
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
)
SCREAMING_SNAKE_CASE__     :    Dict      							=					logging.getLogger(__name__)




def 				__magic_name__				(     )    ->     Union[str, Any]:
				__lowerCamelCase					      =			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.'''							)
				__lowerCamelCase					      =			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()

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

				__lowerCamelCase					      =			corpus.get_iterator('''valid'''	,						args.batch_size	,						args.tgt_len	,						device=__lowerCAmelCase	,						ext_len=args.ext_len							)
				__lowerCamelCase					      =			corpus.get_iterator('''test'''	,						args.batch_size	,						args.tgt_len	,						device=__lowerCAmelCase	,						ext_len=args.ext_len							)

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

				###############################################################################
				# Evaluation code
				###############################################################################
				def evaluate(__lowerCAmelCase			:		Any							):
								# Turn on evaluation mode which disables dropout.
								model.eval()
								__lowerCamelCase      ,					__lowerCamelCase					      =			0, 0.0
								__lowerCamelCase					      =			time.time()
								with torch.no_grad():
												__lowerCamelCase					      =			None
												for idx, (data, target, seq_len) in enumerate(__lowerCAmelCase							):
																__lowerCamelCase					      =			model(__lowerCAmelCase	,						lm_labels=__lowerCAmelCase	,						mems=__lowerCAmelCase							)
																__lowerCamelCase      ,					__lowerCamelCase      ,					__lowerCamelCase					      =			ret
																__lowerCamelCase					      =			loss.mean()
																total_loss += seq_len * loss.item()
																total_len += seq_len
												__lowerCamelCase					      =			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":
								__lowerCamelCase					      =			evaluate(__lowerCAmelCase							)
								__lowerCamelCase					      =			evaluate(__lowerCAmelCase							)
				elif args.split == "valid":
								__lowerCamelCase					      =			evaluate(__lowerCAmelCase							)
								__lowerCamelCase					      =			None
				elif args.split == "test":
								__lowerCamelCase					      =			evaluate(__lowerCAmelCase							)
								__lowerCamelCase					      =			None

				def format_log(__lowerCAmelCase			:		Any	,						__lowerCAmelCase			:		Tuple							):
								__lowerCamelCase					      =			'''| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '''.format(__lowerCAmelCase	,						__lowerCAmelCase	,						math.exp(__lowerCAmelCase							)							)
								return log_str

				__lowerCamelCase					      =			''''''
				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()





from ..utils import requires_backends


SCREAMING_SNAKE_CASE__     :    Union[str, Any]      							=					None

class      lowerCAmelCase__  :






		def __init__(	self	:							int							,    *SCREAMING_SNAKE_CASE__	:							Dict							,    **SCREAMING_SNAKE_CASE__	:							Tuple						)  ->							Optional[Any]:
						requires_backends(self							,    ['''detectron2''']						)







		@classmethod
		def 				__A (	cls	:							Dict							,    *SCREAMING_SNAKE_CASE__	:							Dict							,    **SCREAMING_SNAKE_CASE__	:							Tuple						)  ->							Tuple:
						requires_backends(cls							,    ['''detectron2''']						)



