


"""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
)
__magic_name__	:   str          =							logging.getLogger(__name__)





def 			a_   (  ):
			__lowerCamelCase        =			argparse.ArgumentParser(description="""PyTorch Transformer Language Model"""    )
			parser.add_argument("""--model_name""", type=lowercase__, default="""transfo-xl-wt103""", help="""pretrained model name"""    )
			parser.add_argument(
			    """--split""", type=lowercase__, default="""test""", choices=["""all""", """valid""", """test"""], help="""which split to evaluate"""    )
			parser.add_argument("""--batch_size""", type=lowercase__, default=10, help="""batch size"""    )
			parser.add_argument("""--tgt_len""", type=lowercase__, default=128, help="""number of tokens to predict"""    )
			parser.add_argument("""--ext_len""", type=lowercase__, default=0, help="""length of the extended context"""    )
			parser.add_argument("""--mem_len""", type=lowercase__, default=1600, help="""length of the retained previous heads"""    )
			parser.add_argument("""--clamp_len""", type=lowercase__, default=1000, 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=lowercase__, required=lowercase__, 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=lowercase__, default="""""", help="""Can be used for distant debugging."""    )
			parser.add_argument("""--server_port""", type=lowercase__, default="""""", help="""Can be used for distant debugging."""    )
			__lowerCamelCase        =			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=lowercase__    )
						ptvsd.wait_for_attach()

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

			# 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 )
			__lowerCamelCase        =			TransfoXLCorpus.from_pretrained(args.model_name    )

			__lowerCamelCase        =			corpus.get_iterator("""valid""", args.batch_size, args.tgt_len, device=lowercase__, ext_len=args.ext_len    )
			__lowerCamelCase        =			corpus.get_iterator("""test""", args.batch_size, args.tgt_len, device=lowercase__, ext_len=args.ext_len    )

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

			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:
						__lowerCamelCase        =			args.clamp_len
			if args.same_length:
						__lowerCamelCase        =			True

			###############################################################################
			# Evaluation code
			###############################################################################
			def evaluate(lowercase__   :List[str]    ):
						# Turn on evaluation mode which disables dropout.
						model.eval()
						__lowerCamelCase    ,__lowerCamelCase        =			0, 0.0
						__lowerCamelCase        =			time.time()
						with torch.no_grad():
									__lowerCamelCase        =			None
									for idx, (data, target, seq_len) in enumerate(lowercase__    ):
												__lowerCamelCase        =			model(lowercase__, lm_labels=lowercase__, mems=lowercase__    )
												__lowerCamelCase    ,__lowerCamelCase    ,__lowerCamelCase        =			ret
												__lowerCamelCase        =			loss.mean()
												total_loss += seq_len * loss.item()
												total_len += seq_len
									__lowerCamelCase        =			time.time() - start_time
						logger.info("""Time : {:.2f}s, {:.2f}ms/segment""".format(lowercase__, 1000 * total_time / (idx + 1)    )    )
						return total_loss / total_len

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

			def format_log(lowercase__   :Optional[Any], lowercase__   :str    ):
						__lowerCamelCase        =			"""| {0} loss {1:5.2f} | {0} ppl {2:9.3f} """.format(lowercase__, lowercase__, math.exp(lowercase__    )    )
						return log_str

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

			logger.info("""=""" * 100    )
			logger.info(lowercase__    )
			logger.info("""=""" * 100    )


if __name__ == "__main__":
					main()








"""simple docstring"""




import os





def 			a_   (  ):
			__lowerCamelCase        =			os.path.dirname(os.path.realpath(lowercase__    )    )
			__lowerCamelCase        =			os.path.join(lowercase__, """triangle.txt"""    )

			with open(lowercase__    ) as in_file:
						__lowerCamelCase        =			[[int(lowercase__    ) for i in line.split()] for line in in_file]

			while len(lowercase__    ) != 1:
						__lowerCamelCase        =			triangle.pop()
						__lowerCamelCase        =			triangle[-1]
						for j in range(len(lowercase__    ) - 1    ):
									curr_row[j] += max(last_row[j], last_row[j + 1]    )
			return triangle[0][0]


if __name__ == "__main__":
					print(solution())




