






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 		_a					(   )							->    str:
 a							=   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=10			,							help='''batch size'''				)
 parser.add_argument('''--tgt_len'''			,							type=a			,							default=128			,							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_600			,							help='''length of the retained previous heads'''				)
 parser.add_argument('''--clamp_len'''			,							type=a			,							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=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.'''				)
 a							=   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()

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

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

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

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

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

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

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


if __name__ == "__main__":
     main()










from collections import namedtuple

UpperCAmelCase__            =	namedtuple("from_to", "from_ to")

UpperCAmelCase__            =	{
    "atm": from_to(1, 1),
    "pascal": from_to(0.000_0098, 101325),
    "bar": from_to(0.98_6923, 1.0_1325),
    "kilopascal": from_to(0.0098_6923, 101.325),
    "megapascal": from_to(9.8_6923, 0.10_1325),
    "psi": from_to(0.06_8046, 14.6959),
    "inHg": from_to(0.033_4211, 29.9213),
    "torr": from_to(0.0013_1579, 760),
}
def 		_a					(   a				:float			,							a				:str			,							a				:str				)							->    float:
 if from_type not in PRESSURE_CONVERSION:
  raise ValueError(
      F"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
      + ''', '''.join(a				)				)
 if to_type not in PRESSURE_CONVERSION:
  raise ValueError(
      F"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
      + ''', '''.join(a				)				)
 return (
     value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
 )


if __name__ == "__main__":
     import doctest

     doctest.testmod()


