


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

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


A :	Tuple												=     logging.getLogger(__name__)



@dataclass
class 							A					:
					'''simple docstring'''






					A__    =				field(
					    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}							)
					A__    =				field(
					    default=UpperCAmelCase__				,						metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}							)
					A__    =				field(
					    default=UpperCAmelCase__				,						metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}							)
					A__    =				field(default=UpperCAmelCase__				,						metadata={'''help''': '''Set this flag to use fast tokenization.'''}							)
					# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
					# or just modify its tokenizer_config.json.
					A__    =				field(
					    default=UpperCAmelCase__				,						metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}				,						)






def 				UpperCamelCase      (			)     ->						int:
		"""simple docstring"""




		lowercase__      =	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )

		if len(sys.argv  ) == 2 and sys.argv[1].endswith(""".json"""  ):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				lowercase__    ,			lowercase__    ,			lowercase__      =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
		else:
				lowercase__    ,			lowercase__    ,			lowercase__      =	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"""					,					__magic_name__  )

		# Prepare Question-Answering task
		# Load pretrained model and tokenizer
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.

		lowercase__      =	AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path					,					cache_dir=model_args.cache_dir					,					)
		lowercase__      =	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					,					use_fast=__magic_name__					,					)
		lowercase__      =	AutoModelForQuestionAnswering.from_pretrained(
		    model_args.model_name_or_path					,					from_tf=bool(""".ckpt""" in model_args.model_name_or_path  )					,					config=__magic_name__					,					cache_dir=model_args.cache_dir					,					)

		# Get datasets
		lowercase__      =	hasattr(model.config					,					"""lang2id"""  )
		lowercase__      =	(
		    SquadDataset(
		        __magic_name__					,					tokenizer=__magic_name__					,					is_language_sensitive=__magic_name__					,					cache_dir=model_args.cache_dir  )
		    if training_args.do_train
		    else None
		)
		lowercase__      =	(
		    SquadDataset(
		        __magic_name__					,					tokenizer=__magic_name__					,					mode="""dev"""					,					is_language_sensitive=__magic_name__					,					cache_dir=model_args.cache_dir					,					)
		    if training_args.do_eval
		    else None
		)

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

		# Initialize our Trainer
		lowercase__      =	Trainer(
		    model=__magic_name__					,					args=__magic_name__					,					train_dataset=__magic_name__					,					eval_dataset=__magic_name__					,					data_collator=__magic_name__					,					)

		# 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  )






def 				UpperCamelCase      (			__magic_name__  :							List[Any]  )     ->						Optional[Any]:
		"""simple docstring"""




		main()


if __name__ == "__main__":
	main()





import math






def 				UpperCamelCase      (			__magic_name__  :							int  )     ->						bool:
		"""simple docstring"""




		if 1 < number < 4:
				# 2 and 3 are primes
				return True
		elif number < 2 or number % 2 == 0 or number % 3 == 0:
				# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
				return False

		# All primes number are in format of 6k +/- 1
		for i in range(5					,					int(math.sqrt(__magic_name__  ) + 1  )					,					6  ):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True






def 				UpperCamelCase      (			__magic_name__  :							int = 1000					,					__magic_name__  :							int = 1000  )     ->						int:
		"""simple docstring"""




		lowercase__      =	[0, 0, 0]  # length, a, b
		for a in range((a_limit * -1) + 1					,					__magic_name__  ):
				for b in range(2					,					__magic_name__  ):
						if is_prime(__magic_name__  ):
								lowercase__      =	0
								lowercase__      =	0
								while is_prime((n**2) + (a * n) + b  ):
										count += 1
										n += 1
								if count > longest[0]:
										lowercase__      =	[count, a, b]
		lowercase__      =	longest[1] * longest[2]
		return ans


if __name__ == "__main__":
	print(solution(1_0_0_0, 1_0_0_0))

