




"""simple docstring"""

import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
		import torch

		from transformers import AutoModelForCausalLM

@require_torch
class        lowercase      (    unittest.TestCase):







				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					A_  :				int	   =		AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       )
					A_  :				List[Any]	   =		AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       ).to(__snake_case       )
					A_  :				Optional[Any]	   =		-1

					A_  :				List[str]	   =		ids_tensor((1, 5)      ,     vocab_size=model.config.vocab_size       ).to(__snake_case       )
					A_  :				Tuple	   =		model.generate(__snake_case      ,     max_new_tokens=10      ,     do_sample=__snake_case       )
					A_  :				str	   =		tokenizer.decode(greedy_ids[0]       )

					with CaptureStdout() as cs:
						A_  :				str	   =		TextStreamer(__snake_case       )
						model.generate(__snake_case      ,     max_new_tokens=10      ,     do_sample=__snake_case      ,     streamer=__snake_case       )
					# The greedy text should be printed to stdout, except for the final "\n" in the streamer
					A_  :				Optional[int]	   =		cs.out[:-1]

					self.assertEqual(__snake_case      ,     __snake_case       )







				def 					a_  (							self   :					Dict       ):



					"""simple docstring"""

					A_  :				Tuple	   =		AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       )
					A_  :				int	   =		AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       ).to(__snake_case       )
					A_  :				List[Any]	   =		-1

					A_  :				Dict	   =		ids_tensor((1, 5)      ,     vocab_size=model.config.vocab_size       ).to(__snake_case       )
					A_  :				Tuple	   =		model.generate(__snake_case      ,     max_new_tokens=10      ,     do_sample=__snake_case       )
					A_  :				List[Any]	   =		tokenizer.decode(greedy_ids[0]       )

					A_  :				List[str]	   =		TextIteratorStreamer(__snake_case       )
					A_  :				List[str]	   =		{'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
					A_  :				Tuple	   =		Thread(target=model.generate      ,     kwargs=__snake_case       )
					thread.start()
					A_  :				Any	   =		''''''
					for new_text in streamer:
						streamer_text += new_text

					self.assertEqual(__snake_case      ,     __snake_case       )







				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					A_  :				int	   =		AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       )
					A_  :				Dict	   =		AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       ).to(__snake_case       )
					A_  :				List[str]	   =		-1

					A_  :				Optional[Any]	   =		ids_tensor((1, 5)      ,     vocab_size=model.config.vocab_size       ).to(__snake_case       )
					A_  :				Optional[Any]	   =		model.generate(__snake_case      ,     max_new_tokens=10      ,     do_sample=__snake_case       )
					A_  :				List[str]	   =		greedy_ids[:, input_ids.shape[1] :]
					A_  :				Union[str, Any]	   =		tokenizer.decode(new_greedy_ids[0]       )

					with CaptureStdout() as cs:
						A_  :				List[str]	   =		TextStreamer(__snake_case      ,     skip_prompt=__snake_case       )
						model.generate(__snake_case      ,     max_new_tokens=10      ,     do_sample=__snake_case      ,     streamer=__snake_case       )
					# The greedy text should be printed to stdout, except for the final "\n" in the streamer
					A_  :				int	   =		cs.out[:-1]

					self.assertEqual(__snake_case      ,     __snake_case       )







				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					A_  :				List[Any]	   =		AutoTokenizer.from_pretrained('''distilgpt2'''       )
					A_  :				Union[str, Any]	   =		AutoModelForCausalLM.from_pretrained('''distilgpt2'''       ).to(__snake_case       )
					A_  :				Optional[int]	   =		-1

					A_  :				Union[str, Any]	   =		torch.ones((1, 5)      ,     device=__snake_case       ).long() * model.config.bos_token_id
					with CaptureStdout() as cs:
						A_  :				Dict	   =		TextStreamer(__snake_case      ,     skip_special_tokens=__snake_case       )
						model.generate(__snake_case      ,     max_new_tokens=1      ,     do_sample=__snake_case      ,     streamer=__snake_case       )

					# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
					# re-tokenized, must only contain one token
					A_  :				Tuple	   =		cs.out[:-1]  # Remove the final "\n"
					A_  :				int	   =		tokenizer(__snake_case      ,     return_tensors='''pt'''       )
					self.assertEqual(streamer_text_tokenized.input_ids.shape      ,     (1, 1)       )







				def 					a_  (							self   :					List[Any]       ):



					"""simple docstring"""

					A_  :				List[str]	   =		AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       )
					A_  :				Optional[int]	   =		AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       ).to(__snake_case       )
					A_  :				Optional[int]	   =		-1

					A_  :				Tuple	   =		ids_tensor((1, 5)      ,     vocab_size=model.config.vocab_size       ).to(__snake_case       )
					A_  :				List[Any]	   =		TextIteratorStreamer(__snake_case      ,     timeout=0.001       )
					A_  :				Dict	   =		{'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
					A_  :				Tuple	   =		Thread(target=model.generate      ,     kwargs=__snake_case       )
					thread.start()

					# The streamer will timeout after 0.001 seconds, so an exception will be raised
					with self.assertRaises(__snake_case       ):
						A_  :				Dict	   =		''''''
						for new_text in streamer:
							streamer_text += new_text











"""simple docstring"""

import requests
from bsa import BeautifulSoup

def 		lowercase_      (		_UpperCAmelCase = "https://www.worldometers.info/coronavirus"	):




	"""simple docstring"""






	A_  :				str	   =		BeautifulSoup(requests.get(_UpperCAmelCase	).text  ,      '''html.parser'''	)
	A_  :				Union[str, Any]	   =		soup.findAll('''h1'''	)
	A_  :				Union[str, Any]	   =		soup.findAll('''div'''  ,      {'''class''': '''maincounter-number'''}	)
	keys += soup.findAll('''span'''  ,      {'''class''': '''panel-title'''}	)
	values += soup.findAll('''div'''  ,      {'''class''': '''number-table-main'''}	)
	return {key.text.strip(): value.text.strip() for key, value in zip(_UpperCAmelCase  ,      _UpperCAmelCase	)}


if __name__ == "__main__":
		print('\033[1m' + 'COVID-19 Status of the World' + '\033[0m\n')
		for key, value in world_covidaa_stats().items():
				print(f'{key}\n{value}\n')





