




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 		_lowerCamelCase				(					)			->		int:





  """simple docstring"""





  _lowercase: Tuple						     =    argparse.ArgumentParser(description='PyTorch Transformer Language Model'							)
  parser.add_argument('--model_name'					,     type=UpperCamelCase_					,     default='transfo-xl-wt103'					,     help='pretrained model name'							)
  parser.add_argument(
      '--split'					,     type=UpperCamelCase_					,     default='test'					,     choices=['all', 'valid', 'test']					,     help='which split to evaluate'							)
  parser.add_argument('--batch_size'					,     type=UpperCamelCase_					,     default=10					,     help='batch size'							)
  parser.add_argument('--tgt_len'					,     type=UpperCamelCase_					,     default=128					,     help='number of tokens to predict'							)
  parser.add_argument('--ext_len'					,     type=UpperCamelCase_					,     default=0					,     help='length of the extended context'							)
  parser.add_argument('--mem_len'					,     type=UpperCamelCase_					,     default=1_600					,     help='length of the retained previous heads'							)
  parser.add_argument('--clamp_len'					,     type=UpperCamelCase_					,     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=UpperCamelCase_					,     required=UpperCamelCase_					,     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=UpperCamelCase_					,     default=''					,     help='Can be used for distant debugging.'							)
  parser.add_argument('--server_port'					,     type=UpperCamelCase_					,     default=''					,     help='Can be used for distant debugging.'							)
  _lowercase: List[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=UpperCamelCase_							)
    ptvsd.wait_for_attach()

  _lowercase: str						     =    torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'							)
  logger.info('device: {}'.format(UpperCamelCase_							)							)

  # 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 )
  _lowercase: Optional[int]						     =    TransfoXLCorpus.from_pretrained(args.model_name							)

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

  # Load a pre-trained model
  _lowercase: Union[str, Any]						     =    TransfoXLLMHeadModel.from_pretrained(args.model_name							)
  model.to(UpperCamelCase_							)

  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:
    _lowercase: Optional[Any]						     =    args.clamp_len
  if args.same_length:
    _lowercase: Tuple						     =    True

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

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

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

  _lowercase: Optional[Any]						     =    ''
  if valid_loss is not None:
    log_str += format_log(UpperCamelCase_					,     'valid'							)
  if test_loss is not None:
    log_str += format_log(UpperCamelCase_					,     'test'							)

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


if __name__ == "__main__":
   main()






from __future__ import annotations

from pathlib import Path


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		int:





  """simple docstring"""





  return pointa[0] * pointa[1] - pointa[1] * pointa[0]


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		bool:





  """simple docstring"""





  _lowercase: tuple[int, int]						     =    (xa, ya)
  _lowercase: tuple[int, int]						     =    (xa - xa, ya - ya)
  _lowercase: tuple[int, int]						     =    (xa - xa, ya - ya)
  _lowercase: float						     =    -vector_product(UpperCamelCase					,     UpperCamelCase							) / vector_product(
      UpperCamelCase					,     UpperCamelCase							)
  _lowercase: float						     =    +vector_product(UpperCamelCase					,     UpperCamelCase							) / vector_product(
      UpperCamelCase					,     UpperCamelCase							)

  return a > 0 and b > 0 and a + b < 1


def 		_lowerCamelCase				(					UpperCamelCase = "p102_triangles.txt"							)			->		int:





  """simple docstring"""





  _lowercase: str						     =    Path(UpperCamelCase							).parent.joinpath(UpperCamelCase							).read_text(encoding='utf-8'							)

  _lowercase: list[list[int]]						     =    []
  for line in data.strip().split('\n'							):
    triangles.append([int(UpperCamelCase							) for number in line.split(','							)]							)

  _lowercase: int						     =    0
  _lowercase: list[int]

  for triangle in triangles:
    ret += contains_origin(*UpperCamelCase							)

  return ret


if __name__ == "__main__":
   print(f'''{solution() = }''')
