




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							= logging.getLogger(__name__)

@dataclass
class   __a:






    """simple docstring"""





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


@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase  			=      field(
        metadata={'''help''': '''The name of the task to train selected in the list: ''' + ''', '''.join(hans_processors.keys()   )}   )
    lowerCAmelCase  			=      field(
        metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}   )
    lowerCAmelCase  			=      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.'''
            )
        }				,						)
    lowerCAmelCase  			=      field(
        default=_UpperCamelCase				,						metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}   )







def lowerCamelCase__   (			_lowercase      ):

     '''simple docstring'''


     UpperCAmelCase_			:							List[Any]   =			default_data_collator(__lowercase      )
     UpperCAmelCase_			:							Optional[int]   =			batch.pop('''pairID''' ,						__lowercase      )
     return batch







def lowerCamelCase__   (			):

     '''simple docstring'''


     UpperCAmelCase_			:							Any   =			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
     UpperCAmelCase_,    UpperCAmelCase_,    UpperCAmelCase_			:							List[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 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''' ,						__lowercase      )

     # Set seed
     set_seed(training_args.seed      )

     try:
          UpperCAmelCase_			:							Any   =			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.

     UpperCAmelCase_			:							Optional[Any]   =			AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path ,						num_labels=__lowercase ,						finetuning_task=data_args.task_name ,						cache_dir=model_args.cache_dir ,						)
     UpperCAmelCase_			:							List[str]   =			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 ,						)
     UpperCAmelCase_			:							List[str]   =			AutoModelForSequenceClassification.from_pretrained(
         model_args.model_name_or_path ,						from_tf=bool('''.ckpt''' in model_args.model_name_or_path      ) ,						config=__lowercase ,						cache_dir=model_args.cache_dir ,						)

     # Get datasets
     UpperCAmelCase_			:							Optional[int]   =			(
         HansDataset(
             data_dir=data_args.data_dir ,						tokenizer=__lowercase ,						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
     )
     UpperCAmelCase_			:							str   =			(
         HansDataset(
             data_dir=data_args.data_dir ,						tokenizer=__lowercase ,						task=data_args.task_name ,						max_seq_length=data_args.max_seq_length ,						overwrite_cache=data_args.overwrite_cache ,						evaluate=__lowercase ,						)
         if training_args.do_eval
         else None
     )

     # Initialize our Trainer
     UpperCAmelCase_			:							Any   =			Trainer(
         model=__lowercase ,						args=__lowercase ,						train_dataset=__lowercase ,						eval_dataset=__lowercase ,						data_collator=__lowercase ,						)

     # 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 ***'''      )

          UpperCAmelCase_			:							Optional[Any]   =			trainer.predict(__lowercase      )
          UpperCAmelCase_			:							Any   =			output.predictions
          UpperCAmelCase_			:							Dict   =			np.argmax(__lowercase ,						axis=1      )

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

          trainer._log(output.metrics      )







def lowerCamelCase__   (			_lowercase      ):

     '''simple docstring'''


     main()


if __name__ == "__main__":
     main()



"""simple docstring"""


import math
from decimal import Decimal, getcontext






def    a__       ( __lowercase = 400_0000      )		->		int:

				try:
								_A         =	int(__lowercase      )
				except (TypeError, ValueError):
								raise TypeError("Parameter n must be int or castable to int."      )
				if n <= 0:
								raise ValueError("Parameter n must be greater than or equal to one."      )
				_A         =	100
				_A         =	(Decimal(5      ) ** Decimal(0.5      ) + 1) / Decimal(2      )

				_A         =	(math.floor(math.log(n * (phi + 2)   ,	__lowercase      ) - 1      ) // 3) * 3 + 2
				_A         =	Decimal(round(phi ** Decimal(index + 1      )      )      ) / (phi + 2)
				_A         =	num // 2
				return int(__lowercase      )


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