

"""simple docstring"""

def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__      ) ->	int:
 if a < b:
  return gcd(lowercase__				,				lowercase__      )
 if a % b == 0:
  return b
 return gcd(lowercase__				,				a % b      )



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__				,				lowercase__      ) ->	int:
 if y == 0:
  return 1
 lowerCAmelCase__      :							List[str]    =							power(lowercase__				,				y // 2				,				lowercase__      ) % mod
 lowerCAmelCase__      :							Tuple    =							(temp * temp) % mod
 if y % 2 == 1:
  lowerCAmelCase__      :							str    =							(temp * x) % mod
 return temp



def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	bool:
 lowerCAmelCase__      :							Optional[Any]    =							2
 while b < n:
  if gcd(lowercase__				,				lowercase__      ) == 1 and power(lowercase__				,				n - 1				,				lowercase__      ) != 1:
   return False
  b += 1
 return True


if __name__ == "__main__":
    _UpperCamelCase		    =						int(input("""Enter number: """).strip())
    if is_carmichael_number(number):
        print(f"{number} is a Carmichael Number.")
    else:
        print(f"{number} is not a Carmichael Number.")









"""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


_UpperCamelCase		    =						logging.getLogger(__name__)






@dataclass
class  __a				:





       """simple docstring"""


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






@dataclass
class  __a				:





       """simple docstring"""


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




def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	Dict[str, torch.Tensor]:
 lowerCAmelCase__      :							List[Any]    =							default_data_collator(lowercase__      )
 lowerCAmelCase__      :							Union[str, Any]    =							batch.pop("pairID"				,				lowercase__      )
 return batch



def 			SCREAMING_SNAKE_CASE       (   ) ->	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__      :							str    =							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
 lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__      :							List[Any]    =							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:
  lowerCAmelCase__      :							Tuple    =							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__      :							int    =							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				,				)
 lowerCAmelCase__      :							Optional[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__      :							Union[str, Any]    =							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
 lowerCAmelCase__      :							Dict    =							(
     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
 )
 lowerCAmelCase__      :							Union[str, Any]    =							(
     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
 lowerCAmelCase__      :							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 ***"      )

  lowerCAmelCase__      :							Tuple    =							trainer.predict(lowercase__      )
  lowerCAmelCase__      :							Optional[Any]    =							output.predictions
  lowerCAmelCase__      :							Dict    =							np.argmax(lowercase__				,				axis=1      )

  lowerCAmelCase__      :							int    =							[ex.pairID for ex in eval_dataset]
  lowerCAmelCase__      :							Tuple    =							os.path.join(training_args.output_dir				,				"hans_predictions.txt"      )
  lowerCAmelCase__      :							Tuple    =							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 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	Optional[Any]:
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
    main()






