






"""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_     =				logging.getLogger(__name__)




def   _lowerCAmelCase		(				)		->List[Any]:
						A__    :	Tuple			=	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=1_0,    help="""batch size"""   )
						parser.add_argument("""--tgt_len""",    type=UpperCAmelCase__,    default=1_2_8,    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_6_0_0,    help="""length of the retained previous heads"""   )
						parser.add_argument("""--clamp_len""",    type=UpperCAmelCase__,    default=1_0_0_0,    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__    :	Tuple			=	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__    :	Tuple			=	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__    :	Any			=	TransfoXLCorpus.from_pretrained(args.model_name   )

						A__    :	List[str]			=	corpus.get_iterator("""valid""",    args.batch_size,    args.tgt_len,    device=UpperCAmelCase__,    ext_len=args.ext_len   )
						A__    :	Optional[int]			=	corpus.get_iterator("""test""",    args.batch_size,    args.tgt_len,    device=UpperCAmelCase__,    ext_len=args.ext_len   )

						# Load a pre-trained model
						A__    :	Tuple			=	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__    :	Any			=	args.clamp_len
						if args.same_length:
												A__    :	List[Any]			=	True

						###############################################################################
						# Evaluation code
						###############################################################################
						def evaluate(UpperCAmelCase__							:       Optional[int]   ):
												# Turn on evaluation mode which disables dropout.
												model.eval()
												A__	,	A__    :	Optional[int]			=	0, 0.0
												A__    :	Dict			=	time.time()
												with torch.no_grad():
																		A__    :	Tuple			=	None
																		for idx, (data, target, seq_len) in enumerate(UpperCAmelCase__   ):
																								A__    :	Union[str, Any]			=	model(UpperCAmelCase__,    lm_labels=UpperCAmelCase__,    mems=UpperCAmelCase__   )
																								A__	,	A__	,	A__    :	Dict			=	ret
																								A__    :	Optional[int]			=	loss.mean()
																								total_loss += seq_len * loss.item()
																								total_len += seq_len
																		A__    :	List[Any]			=	time.time() - start_time
												logger.info("""Time : {:.2f}s, {:.2f}ms/segment""".format(UpperCAmelCase__,    1_0_0_0 * total_time / (idx + 1)   )   )
												return total_loss / total_len

						# Run on test data.
						if args.split == "all":
												A__    :	Optional[Any]			=	evaluate(UpperCAmelCase__   )
												A__    :	List[Any]			=	evaluate(UpperCAmelCase__   )
						elif args.split == "valid":
												A__    :	Tuple			=	evaluate(UpperCAmelCase__   )
												A__    :	List[str]			=	None
						elif args.split == "test":
												A__    :	int			=	evaluate(UpperCAmelCase__   )
												A__    :	List[str]			=	None

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

						A__    :	Union[str, Any]			=	""""""
						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("""=""" * 1_0_0   )
						logger.info(UpperCAmelCase__   )
						logger.info("""=""" * 1_0_0   )


if __name__ == "__main__":
						main()














"""simple docstring"""



def   _lowerCAmelCase		(				UpperCAmelCase__							:       int = 4_0_0_0_0_0_0   )		->int:
						A__    :	int			=	1
						A__    :	List[str]			=	2
						A__    :	Union[str, Any]			=	0
						while j <= n:
												if j % 2 == 0:
																		total += j
												A__	,	A__    :	str			=	j, i + j

						return total


if __name__ == "__main__":
						print(F'{solution() = }')






