred in zip(snake_case__				,      snake_case__				):
                              writer.write("ex" + str(snake_case__				) + "," + label_list[int(snake_case__				)] + "\n"				)

            trainer._log(output.metrics				)


def     _UpperCamelCase				(  snake_case__				):
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
       main()










'''simple docstring'''







def     _UpperCamelCase				(  snake_case__ = 1_000				):
      __A		 =   []
      __A		 =   []
      __A		 =   3
      __A		 =   5
      __A		 =   1
      while True:
            __A		 =   z * temp
            if result < n:
                  zmulti.append(snake_case__				)
                  temp += 1
            else:
                  __A		 =   1
                  break
      while True:
            __A		 =   x * temp
            if result < n:
                  xmulti.append(snake_case__				)
                  temp += 1
            else:
                  break
      __A		 =   list(set(xmulti + zmulti				)				)
      return sum(snake_case__				)


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


