

"""simple docstring"""




import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
A_							:     List[str]     =	logging.getLogger(__name__)

def 		A    (       ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= argparse.ArgumentParser(description="""PyTorch Transformer Language Model"""      )
			parser.add_argument("""--model_name"""						,			type=__A						,			default="""transfo-xl-wt103"""						,			help="""pretrained model name"""      )
			parser.add_argument(
			    """--split"""						,			type=__A						,			default="""test"""						,			choices=["""all""", """valid""", """test"""]						,			help="""which split to evaluate"""      )
			parser.add_argument("""--batch_size"""						,			type=__A						,			default=10						,			help="""batch size"""      )
			parser.add_argument("""--tgt_len"""						,			type=__A						,			default=1_28						,			help="""number of tokens to predict"""      )
			parser.add_argument("""--ext_len"""						,			type=__A						,			default=0						,			help="""length of the extended context"""      )
			parser.add_argument("""--mem_len"""						,			type=__A						,			default=16_00						,			help="""length of the retained previous heads"""      )
			parser.add_argument("""--clamp_len"""						,			type=__A						,			default=10_00						,			help="""max positional embedding index"""      )
			parser.add_argument("""--no_cuda"""						,			action="""store_true"""						,			help="""Do not use CUDA even though CUA is available"""      )
			parser.add_argument("""--work_dir"""						,			type=__A						,			required=__A						,			help="""path to the work_dir"""      )
			parser.add_argument("""--no_log"""						,			action="""store_true"""						,			help="""do not log the eval result"""      )
			parser.add_argument("""--same_length"""						,			action="""store_true"""						,			help="""set same length attention with masking"""      )
			parser.add_argument("""--server_ip"""						,			type=__A						,			default=""""""						,			help="""Can be used for distant debugging."""      )
			parser.add_argument("""--server_port"""						,			type=__A						,			default=""""""						,			help="""Can be used for distant debugging."""      )
			SCREAMING_SNAKE_CASE__									= parser.parse_args()
			assert args.ext_len >= 0, "extended context length must be non-negative"

			if args.server_ip and args.server_port:
						# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
						import ptvsd

						print("""Waiting for debugger attach"""      )
						ptvsd.enable_attach(address=(args.server_ip, args.server_port)						,			redirect_output=__A      )
						ptvsd.wait_for_attach()

			SCREAMING_SNAKE_CASE__									= torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""      )
			logger.info("""device: {}""".format(__A      )      )

			# Load a pre-processed dataset
			# You can also build the corpus yourself using TransfoXLCorpus methods
			# The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
			# and tokenizing the dataset
			# The pre-processed corpus is a convertion (using the conversion script )
			SCREAMING_SNAKE_CASE__									= TransfoXLCorpus.from_pretrained(args.model_name      )

			SCREAMING_SNAKE_CASE__									= corpus.get_iterator("""valid"""						,			args.batch_size						,			args.tgt_len						,			device=__A						,			ext_len=args.ext_len      )
			SCREAMING_SNAKE_CASE__									= corpus.get_iterator("""test"""						,			args.batch_size						,			args.tgt_len						,			device=__A						,			ext_len=args.ext_len      )

			# Load a pre-trained model
			SCREAMING_SNAKE_CASE__									= TransfoXLLMHeadModel.from_pretrained(args.model_name      )
			model.to(__A      )

			logger.info(
			    """Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}""".format(
			        args.batch_size						,			args.tgt_len						,			args.ext_len						,			args.mem_len						,			args.clamp_len      )      )

			model.reset_memory_length(args.mem_len      )
			if args.clamp_len > 0:
						SCREAMING_SNAKE_CASE__									= args.clamp_len
			if args.same_length:
						SCREAMING_SNAKE_CASE__									= True

			###############################################################################
			# Evaluation code
			###############################################################################
			def evaluate(snake_case__      ):
						# Turn on evaluation mode which disables dropout.
						model.eval()
						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= 0, 0.0
						SCREAMING_SNAKE_CASE__									= time.time()
						with torch.no_grad():
									SCREAMING_SNAKE_CASE__									= None
									for idx, (data, target, seq_len) in enumerate(__A      ):
												SCREAMING_SNAKE_CASE__									= model(__A						,			lm_labels=__A						,			mems=__A      )
												SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= ret
												SCREAMING_SNAKE_CASE__									= loss.mean()
												total_loss += seq_len * loss.item()
												total_len += seq_len
									SCREAMING_SNAKE_CASE__									= time.time() - start_time
						logger.info("""Time : {:.2f}s, {:.2f}ms/segment""".format(__A						,			10_00 * total_time / (idx + 1)      )      )
						return total_loss / total_len

			# Run on test data.
			if args.split == "all":
						SCREAMING_SNAKE_CASE__									= evaluate(__A      )
						SCREAMING_SNAKE_CASE__									= evaluate(__A      )
			elif args.split == "valid":
						SCREAMING_SNAKE_CASE__									= evaluate(__A      )
						SCREAMING_SNAKE_CASE__									= None
			elif args.split == "test":
						SCREAMING_SNAKE_CASE__									= evaluate(__A      )
						SCREAMING_SNAKE_CASE__									= None

			def format_log(snake_case__						,			snake_case__      ):
						SCREAMING_SNAKE_CASE__									= """| {0} loss {1:5.2f} | {0} ppl {2:9.3f} """.format(__A						,			__A						,			math.exp(__A      )      )
						return log_str

			SCREAMING_SNAKE_CASE__									= """"""
			if valid_loss is not None:
						log_str += format_log(__A						,			"""valid"""      )
			if test_loss is not None:
						log_str += format_log(__A						,			"""test"""      )

			logger.info("""=""" * 1_00      )
			logger.info(__A      )
			logger.info("""=""" * 1_00      )


if __name__ == "__main__":
						main()





"""simple docstring"""




import math

def 		A    (       snake_case__      ):




			'''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(snake_case__      ) + 1      )						,			6      ):
						if number % i == 0 or number % (i + 2) == 0:
									return False
			return True

def 		A    (       snake_case__ = 10_00						,			snake_case__ = 10_00      ):




			'''simple docstring'''


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


if __name__ == "__main__":
						print(solution(1_000, 1_000))


