

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:      Optional[Any]								=     logging.getLogger(__name__)



def  __lowerCAmelCase (  ):
	UpperCAmelCase_    			=     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=128    ,       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=1600    ,       help="length of the retained previous heads"      )
	parser.add_argument("--clamp_len"    ,       type=A    ,       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=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."      )
	UpperCAmelCase_    			=     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()

	UpperCAmelCase_    			=     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 )
	UpperCAmelCase_    			=     TransfoXLCorpus.from_pretrained(args.model_name      )

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

	# Load a pre-trained model
	UpperCAmelCase_    			=     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:
		UpperCAmelCase_    			=     args.clamp_len
	if args.same_length:
		UpperCAmelCase_    			=     True

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

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

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

	UpperCAmelCase_    			=     ""
	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("=" * 100      )
	logger.info(A      )
	logger.info("=" * 100      )


if __name__ == "__main__":
				main()


def  __lowerCAmelCase (  A      ):
	UpperCAmelCase_    			=     len(A      )

	# Set the gap value to be decreased by a factor of 1.3
	# after each iteration
	UpperCAmelCase_    			=     1.3

	# Continue sorting until the gap is 1
	while gap > 1:
		# Decrease the gap value
		UpperCAmelCase_    			=     int(gap / shrink      )

		# Sort the elements using insertion sort
		for i in range(A    ,       len(A      )      ):
			UpperCAmelCase_    			=     collection[i]
			UpperCAmelCase_    			=     i
			while j >= gap and collection[j - gap] > temp:
				UpperCAmelCase_    			=     collection[j - gap]
				j -= gap
			UpperCAmelCase_    			=     temp

	return collection


if __name__ == "__main__":
				import doctest

				doctest.testmod()