





import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
snake_case_           =      logging.getLogger(__name__)






def lowerCamelCase__   (							snake_case_     :  Optional[int]	,				snake_case_     :  Any	)				->    Tuple:
   __snake_case								=       np.argmax(snake_case_	,				axis=1	)
   return np.sum(outputs == labels	)






def lowerCamelCase__   (							snake_case_     :  Tuple	)				->    Any:
   with open(snake_case_	,				encoding='''utf_8'''	) as f:
      __snake_case								=       csv.reader(snake_case_	)
      __snake_case								=       []
      next(snake_case_	)  # skip the first line
      for line in tqdm(snake_case_	):
         output.append((''' '''.join(line[1:5]	), line[5], line[6], int(line[-1]	) - 1)	)
   return output






def lowerCamelCase__   (							snake_case_     :  Dict	,				snake_case_     :  Optional[Any]	,				snake_case_     :  Optional[int]	,				snake_case_     :  str	,				snake_case_     :  Optional[int]	,				snake_case_     :  int	)				->    Tuple:
   __snake_case								=       []
   for dataset in encoded_datasets:
      __snake_case								=       len(snake_case_	)
      __snake_case								=       np.zeros((n_batch, 2, input_len)	,				dtype=np.intaa	)
      __snake_case								=       np.zeros((n_batch, 2)	,				dtype=np.intaa	)
      __snake_case								=       np.full((n_batch, 2, input_len)	,				fill_value=-100	,				dtype=np.intaa	)
      __snake_case								=       np.zeros((n_batch,)	,				dtype=np.intaa	)
      for (
          i,
          (story, conta, conta, mc_label),
      ) in enumerate(snake_case_	):
         __snake_case								=       [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
         __snake_case								=       [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
         __snake_case								=       with_conta
         __snake_case								=       with_conta
         __snake_case								=       len(snake_case_	) - 1
         __snake_case								=       len(snake_case_	) - 1
         __snake_case								=       with_conta
         __snake_case								=       with_conta
         __snake_case								=       mc_label
      __snake_case								=       (input_ids, mc_token_ids, lm_labels, mc_labels)
      tensor_datasets.append(tuple(torch.tensor(snake_case_	) for t in all_inputs	)	)
   return tensor_datasets






def lowerCamelCase__   (							)				->    Dict:
   __snake_case								=       argparse.ArgumentParser()
   parser.add_argument('''--model_name'''	,				type=snake_case_	,				default='''openai-gpt'''	,				help='''pretrained model name'''	)
   parser.add_argument('''--do_train'''	,				action='''store_true'''	,				help='''Whether to run training.'''	)
   parser.add_argument('''--do_eval'''	,				action='''store_true'''	,				help='''Whether to run eval on the dev set.'''	)
   parser.add_argument(
       '''--output_dir'''	,				default=snake_case_	,				type=snake_case_	,				required=snake_case_	,				help='''The output directory where the model predictions and checkpoints will be written.'''	,				)
   parser.add_argument('''--train_dataset'''	,				type=snake_case_	,				default=''''''	)
   parser.add_argument('''--eval_dataset'''	,				type=snake_case_	,				default=''''''	)
   parser.add_argument('''--seed'''	,				type=snake_case_	,				default=42	)
   parser.add_argument('''--num_train_epochs'''	,				type=snake_case_	,				default=3	)
   parser.add_argument('''--train_batch_size'''	,				type=snake_case_	,				default=8	)
   parser.add_argument('''--eval_batch_size'''	,				type=snake_case_	,				default=16	)
   parser.add_argument('''--adam_epsilon'''	,				default=1e-8	,				type=snake_case_	,				help='''Epsilon for Adam optimizer.'''	)
   parser.add_argument('''--max_grad_norm'''	,				type=snake_case_	,				default=1	)
   parser.add_argument(
       '''--max_steps'''	,				default=-1	,				type=snake_case_	,				help=(
           '''If > 0: set total number of training                         steps to perform. Override num_train_epochs.'''
       )	,				)
   parser.add_argument(
       '''--gradient_accumulation_steps'''	,				type=snake_case_	,				default=1	,				help='''Number of updates steps to accumulate before                        performing a backward/update pass.'''	,				)
   parser.add_argument('''--learning_rate'''	,				type=snake_case_	,				default=6.25e-5	)
   parser.add_argument('''--warmup_steps'''	,				default=0	,				type=snake_case_	,				help='''Linear warmup over warmup_steps.'''	)
   parser.add_argument('''--lr_schedule'''	,				type=snake_case_	,				default='''warmup_linear'''	)
   parser.add_argument('''--weight_decay'''	,				type=snake_case_	,				default=0.01	)
   parser.add_argument('''--lm_coef'''	,				type=snake_case_	,				default=0.9	)
   parser.add_argument('''--n_valid'''	,				type=snake_case_	,				default=374	)

   parser.add_argument('''--server_ip'''	,				type=snake_case_	,				default=''''''	,				help='''Can be used for distant debugging.'''	)
   parser.add_argument('''--server_port'''	,				type=snake_case_	,				default=''''''	,				help='''Can be used for distant debugging.'''	)
   __snake_case								=       parser.parse_args()
   print(snake_case_	)

   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=snake_case_	)
      ptvsd.wait_for_attach()

   random.seed(args.seed	)
   np.random.seed(args.seed	)
   torch.manual_seed(args.seed	)
   torch.cuda.manual_seed_all(args.seed	)

   __snake_case								=       torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''	)
   __snake_case								=       torch.cuda.device_count()
   logger.info('''device: {}, n_gpu {}'''.format(snake_case_	,				snake_case_	)	)

   if not args.do_train and not args.do_eval:
      raise ValueError('''At least one of `do_train` or `do_eval` must be True.'''	)

   if not os.path.exists(args.output_dir	):
      os.makedirs(args.output_dir	)

   # Load tokenizer and model
   # This loading functions also add new tokens and embeddings called `special tokens`
   # These new embeddings will be fine-tuned on the RocStories dataset
   __snake_case								=       ['''_start_''', '''_delimiter_''', '''_classify_''']
   __snake_case								=       OpenAIGPTTokenizer.from_pretrained(args.model_name	)
   tokenizer.add_tokens(snake_case_	)
   __snake_case								=       tokenizer.convert_tokens_to_ids(snake_case_	)
   __snake_case								=       OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name	)
   model.resize_token_embeddings(len(snake_case_	)	)
   model.to(snake_case_	)

   # Load and encode the datasets
   def tokenize_and_encode(snake_case_     :  List[str]	):
      if isinstance(snake_case_	,				snake_case_	):
         return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(snake_case_	)	)
      elif isinstance(snake_case_	,				snake_case_	):
         return obj
      return [tokenize_and_encode(snake_case_	) for o in obj]

   logger.info('''Encoding dataset...'''	)
   __snake_case								=       load_rocstories_dataset(args.train_dataset	)
   __snake_case								=       load_rocstories_dataset(args.eval_dataset	)
   __snake_case								=       (train_dataset, eval_dataset)
   __snake_case								=       tokenize_and_encode(snake_case_	)

   # Compute the max input length for the Transformer
   __snake_case								=       model.config.n_positions // 2 - 2
   __snake_case								=       max(
       len(story[:max_length]	) + max(len(conta[:max_length]	)	,				len(conta[:max_length]	)	) + 3
       for dataset in encoded_datasets
       for story, conta, conta, _ in dataset	)
   __snake_case								=       min(snake_case_	,				model.config.n_positions	)  # Max size of input for the pre-trained model

   # Prepare inputs tensors and dataloaders
   __snake_case								=       pre_process_datasets(snake_case_	,				snake_case_	,				snake_case_	,				*snake_case_	)
   __snake_case	,     __snake_case								=       tensor_datasets[0], tensor_datasets[1]

   __snake_case								=       TensorDataset(*snake_case_	)
   __snake_case								=       RandomSampler(snake_case_	)
   __snake_case								=       DataLoader(snake_case_	,				sampler=snake_case_	,				batch_size=args.train_batch_size	)

   __snake_case								=       TensorDataset(*snake_case_	)
   __snake_case								=       SequentialSampler(snake_case_	)
   __snake_case								=       DataLoader(snake_case_	,				sampler=snake_case_	,				batch_size=args.eval_batch_size	)

   # Prepare optimizer
   if args.do_train:
      if args.max_steps > 0:
         __snake_case								=       args.max_steps
         __snake_case								=       args.max_steps // (len(snake_case_	) // args.gradient_accumulation_steps) + 1
      else:
         __snake_case								=       len(snake_case_	) // args.gradient_accumulation_steps * args.num_train_epochs

      __snake_case								=       list(model.named_parameters()	)
      __snake_case								=       ['''bias''', '''LayerNorm.bias''', '''LayerNorm.weight''']
      __snake_case								=       [
          {
              '''params''': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay	)],
              '''weight_decay''': args.weight_decay,
          },
          {'''params''': [p for n, p in param_optimizer if any(nd in n for nd in no_decay	)], '''weight_decay''': 0.0},
      ]
      __snake_case								=       AdamW(snake_case_	,				lr=args.learning_rate	,				eps=args.adam_epsilon	)
      __snake_case								=       get_linear_schedule_with_warmup(
          snake_case_	,				num_warmup_steps=args.warmup_steps	,				num_training_steps=snake_case_	)

   if args.do_train:
      __snake_case	,     __snake_case	,     __snake_case								=       0, 0, None
      model.train()
      for _ in trange(int(args.num_train_epochs	)	,				desc='''Epoch'''	):
         __snake_case								=       0
         __snake_case								=       0
         __snake_case								=       tqdm(snake_case_	,				desc='''Training'''	)
         for step, batch in enumerate(snake_case_	):
            __snake_case								=       tuple(t.to(snake_case_	) for t in batch	)
            __snake_case	,     __snake_case	,     __snake_case	,     __snake_case								=       batch
            __snake_case								=       model(snake_case_	,				mc_token_ids=snake_case_	,				lm_labels=snake_case_	,				mc_labels=snake_case_	)
            __snake_case								=       args.lm_coef * losses[0] + losses[1]
            loss.backward()
            optimizer.step()
            scheduler.step()
            optimizer.zero_grad()
            tr_loss += loss.item()
            __snake_case								=       (
                loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
            )
            nb_tr_steps += 1
            __snake_case								=       '''Training loss: {:.2e} lr: {:.2e}'''.format(snake_case_	,				scheduler.get_lr()[0]	)

    # Save a trained model
   if args.do_train:
      # Save a trained model, configuration and tokenizer
      __snake_case								=       model.module if hasattr(snake_case_	,				'''module'''	) else model  # Only save the model itself

      # If we save using the predefined names, we can load using `from_pretrained`
      __snake_case								=       os.path.join(args.output_dir	,				snake_case_	)
      __snake_case								=       os.path.join(args.output_dir	,				snake_case_	)

      torch.save(model_to_save.state_dict()	,				snake_case_	)
      model_to_save.config.to_json_file(snake_case_	)
      tokenizer.save_vocabulary(args.output_dir	)

      # Load a trained model and vocabulary that you have fine-tuned
      __snake_case								=       OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir	)
      __snake_case								=       OpenAIGPTTokenizer.from_pretrained(args.output_dir	)
      model.to(snake_case_	)

   if args.do_eval:
      model.eval()
      __snake_case	,     __snake_case								=       0, 0
      __snake_case	,     __snake_case								=       0, 0
      for batch in tqdm(snake_case_	,				desc='''Evaluating'''	):
         __snake_case								=       tuple(t.to(snake_case_	) for t in batch	)
         __snake_case	,     __snake_case	,     __snake_case	,     __snake_case								=       batch
         with torch.no_grad():
            __snake_case	,     __snake_case	,     __snake_case	,     __snake_case								=       model(
                snake_case_	,				mc_token_ids=snake_case_	,				lm_labels=snake_case_	,				mc_labels=snake_case_	)

         __snake_case								=       mc_logits.detach().cpu().numpy()
         __snake_case								=       mc_labels.to('''cpu'''	).numpy()
         __snake_case								=       accuracy(snake_case_	,				snake_case_	)

         eval_loss += mc_loss.mean().item()
         eval_accuracy += tmp_eval_accuracy

         nb_eval_examples += input_ids.size(0	)
         nb_eval_steps += 1

      __snake_case								=       eval_loss / nb_eval_steps
      __snake_case								=       eval_accuracy / nb_eval_examples
      __snake_case								=       tr_loss / nb_tr_steps if args.do_train else None
      __snake_case								=       {'''eval_loss''': eval_loss, '''eval_accuracy''': eval_accuracy, '''train_loss''': train_loss}

      __snake_case								=       os.path.join(args.output_dir	,				'''eval_results.txt'''	)
      with open(snake_case_	,				'''w'''	) as writer:
         logger.info('''***** Eval results *****'''	)
         for key in sorted(result.keys()	):
            logger.info('''  %s = %s'''	,				snake_case_	,				str(result[key]	)	)
            writer.write('''%s = %s\n''' % (key, str(result[key]	))	)


if __name__ == "__main__":
    main()








from __future__ import annotations

snake_case_           =      'Muhammad Umer Farooq'
snake_case_           =      'MIT'
snake_case_           =      '1.0.0'
snake_case_           =      'Muhammad Umer Farooq'
snake_case_           =      'contact@muhammadumerfarooq.me'
snake_case_           =      'Alpha'

import re
from html.parser import HTMLParser
from urllib import parse

import requests

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):


      def __init__(self	:				Dict    ,      a__	:				str				):





         """simple docstring"""



         super().__init__()
         __snake_case								=       []
         __snake_case								=       domain


      def        a			(self	:				Tuple    ,      a__	:				str    ,      a__	:				list[tuple[str, str | None]]				):





         """simple docstring"""



         if tag == "a":
            # Check the list of defined attributes.
            for name, value in attrs:
               # If href is defined, and not empty nor # print it.
               if name == "href" and value != "#" and value != "":
                  # If not already in urls.
                  if value not in self.urls:
                     __snake_case								=       parse.urljoin(self.domain    ,      a__				)
                     self.urls.append(a__				)






def lowerCamelCase__   (							snake_case_     :  str	)				->    str:
   return ".".join(get_sub_domain_name(snake_case_	).split('''.'''	)[-2:]	)






def lowerCamelCase__   (							snake_case_     :  str	)				->    str:
   return parse.urlparse(snake_case_	).netloc






def lowerCamelCase__   (							snake_case_     :  str = "https://github.com"	)				->    list[str]:
   __snake_case								=       get_domain_name(snake_case_	)

   # Initialize the parser
   __snake_case								=       Parser(snake_case_	)

   try:
      # Open URL
      __snake_case								=       requests.get(snake_case_	)

      # pass the raw HTML to the parser to get links
      parser.feed(r.text	)

      # Get links and loop through
      __snake_case								=       set()
      for link in parser.urls:
         # open URL.
         # read = requests.get(link)
         try:
            __snake_case								=       requests.get(snake_case_	)
            # Get the valid email.
            __snake_case								=       re.findall('''[a-zA-Z0-9]+@''' + domain	,				read.text	)
            # If not in list then append it.
            for email in emails:
               valid_emails.add(snake_case_	)
         except ValueError:
            pass
   except ValueError:
      raise SystemExit(1	)

   # Finally return a sorted list of email addresses with no duplicates.
   return sorted(snake_case_	)


if __name__ == "__main__":
    snake_case_           =      emails_from_url('https://github.com')
    print(F'{len(emails)} emails found:')
    print('\n'.join(sorted(emails)))

