




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    snake_case   (				)				->				Dict:




	'''simple docstring'''


	lowerCamelCase_				:						Dict				  =				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=1600		,  help='length of the retained previous heads'     )
	parser.add_argument('--clamp_len'		,  type=A		,  default=1000		,  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.'     )
	lowerCamelCase_				:						str				  =				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()

	lowerCamelCase_				:						Tuple				  =				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 )
	lowerCamelCase_				:						List[Any]				  =				TransfoXLCorpus.from_pretrained(args.model_name     )

	lowerCamelCase_				:						List[Any]				  =				corpus.get_iterator('valid'		,  args.batch_size		,  args.tgt_len		,  device=A		,  ext_len=args.ext_len     )
	lowerCamelCase_				:						Tuple				  =				corpus.get_iterator('test'		,  args.batch_size		,  args.tgt_len		,  device=A		,  ext_len=args.ext_len     )

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

	###############################################################################
	# Evaluation code
	###############################################################################
	def evaluate(A     ):
		# Turn on evaluation mode which disables dropout.
		model.eval()
		lowerCamelCase_  ,     lowerCamelCase_				:						Optional[int]				  =				0, 0.0
		lowerCamelCase_				:						str				  =				time.time()
		with torch.no_grad():
			lowerCamelCase_				:						int				  =				None
			for idx, (data, target, seq_len) in enumerate(A     ):
				lowerCamelCase_				:						int				  =				model(A		,  lm_labels=A		,  mems=A     )
				lowerCamelCase_  ,     lowerCamelCase_  ,     lowerCamelCase_				:						Optional[Any]				  =				ret
				lowerCamelCase_				:						Optional[int]				  =				loss.mean()
				total_loss += seq_len * loss.item()
				total_len += seq_len
			lowerCamelCase_				:						Tuple				  =				time.time() - start_time
		logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(A		,  1000 * total_time / (idx + 1)     )     )
		return total_loss / total_len

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

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

	lowerCamelCase_				:						Any				  =				''
	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()






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'     )
