
'''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 						__UpperCAmelCase      (					unittest.TestCase						):


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'  )
								_snake_case   						=		AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'  ).to(lowerCAmelCase_  )
								_snake_case   						=		-1

								_snake_case   						=		ids_tensor((1, 5)    ,   vocab_size=model.config.vocab_size  ).to(lowerCAmelCase_  )
								_snake_case   						=		model.generate(lowerCAmelCase_    ,   max_new_tokens=10    ,   do_sample=lowerCAmelCase_  )
								_snake_case   						=		tokenizer.decode(greedy_ids[0]  )

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

								self.assertEqual(lowerCAmelCase_    ,   lowerCAmelCase_  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'  )
								_snake_case   						=		AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'  ).to(lowerCAmelCase_  )
								_snake_case   						=		-1

								_snake_case   						=		ids_tensor((1, 5)    ,   vocab_size=model.config.vocab_size  ).to(lowerCAmelCase_  )
								_snake_case   						=		model.generate(lowerCAmelCase_    ,   max_new_tokens=10    ,   do_sample=lowerCAmelCase_  )
								_snake_case   						=		tokenizer.decode(greedy_ids[0]  )

								_snake_case   						=		TextIteratorStreamer(lowerCAmelCase_  )
								_snake_case   						=		{'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
								_snake_case   						=		Thread(target=model.generate    ,   kwargs=lowerCAmelCase_  )
								thread.start()
								_snake_case   						=		''
								for new_text in streamer:
									streamer_text += new_text

								self.assertEqual(lowerCAmelCase_    ,   lowerCAmelCase_  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'  )
								_snake_case   						=		AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'  ).to(lowerCAmelCase_  )
								_snake_case   						=		-1

								_snake_case   						=		ids_tensor((1, 5)    ,   vocab_size=model.config.vocab_size  ).to(lowerCAmelCase_  )
								_snake_case   						=		model.generate(lowerCAmelCase_    ,   max_new_tokens=10    ,   do_sample=lowerCAmelCase_  )
								_snake_case   						=		greedy_ids[:, input_ids.shape[1] :]
								_snake_case   						=		tokenizer.decode(new_greedy_ids[0]  )

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

								self.assertEqual(lowerCAmelCase_    ,   lowerCAmelCase_  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		AutoTokenizer.from_pretrained('distilgpt2'  )
								_snake_case   						=		AutoModelForCausalLM.from_pretrained('distilgpt2'  ).to(lowerCAmelCase_  )
								_snake_case   						=		-1

								_snake_case   						=		torch.ones((1, 5)    ,   device=lowerCAmelCase_  ).long() * model.config.bos_token_id
								with CaptureStdout() as cs:
									_snake_case   						=		TextStreamer(lowerCAmelCase_    ,   skip_special_tokens=lowerCAmelCase_  )
									model.generate(lowerCAmelCase_    ,   max_new_tokens=1    ,   do_sample=lowerCAmelCase_    ,   streamer=lowerCAmelCase_  )

								# 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
								_snake_case   						=		cs.out[:-1]  # Remove the final "\n"
								_snake_case   						=		tokenizer(lowerCAmelCase_    ,   return_tensors='pt'  )
								self.assertEqual(streamer_text_tokenized.input_ids.shape    ,   (1, 1)  )




							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'  )
								_snake_case   						=		AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'  ).to(lowerCAmelCase_  )
								_snake_case   						=		-1

								_snake_case   						=		ids_tensor((1, 5)    ,   vocab_size=model.config.vocab_size  ).to(lowerCAmelCase_  )
								_snake_case   						=		TextIteratorStreamer(lowerCAmelCase_    ,   timeout=0.001  )
								_snake_case   						=		{'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
								_snake_case   						=		Thread(target=model.generate    ,   kwargs=lowerCAmelCase_  )
								thread.start()

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



'''simple docstring'''







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	str:
	_snake_case   						=		1
	_snake_case   						=		2
	while i * i <= n:
		_snake_case   						=		0
		while n % i == 0:
			n //= i
			multiplicity += 1
		n_divisors *= multiplicity + 1
		i += 1
	if n > 1:
		n_divisors *= 2
	return n_divisors







def 				SCREAMING_SNAKE_CASE__	(   )       ->	List[str]:
	_snake_case   						=		1
	_snake_case   						=		1

	while True:
		i += 1
		t_num += i

		if count_divisors(__A	) > 500:
			break

	return t_num


if __name__ == "__main__":
					print(solution())

