




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
)
lowerCamelCase_  						=   logging.getLogger(__name__)



def 	UpperCAmelCase_    (	):
    SCREAMING_SNAKE_CASE__								=argparse.ArgumentParser(description="""PyTorch Transformer Language Model"""	)
    parser.add_argument("""--model_name""",				type=snake_case__,				default="""transfo-xl-wt103""",				help="""pretrained model name"""	)
    parser.add_argument(
        """--split""",				type=snake_case__,				default="""test""",				choices=["""all""", """valid""", """test"""],				help="""which split to evaluate"""	)
    parser.add_argument("""--batch_size""",				type=snake_case__,				default=10,				help="""batch size"""	)
    parser.add_argument("""--tgt_len""",				type=snake_case__,				default=128,				help="""number of tokens to predict"""	)
    parser.add_argument("""--ext_len""",				type=snake_case__,				default=0,				help="""length of the extended context"""	)
    parser.add_argument("""--mem_len""",				type=snake_case__,				default=1_600,				help="""length of the retained previous heads"""	)
    parser.add_argument("""--clamp_len""",				type=snake_case__,				default=1_000,				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=snake_case__,				required=snake_case__,				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=snake_case__,				default="""""",				help="""Can be used for distant debugging."""	)
    parser.add_argument("""--server_port""",				type=snake_case__,				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=snake_case__	)
        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(snake_case__	)	)

    # 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=snake_case__,				ext_len=args.ext_len	)
    SCREAMING_SNAKE_CASE__								=corpus.get_iterator("""test""",				args.batch_size,				args.tgt_len,				device=snake_case__,				ext_len=args.ext_len	)

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

    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(__UpperCamelCase	):
        # 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(snake_case__	):
                SCREAMING_SNAKE_CASE__								=model(snake_case__,				lm_labels=snake_case__,				mems=snake_case__	)
                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(snake_case__,				1_000 * total_time / (idx + 1)	)	)
        return total_loss / total_len

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

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

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

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


if __name__ == "__main__":
    main()









import os
def 						UpperCamelCase_(			)       -> int:
 UpperCAmelCase__							=				os.path.dirname(os.path.realpath(snake_case__   )   )
 UpperCAmelCase__							=				os.path.join(snake_case__  ,    'triangle.txt'   )

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

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


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



