





import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed



class       A__     ( __snake_case    ):





      def __init__(				self ,							A_ ,							A_ ,							A_=1024 ,							A_=1024 ,							A_=3.6     ):
       '''simple docstring'''




       UpperCamelCase			:      List[Any] 			=     tokenizer
       UpperCamelCase			:      Optional[int] 			=     tokenizer.bos_token_id
       UpperCamelCase			:      Optional[int] 			=     dataset
       UpperCamelCase			:      Optional[int] 			=     seq_length
       UpperCamelCase			:      List[str] 			=     seq_length * chars_per_token * num_of_sequences





      def __iter__(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      List[str] 			=     iter(self.dataset     )
       UpperCamelCase			:      Union[str, Any] 			=     True
       while more_examples:
        UpperCamelCase			,					UpperCamelCase			:      Union[str, Any] 			=     [], 0
        while True:
         if buffer_len >= self.input_characters:
          break
         try:
          buffer.append(next(A_     )["content"]     )
          buffer_len += len(buffer[-1]     )
         except StopIteration:
          UpperCamelCase			:      Optional[int] 			=     False
          break
        UpperCamelCase			:      Dict 			=     tokenizer(A_ ,							truncation=A_     )["input_ids"]
        UpperCamelCase			:      List[str] 			=     []
        for tokenized_input in tokenized_inputs:
         all_token_ids.extend(tokenized_input + [self.concat_token_id]     )
        for i in range(0 ,							len(A_     ) ,							self.seq_length     ):
         UpperCamelCase			:      Optional[Any] 			=     all_token_ids[i : i + self.seq_length]
         if len(A_     ) == self.seq_length:
          yield torch.tensor(A_     )



def 	A_       (  _lowerCAmelCase  )		->				Optional[int]:
 UpperCamelCase			:      Union[str, Any] 			=     {"streaming": True}
 UpperCamelCase			:      Optional[Any] 			=     load_dataset(args.dataset_name							,     split="train"							,     **_lowerCAmelCase  )
 UpperCamelCase			:      List[str] 			=     ConstantLengthDataset(_lowerCAmelCase							,     _lowerCAmelCase							,     seq_length=args.seq_length  )
 UpperCamelCase			:      List[str] 			=     DataLoader(_lowerCAmelCase							,     batch_size=args.batch_size  )
 return eval_dataloader



def 	A_       (  _lowerCAmelCase  )		->				Optional[Any]:
 model.eval()
 UpperCamelCase			:      Any 			=     []
 for step, batch in enumerate(_lowerCAmelCase  ):
  with torch.no_grad():
   UpperCamelCase			:      str 			=     model(_lowerCAmelCase							,     labels=_lowerCAmelCase  )
  UpperCamelCase			:      Dict 			=     outputs.loss.repeat(args.batch_size  )
  losses.append(accelerator.gather(_lowerCAmelCase  )  )

  if args.max_eval_steps > 0 and step >= args.max_eval_steps:
   break
 UpperCamelCase			:      Optional[int] 			=     torch.mean(torch.cat(_lowerCAmelCase  )  )
 try:
  UpperCamelCase			:      int 			=     torch.exp(_lowerCAmelCase  )
 except OverflowError:
  UpperCamelCase			:      Optional[Any] 			=     float("inf"  )
 return loss.item(), perplexity.item()


# Setup Accelerator
__lowerCamelCase :  Tuple										=  Accelerator()

# Parse configuration
__lowerCamelCase :  Tuple										=  HfArgumentParser(EvaluationArguments)
__lowerCamelCase :  List[str]										=  parser.parse_args()
set_seed(args.seed)

# Logging
__lowerCamelCase :  Union[str, Any]										=  logging.getLogger(__name__)
logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)

# Load model and tokenizer
__lowerCamelCase :  Optional[Any]										=  AutoModelForCausalLM.from_pretrained(args.model_ckpt)
__lowerCamelCase :  Any										=  AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
__lowerCamelCase :  Union[str, Any]										=  create_dataloader(args)

# Prepare everything with our `accelerator`.
__lowerCamelCase						,			__lowerCamelCase :  Any										=  accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
__lowerCamelCase						,			__lowerCamelCase :  int										=  evaluate(args)


logger.info(f"""loss/eval: {eval_loss}, perplexity: {perplexity}""")







from __future__ import annotations

__lowerCamelCase :  Optional[int]										=  """Muhammad Umer Farooq"""
__lowerCamelCase :  Tuple										=  """MIT"""
__lowerCamelCase :  Optional[int]										=  """1.0.0"""
__lowerCamelCase :  int										=  """Muhammad Umer Farooq"""
__lowerCamelCase :  Optional[int]										=  """contact@muhammadumerfarooq.me"""
__lowerCamelCase :  Dict										=  """Alpha"""

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

import requests



class       A__     ( __snake_case    ):





      def __init__(				self ,							A_     ):
       '''simple docstring'''




       super().__init__()
       UpperCamelCase			:      list[str] 			=     []
       UpperCamelCase			:      str 			=     domain





      def  __UpperCamelCase(				self ,							A_ ,							A_     ):
       '''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:
           UpperCamelCase			:      Any 			=     parse.urljoin(self.domain ,							A_     )
           self.urls.append(A_     )



def 	A_       (  _lowerCAmelCase  )		->				str:
 return ".".join(get_sub_domain_name(_lowerCAmelCase  ).split("."  )[-2:]  )



def 	A_       (  _lowerCAmelCase  )		->				str:
 return parse.urlparse(_lowerCAmelCase  ).netloc



def 	A_       (  _lowerCAmelCase = "https://github.com"  )		->				list[str]:
 UpperCamelCase			:      int 			=     get_domain_name(_lowerCAmelCase  )

 # Initialize the parser
 UpperCamelCase			:      str 			=     Parser(_lowerCAmelCase  )

 try:
  # Open URL
  UpperCamelCase			:      int 			=     requests.get(_lowerCAmelCase  )

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

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

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


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