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



def 				__a       (			):







					"""simple docstring"""





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

					lowerCamelCase__		:   Optional[int]  				=					torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''						)
					logger.info('''device: {}'''.format(_lowercase						)						)

					# 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__		:   str  				=					TransfoXLCorpus.from_pretrained(args.model_name						)

					lowerCamelCase__		:   Dict  				=					corpus.get_iterator('''valid'''							, args.batch_size							, args.tgt_len							, device=_lowercase							, ext_len=args.ext_len						)
					lowerCamelCase__		:   Optional[int]  				=					corpus.get_iterator('''test'''							, args.batch_size							, args.tgt_len							, device=_lowercase							, ext_len=args.ext_len						)

					# Load a pre-trained model
					lowerCamelCase__		:   List[str]  				=					TransfoXLLMHeadModel.from_pretrained(args.model_name						)
					model.to(_lowercase						)

					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__		:   Dict  				=					args.clamp_len
					if args.same_length:
										lowerCamelCase__		:   Optional[int]  				=					True

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

					# Run on test data.
					if args.split == "all":
										lowerCamelCase__		:   Any  				=					evaluate(_lowercase						)
										lowerCamelCase__		:   Any  				=					evaluate(_lowercase						)
					elif args.split == "valid":
										lowerCamelCase__		:   Optional[Any]  				=					evaluate(_lowercase						)
										lowerCamelCase__		:   Union[str, Any]  				=					None
					elif args.split == "test":
										lowerCamelCase__		:   Tuple  				=					evaluate(_lowercase						)
										lowerCamelCase__		:   Tuple  				=					None

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

					lowerCamelCase__		:   List[str]  				=					''''''
					if valid_loss is not None:
										log_str += format_log(_lowercase							, '''valid'''						)
					if test_loss is not None:
										log_str += format_log(_lowercase							, '''test'''						)

					logger.info('''=''' * 100						)
					logger.info(_lowercase						)
					logger.info('''=''' * 100						)


if __name__ == "__main__":
					main()







"""simple docstring"""




from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments



def 				__a       (			):







					"""simple docstring"""





					lowerCamelCase__		:   Union[str, Any]  				=					HfArgumentParser(_lowercase						)
					try:
										lowerCamelCase__		:   Optional[int]  				=					parser.parse_args_into_dataclasses()[0]
					except ValueError as e:
										lowerCamelCase__		:   Tuple  				=					'''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
										lowerCamelCase__		:   Tuple  				=					''' '''.join(str(_lowercase						).split(''' '''						)[:-1]						)
										lowerCamelCase__		:   Union[str, Any]  				=					''''''
										lowerCamelCase__		:   int  				=					eval(str(_lowercase						).split(''' '''						)[-1]						)
										lowerCamelCase__		:   Any  				=					[]
										for arg in depreciated_args:
															# arg[2:] removes '--'
															if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
																				# arg[5:] removes '--no_'
																				full_error_msg += arg_error_msg.format(arg[5:]						)
															else:
																				wrong_args.append(_lowercase						)
										if len(_lowercase						) > 0:
															lowerCamelCase__		:   Any  				=					full_error_msg + begin_error_msg + str(_lowercase						)
										raise ValueError(_lowercase						)

					lowerCamelCase__		:   List[Any]  				=					PyTorchBenchmark(args=_lowercase						)
					benchmark.run()


if __name__ == "__main__":
					main()






