
'''simple docstring'''


from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments




def lowercase_		(				)       ->							str:
					'''simple docstring'''

					lowerCamelCase_   : Tuple       =						HfArgumentParser(_lowercase			)
					try:
										lowerCamelCase_   : str       =						parser.parse_args_into_dataclasses()[0]
					except ValueError as e:
										lowerCamelCase_   : Dict       =						'''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
										lowerCamelCase_   : List[str]       =						''' '''.join(str(_lowercase			).split(''' '''			)[:-1]			)
										lowerCamelCase_   : List[Any]       =						''''''
										lowerCamelCase_   : Optional[Any]       =						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_   : List[str]       =						full_error_msg + begin_error_msg + str(_lowercase			)
										raise ValueError(_lowercase			)

					lowerCamelCase_   : Union[str, Any]       =						PyTorchBenchmark(args=_lowercase			)
					benchmark.run()


if __name__ == "__main__":
					main()




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




def lowercase_		(				)       ->							Union[str, Any]:
					'''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=1_600     ,    help='''length of the retained previous heads'''			)
					parser.add_argument('''--clamp_len'''     ,    type=_lowercase     ,    default=1_000     ,    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_   : Dict       =						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_   : List[str]       =						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_   : Optional[int]       =						corpus.get_iterator('''valid'''     ,    args.batch_size     ,    args.tgt_len     ,    device=_lowercase     ,    ext_len=args.ext_len			)
					lowerCamelCase_   : str       =						corpus.get_iterator('''test'''     ,    args.batch_size     ,    args.tgt_len     ,    device=_lowercase     ,    ext_len=args.ext_len			)

					# Load a pre-trained model
					lowerCamelCase_   : 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_   : Union[str, Any]       =						args.clamp_len
					if args.same_length:
										lowerCamelCase_   : int       =						True

					###############################################################################
					# Evaluation code
					###############################################################################
					def evaluate(_lowercase			):
										# Turn on evaluation mode which disables dropout.
										model.eval()
										lowerCamelCase_, lowerCamelCase_   : Union[str, Any]       =						0, 0.0
										lowerCamelCase_   : Optional[int]       =						time.time()
										with torch.no_grad():
															lowerCamelCase_   : Dict       =						None
															for idx, (data, target, seq_len) in enumerate(_lowercase			):
																				lowerCamelCase_   : str       =						model(_lowercase     ,    lm_labels=_lowercase     ,    mems=_lowercase			)
																				lowerCamelCase_, lowerCamelCase_, lowerCamelCase_   : int       =						ret
																				lowerCamelCase_   : List[str]       =						loss.mean()
																				total_loss += seq_len * loss.item()
																				total_len += seq_len
															lowerCamelCase_   : List[str]       =						time.time() - start_time
										logger.info('''Time : {:.2f}s, {:.2f}ms/segment'''.format(_lowercase     ,    1_000 * total_time / (idx + 1)			)			)
										return total_loss / total_len

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

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

					lowerCamelCase_   : List[Any]       =						''''''
					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()


