
from collections import namedtuple

import requests
from lxml import html  # type: ignore

_SCREAMING_SNAKE_CASE    :      Union[str, Any]          =			namedtuple('covid_data', 'cases deaths recovered')

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						str = "https://www.worldometers.info/coronavirus/"       ) ->       covid_data:

				"""simple docstring"""







				A__  :	str         =							'''//div[@class = "maincounter-number"]/span/text()'''
				return covid_data(*html.fromstring(requests.get(__UpperCamelCase       ).content       ).xpath(__UpperCamelCase       )       )


_SCREAMING_SNAKE_CASE    :      Tuple          =			'Total COVID-19 cases in the world: {}\nTotal deaths due to COVID-19 in the world: {}\nTotal COVID-19 patients recovered in the world: {}'



print(fmt.format(*covid_stats()))

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  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''





	def __init__(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__=1024				,       UpperCamelCase__=1024				,       UpperCamelCase__=3.6		):
					A__  :	str         =							tokenizer
					A__  :	int         =							tokenizer.bos_token_id
					A__  :	List[Any]         =							dataset
					A__  :	Tuple         =							seq_length
					A__  :	Any         =							seq_length * chars_per_token * num_of_sequences





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




def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[Any]       ) ->       Any:

				"""simple docstring"""







				A__  :	Any         =							{'''streaming''': True}
				A__  :	List[str]         =							load_dataset(args.dataset_name							,						split='''train'''							,						**__UpperCamelCase       )
				A__  :	List[str]         =							ConstantLengthDataset(__UpperCamelCase							,						__UpperCamelCase							,						seq_length=args.seq_length       )
				A__  :	int         =							DataLoader(__UpperCamelCase							,						batch_size=args.batch_size       )
				return eval_dataloader

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]       ) ->       Dict:

				"""simple docstring"""







				model.eval()
				A__  :	Dict         =							[]
				for step, batch in enumerate(__UpperCamelCase       ):
								with torch.no_grad():
												A__  :	Any         =							model(__UpperCamelCase							,						labels=__UpperCamelCase       )
								A__  :	Tuple         =							outputs.loss.repeat(args.batch_size       )
								losses.append(accelerator.gather(__UpperCamelCase       )       )

								if args.max_eval_steps > 0 and step >= args.max_eval_steps:
												break
				A__  :	Tuple         =							torch.mean(torch.cat(__UpperCamelCase       )       )
				try:
								A__  :	Optional[Any]         =							torch.exp(__UpperCamelCase       )
				except OverflowError:
								A__  :	Union[str, Any]         =							float('''inf'''       )
				return loss.item(), perplexity.item()


# Setup Accelerator
_SCREAMING_SNAKE_CASE    :      List[Any]          =			Accelerator()

# Parse configuration
_SCREAMING_SNAKE_CASE    :      Optional[int]          =			HfArgumentParser(EvaluationArguments)
_SCREAMING_SNAKE_CASE    :      Union[str, Any]          =			parser.parse_args()
set_seed(args.seed)

# Logging
_SCREAMING_SNAKE_CASE    :      Dict          =			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
_SCREAMING_SNAKE_CASE    :      Optional[int]          =			AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_SCREAMING_SNAKE_CASE    :      List[str]          =			AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			create_dataloader(args)

# Prepare everything with our `accelerator`.
_SCREAMING_SNAKE_CASE   ,     _SCREAMING_SNAKE_CASE    :      Optional[Any]          =			accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
_SCREAMING_SNAKE_CASE   ,     _SCREAMING_SNAKE_CASE    :      Optional[int]          =			evaluate(args)



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