






'''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_					=				logging.getLogger(__name__)






def  lowerCAmelCase__		(					)   -> Optional[Any]:
       UpperCAmelCase__	:   Union[str, Any]												=	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=1_0			,						help='''batch size'''			)
       parser.add_argument('''--tgt_len'''			,						type=a_			,						default=1_2_8			,						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=1_6_0_0			,						help='''length of the retained previous heads'''			)
       parser.add_argument('''--clamp_len'''			,						type=a_			,						default=1_0_0_0			,						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__	:   Union[str, Any]												=	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__	:   Union[str, Any]												=	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__	:   Any												=	TransfoXLCorpus.from_pretrained(args.model_name			)

       UpperCAmelCase__	:   Optional[int]												=	corpus.get_iterator('''valid'''			,						args.batch_size			,						args.tgt_len			,						device=a_			,						ext_len=args.ext_len			)
       UpperCAmelCase__	:   List[Any]												=	corpus.get_iterator('''test'''			,						args.batch_size			,						args.tgt_len			,						device=a_			,						ext_len=args.ext_len			)

       # Load a pre-trained model
       UpperCAmelCase__	:   Any												=	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__	:   Any												=	args.clamp_len
       if args.same_length:
              UpperCAmelCase__	:   Optional[Any]												=	True

       ###############################################################################
       # Evaluation code
       ###############################################################################
       def evaluate(a_  :     Optional[int]			):
              # Turn on evaluation mode which disables dropout.
              model.eval()
              UpperCAmelCase__							,							UpperCAmelCase__	:   Optional[Any]												=	0, 0.0
              UpperCAmelCase__	:   Union[str, Any]												=	time.time()
              with torch.no_grad():
                     UpperCAmelCase__	:   Union[str, Any]												=	None
                     for idx, (data, target, seq_len) in enumerate(a_			):
                            UpperCAmelCase__	:   Tuple												=	model(a_			,						lm_labels=a_			,						mems=a_			)
                            UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:   List[Any]												=	ret
                            UpperCAmelCase__	:   Any												=	loss.mean()
                            total_loss += seq_len * loss.item()
                            total_len += seq_len
                     UpperCAmelCase__	:   Dict												=	time.time() - start_time
              logger.info('''Time : {:.2f}s, {:.2f}ms/segment'''.format(a_			,						1_0_0_0 * total_time / (idx + 1)			)			)
              return total_loss / total_len

       # Run on test data.
       if args.split == "all":
              UpperCAmelCase__	:   Union[str, Any]												=	evaluate(a_			)
              UpperCAmelCase__	:   int												=	evaluate(a_			)
       elif args.split == "valid":
              UpperCAmelCase__	:   Union[str, Any]												=	evaluate(a_			)
              UpperCAmelCase__	:   int												=	None
       elif args.split == "test":
              UpperCAmelCase__	:   str												=	evaluate(a_			)
              UpperCAmelCase__	:   Dict												=	None

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

       UpperCAmelCase__	:   List[str]												=	''''''
       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('''=''' * 1_0_0			)
       logger.info(a_			)
       logger.info('''=''' * 1_0_0			)


if __name__ == "__main__":
  main()







'''simple docstring'''







import re






def  lowerCAmelCase__		(					a_  :     Optional[int]			)   -> Optional[Any]:
       def docstring_decorator(a_  :     Dict			):
              UpperCAmelCase__	:   Optional[int]												=	fn.__doc__
              UpperCAmelCase__	:   Dict												=	func_doc.split('''\n'''			)
              UpperCAmelCase__	:   Any												=	0
              while i < len(a_			) and re.search(r'''^\s*Examples?:\s*$'''			,						lines[i]			) is None:
                     i += 1
              if i < len(a_			):
                     UpperCAmelCase__	:   List[Any]												=	example_docstring
                     UpperCAmelCase__	:   int												=	'''\n'''.join(a_			)
              else:
                     raise ValueError(
                         f"""The function {fn} should have an empty 'Examples:' in its docstring as placeholder, """
                         f"""current docstring is:\n{func_doc}"""			)
              UpperCAmelCase__	:   Any												=	func_doc
              return fn

       return docstring_decorator