




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


SCREAMING_SNAKE_CASE__ :   Dict						=     logging.getLogger(__name__)




@dataclass
class 	UpperCamelCase__			:

    '''simple docstring'''



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




@dataclass
class 	UpperCamelCase__			:

    '''simple docstring'''



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




def   A   (   _SCREAMING_SNAKE_CASE					)			->				Dict[str, torch.Tensor]:
       lowerCamelCase     :   Tuple    				=      default_data_collator(_SCREAMING_SNAKE_CASE					)
       lowerCamelCase     :   int    				=      batch.pop("pairID"				,_SCREAMING_SNAKE_CASE					)
       return batch

def   A   (   )			->				int:
       # 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.

       lowerCamelCase     :   Any    				=      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
       lowerCamelCase				,   lowerCamelCase				,   lowerCamelCase     :   Dict    				=      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"				,_SCREAMING_SNAKE_CASE					)

       # Set seed
       set_seed(training_args.seed					)

       try:
              lowerCamelCase     :   str    				=      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.

       lowerCamelCase     :   Tuple    				=      AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path				,num_labels=_SCREAMING_SNAKE_CASE				,finetuning_task=data_args.task_name				,cache_dir=model_args.cache_dir				,)
       lowerCamelCase     :   int    				=      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				,)
       lowerCamelCase     :   List[str]    				=      AutoModelForSequenceClassification.from_pretrained(
           model_args.model_name_or_path				,from_tf=bool(".ckpt" in model_args.model_name_or_path					)				,config=_SCREAMING_SNAKE_CASE				,cache_dir=model_args.cache_dir				,)

       # Get datasets
       lowerCamelCase     :   int    				=      (
           HansDataset(
               data_dir=data_args.data_dir				,tokenizer=_SCREAMING_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
       )
       lowerCamelCase     :   Any    				=      (
           HansDataset(
               data_dir=data_args.data_dir				,tokenizer=_SCREAMING_SNAKE_CASE				,task=data_args.task_name				,max_seq_length=data_args.max_seq_length				,overwrite_cache=data_args.overwrite_cache				,evaluate=_SCREAMING_SNAKE_CASE				,)
           if training_args.do_eval
           else None
       )

       # Initialize our Trainer
       lowerCamelCase     :   Tuple    				=      Trainer(
           model=_SCREAMING_SNAKE_CASE				,args=_SCREAMING_SNAKE_CASE				,train_dataset=_SCREAMING_SNAKE_CASE				,eval_dataset=_SCREAMING_SNAKE_CASE				,data_collator=_SCREAMING_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 ***"					)

              lowerCamelCase     :   Dict    				=      trainer.predict(_SCREAMING_SNAKE_CASE					)
              lowerCamelCase     :   Optional[int]    				=      output.predictions
              lowerCamelCase     :   List[Any]    				=      np.argmax(_SCREAMING_SNAKE_CASE				,axis=1					)

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

              trainer._log(output.metrics					)

def   A   (   _SCREAMING_SNAKE_CASE					)			->				Optional[int]:
       # For xla_spawn (TPUs)
       main()


if __name__ == "__main__":
       main()












from itertools import compress, repeat
from math import ceil, sqrt

def   A   (   _SCREAMING_SNAKE_CASE					)			->				list[int]:

       if num <= 2:
              return []
       if num == 3:
              return [2]

       # Odd sieve for numbers in range [3, num - 1]
       lowerCamelCase     :   str    				=      bytearray(B"\x01"					) * ((num >> 1) - 1)

       for i in range(3				,int(sqrt(_SCREAMING_SNAKE_CASE					)					) + 1				,2					):
              if sieve[(i >> 1) - 1]:
                     lowerCamelCase     :   Tuple    				=      i**2
                     lowerCamelCase     :   str    				=      repeat(
                         0				,ceil((num - i_squared) / (i << 1)					)					)

       return [2] + list(compress(range(3				,_SCREAMING_SNAKE_CASE				,2					)				,_SCREAMING_SNAKE_CASE					)					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()






