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
)
_SCREAMING_SNAKE_CASE     =     logging.getLogger(__name__)
def 					__lowercase						(    )					->Dict:

  '''simple docstring'''




  a__  		=  argparse.ArgumentParser(description="PyTorch Transformer Language Model"       )
  parser.add_argument("--model_name"			,					type=lowerCamelCase__			,					default="transfo-xl-wt103"			,					help="pretrained model name"       )
  parser.add_argument(
      "--split"			,					type=lowerCamelCase__			,					default="test"			,					choices=["all", "valid", "test"]			,					help="which split to evaluate"       )
  parser.add_argument("--batch_size"			,					type=lowerCamelCase__			,					default=10			,					help="batch size"       )
  parser.add_argument("--tgt_len"			,					type=lowerCamelCase__			,					default=1_28			,					help="number of tokens to predict"       )
  parser.add_argument("--ext_len"			,					type=lowerCamelCase__			,					default=0			,					help="length of the extended context"       )
  parser.add_argument("--mem_len"			,					type=lowerCamelCase__			,					default=16_00			,					help="length of the retained previous heads"       )
  parser.add_argument("--clamp_len"			,					type=lowerCamelCase__			,					default=10_00			,					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=lowerCamelCase__			,					required=lowerCamelCase__			,					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=lowerCamelCase__			,					default=""			,					help="Can be used for distant debugging."       )
  parser.add_argument("--server_port"			,					type=lowerCamelCase__			,					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=lowerCamelCase__       )
    ptvsd.wait_for_attach()

  a__  		=  torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"       )
  logger.info("device: {}".format(lowerCamelCase__       )       )

  # 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=lowerCamelCase__			,					ext_len=args.ext_len       )
  a__  		=  corpus.get_iterator("test"			,					args.batch_size			,					args.tgt_len			,					device=lowerCamelCase__			,					ext_len=args.ext_len       )

  # Load a pre-trained model
  a__  		=  TransfoXLLMHeadModel.from_pretrained(args.model_name       )
  model.to(lowerCamelCase__       )

  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(lowerCamelCase__       ):
    # Turn on evaluation mode which disables dropout.
    model.eval()
    a__  		=  0, 0.0
    a__  		=  time.time()
    with torch.no_grad():
      a__  		=  None
      for idx, (data, target, seq_len) in enumerate(lowerCamelCase__       ):
        a__  		=  model(lowerCamelCase__			,					lm_labels=lowerCamelCase__			,					mems=lowerCamelCase__       )
        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(lowerCamelCase__			,					10_00 * total_time / (idx + 1)       )       )
    return total_loss / total_len

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

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

  a__  		=  ''
  if valid_loss is not None:
    log_str += format_log(lowerCamelCase__			,					"valid"       )
  if test_loss is not None:
    log_str += format_log(lowerCamelCase__			,					"test"       )

  logger.info("=" * 1_00       )
  logger.info(lowerCamelCase__       )
  logger.info("=" * 1_00       )


if __name__ == "__main__":
     main()







import requests

try:
							from django.contrib.auth import authenticate, login
							from django.shortcuts import redirect, render
except ImportError:
							UpperCamelCase__						=		UpperCamelCase__						=		UpperCamelCase__						=		UpperCamelCase__						=		print






def    snake_case   (				A     )				->				List[str]:




	'''simple docstring'''


	lowerCamelCase_				:						Optional[int]				  =				'secretKey'
	lowerCamelCase_				:						str				  =				'https://www.google.com/recaptcha/api/siteverify'

	# when method is not POST, direct user to login page
	if request.method != "POST":
		return render(A		,  'login.html'     )

	# from the frontend, get username, password, and client_key
	lowerCamelCase_				:						int				  =				request.POST.get('username'     )
	lowerCamelCase_				:						str				  =				request.POST.get('password'     )
	lowerCamelCase_				:						List[Any]				  =				request.POST.get('g-recaptcha-response'     )

	# post recaptcha response to Google's recaptcha api
	lowerCamelCase_				:						Union[str, Any]				  =				requests.post(A		,  data={'secret': secret_key, 'response': client_key}     )
	# if the recaptcha api verified our keys
	if response.json().get('success'		,  A     ):
		# authenticate the user
		lowerCamelCase_				:						int				  =				authenticate(A		,  username=A		,  password=A     )
		if user_in_database:
			login(A		,  A     )
			return redirect('/your-webpage'     )
	return render(A		,  'login.html'     )
