

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						:Dict            = logging.getLogger(__name__)

@dataclass
class      _a				:




      """simple docstring"""

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




@dataclass
class      _a				:




      """simple docstring"""

      _lowerCamelCase					: List[str]					=					field(
          metadata={'help': 'The name of the task to train selected in the list: ' + ', '.join(hans_processors.keys()							)}							)
      _lowerCamelCase					: Optional[int]					=					field(
          metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}							)
      _lowerCamelCase					: Union[str, Any]					=					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.'
              )
          }    ,							)
      _lowerCamelCase					: Tuple					=					field(
          default=UpperCamelCase_    ,							metadata={'help': 'Overwrite the cached training and evaluation sets'}							)




def 			__snake_case		(   __UpperCamelCase  :   List[str]						):



      """simple docstring"""

      A_	     =							default_data_collator(snake_case_						)
      A_	     =							batch.pop("pairID"							,snake_case_						)
      return batch

def 			__snake_case		(   ):



      """simple docstring"""

      A_	     =							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
      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 			__snake_case		(   __UpperCamelCase  :   Optional[Any]						):



      """simple docstring"""

      main()


if __name__ == "__main__":
      main()


def 			__snake_case	(   __UpperCamelCase  :   str						):



      """simple docstring"""

      A_	     =							len(__UpperCamelCase						)
      A_	     =							[0] * length
      A_	     =							[[False for i in range(__UpperCamelCase						)] for j in range(__UpperCamelCase						)]
      for i, c in enumerate(__UpperCamelCase						):
            A_	     =							i
            for j in range(i + 1						):
                  if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
                        A_	     =							True
                        A_	     =							min(__UpperCamelCase							,0 if j == 0 else (cut[j - 1] + 1)						)
            A_	     =							mincut
      return cut[length - 1]


if __name__ == "__main__":
      __a						:Union[str, Any]            = input('Enter the string: ').strip()
      __a						:Tuple            = find_minimum_partitions(s)
      print(F"Minimum number of partitions required for the '{s}' is {ans}")