





"""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
)
_snake_case	:						Tuple					=					logging.getLogger(__name__)






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

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

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

							A						=    corpus.get_iterator("valid"   ,    args.batch_size   ,    args.tgt_len   ,    device=UpperCamelCase   ,    ext_len=args.ext_len			)
							A						=    corpus.get_iterator("test"   ,    args.batch_size   ,    args.tgt_len   ,    device=UpperCamelCase   ,    ext_len=args.ext_len			)

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

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

							###############################################################################
							# Evaluation code
							###############################################################################
							def evaluate(UpperCamelCase			):
														# Turn on evaluation mode which disables dropout.
														model.eval()
														A,      A						=    0, 0.0
														A						=    time.time()
														with torch.no_grad():
																					A						=    None
																					for idx, (data, target, seq_len) in enumerate(UpperCamelCase			):
																												A						=    model(UpperCamelCase   ,    lm_labels=UpperCamelCase   ,    mems=UpperCamelCase			)
																												A,      A,      A						=    ret
																												A						=    loss.mean()
																												total_loss += seq_len * loss.item()
																												total_len += seq_len
																					A						=    time.time() - start_time
														logger.info("Time : {:.2f}s, {:.2f}ms/segment".format(UpperCamelCase   ,    1_000 * total_time / (idx + 1)			)			)
														return total_loss / total_len

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

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

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

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


if __name__ == "__main__":
		main()









"""simple docstring"""
from __future__ import annotations






def    A__							(     UpperCamelCase			):
							A						=    42
							A						=    list(UpperCamelCase			)
							A,      A						=    _vector[:-1], int(_vector[-1]			)
							A						=    [int(UpperCamelCase			) for digit in __vector]

							vector.reverse()
							for i, digit in enumerate(UpperCamelCase			):
														if i & 1 == 0:
																					A						=    digit * 2
																					if doubled > 9:
																												doubled -= 9
																					check_digit += doubled
														else:
																					check_digit += digit

							return check_digit % 10 == 0


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		assert is_luhn('79927398713')
		assert not is_luhn('79927398714')


