


"""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
)
_lowerCamelCase  =	logging.getLogger(__name__)


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

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

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

				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(lowerCamelCase_	:   Any	):
								# 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(lowerCamelCase_	):
																SCREAMING_SNAKE_CASE__  				=					model(lowerCamelCase_    ,       lm_labels=lowerCamelCase_    ,       mems=lowerCamelCase_	)
																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(lowerCamelCase_    ,       1000 * total_time / (idx + 1)	)	)
								return total_loss / total_len

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

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

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

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


if __name__ == "__main__":
	main()




"""simple docstring"""






def __lowercase  (   lowerCamelCase_	:   list	):
				if not isinstance(lowerCamelCase_    ,       lowerCamelCase_	):
								raise ValueError("Input series is not valid, valid series - [2, 4, 8]"	)
				if len(lowerCamelCase_	) == 0:
								raise ValueError("Input list must be a non empty list"	)
				if len(lowerCamelCase_	) == 1:
								return True
				try:
								SCREAMING_SNAKE_CASE__  				=					series[1] / series[0]
								for index in range(len(lowerCamelCase_	) - 1	):
												if series[index + 1] / series[index] != common_ratio:
																return False
				except ZeroDivisionError:
								return False
				return True


def __lowercase  (   lowerCamelCase_	:   list	):
				if not isinstance(lowerCamelCase_    ,       lowerCamelCase_	):
								raise ValueError("Input series is not valid, valid series - [2, 4, 8]"	)
				if len(lowerCamelCase_	) == 0:
								raise ValueError("Input list must be a non empty list"	)
				SCREAMING_SNAKE_CASE__  				=					1
				for value in series:
								answer *= value
				return pow(lowerCamelCase_    ,       1 / len(lowerCamelCase_	)	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
