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

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__lowerCamelCase      :			Dict							       =		logging.getLogger(__name__)



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Union[str, Any]			,					__UpperCamelCase   :	Optional[int]     )							-> List[str]:







  """simple docstring"""





  return (preds == labels).mean()







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







@dataclass
class __snake_case    :
   lowerCAmelCase_   							=							field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys()		)}		)
   lowerCAmelCase_   							=							field(metadata={"help": "Should contain the data files for the task."}		)
   lowerCAmelCase_   							=							field(
       default=1_28       ,		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=lowerCamelCase_       ,		metadata={"help": "Overwrite the cached training and evaluation sets"}		)





def      __SCREAMING_SNAKE_CASE  ( )							-> Any:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
  SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				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"""			,					__UpperCamelCase     )

  # Set seed
  set_seed(training_args.seed     )

  try:
    SCREAMING_SNAKE_CASE__      							=				processors[data_args.task_name]()
    SCREAMING_SNAKE_CASE__      							=				processor.get_labels()
    SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     )
  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.

  SCREAMING_SNAKE_CASE__      							=				AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path			,					num_labels=__UpperCamelCase			,					finetuning_task=data_args.task_name			,					cache_dir=model_args.cache_dir			,					)
  SCREAMING_SNAKE_CASE__      							=				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			,					)
  SCREAMING_SNAKE_CASE__      							=				AutoModelForMultipleChoice.from_pretrained(
      model_args.model_name_or_path			,					from_tf=bool(""".ckpt""" in model_args.model_name_or_path     )			,					config=__UpperCamelCase			,					cache_dir=model_args.cache_dir			,					)

  # Get datasets
  SCREAMING_SNAKE_CASE__      							=				(
      MultipleChoiceDataset(
          data_dir=data_args.data_dir			,					tokenizer=__UpperCamelCase			,					task=data_args.task_name			,					max_seq_length=data_args.max_seq_length			,					overwrite_cache=data_args.overwrite_cache			,					mode=Split.train			,					)
      if training_args.do_train
      else None
  )
  SCREAMING_SNAKE_CASE__      							=				(
      MultipleChoiceDataset(
          data_dir=data_args.data_dir			,					tokenizer=__UpperCamelCase			,					task=data_args.task_name			,					max_seq_length=data_args.max_seq_length			,					overwrite_cache=data_args.overwrite_cache			,					mode=Split.dev			,					)
      if training_args.do_eval
      else None
  )

  def compute_metrics(__UpperCamelCase   :	EvalPrediction     ) -> Dict:
    SCREAMING_SNAKE_CASE__      							=				np.argmax(p.predictions			,					axis=1     )
    return {"acc": simple_accuracy(__UpperCamelCase			,					p.label_ids     )}

  # Data collator
  SCREAMING_SNAKE_CASE__      							=				DataCollatorWithPadding(__UpperCamelCase			,					pad_to_multiple_of=8     ) if training_args.fpaa else None

  # Initialize our Trainer
  SCREAMING_SNAKE_CASE__      							=				Trainer(
      model=__UpperCamelCase			,					args=__UpperCamelCase			,					train_dataset=__UpperCamelCase			,					eval_dataset=__UpperCamelCase			,					compute_metrics=__UpperCamelCase			,					data_collator=__UpperCamelCase			,					)

  # 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
  SCREAMING_SNAKE_CASE__      							=				{}
  if training_args.do_eval:
    logger.info("""*** Evaluate ***"""     )

    SCREAMING_SNAKE_CASE__      							=				trainer.evaluate()

    SCREAMING_SNAKE_CASE__      							=				os.path.join(training_args.output_dir			,					"""eval_results.txt"""     )
    if trainer.is_world_master():
      with open(__UpperCamelCase			,					"""w"""     ) as writer:
        logger.info("""***** Eval results *****"""     )
        for key, value in result.items():
          logger.info("""  %s = %s"""			,					__UpperCamelCase			,					__UpperCamelCase     )
          writer.write("""%s = %s\n""" % (key, value)     )

        results.update(__UpperCamelCase     )

  return results



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Any     )							-> List[str]:







  """simple docstring"""





  main()


if __name__ == "__main__":
   main()

from math import isqrt



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int     )							-> list[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				[True] * max_number
  for i in range(2			,					isqrt(max_number - 1     ) + 1     ):
    if is_prime[i]:
      for j in range(i**2			,					__UpperCamelCase			,					__UpperCamelCase     ):
        SCREAMING_SNAKE_CASE__      							=				False

  return [i for i in range(2			,					__UpperCamelCase     ) if is_prime[i]]



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	int = 10**8     )							-> int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				calculate_prime_numbers(max_number // 2     )

  SCREAMING_SNAKE_CASE__      							=				0
  SCREAMING_SNAKE_CASE__      							=				0
  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     ) - 1
  while left <= right:
    while prime_numbers[left] * prime_numbers[right] >= max_number:
      right -= 1
    semiprimes_count += right - left + 1
    left += 1

  return semiprimes_count


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