





'''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 				__SCREAMING_SNAKE_CASE  (unittest.TestCase      ):






				"""simple docstring"""







				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								_a								= AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"					)
								_a								= AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"					).to(__a					)
								_a								= -1

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

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

								self.assertEqual(__a					,				__a					)




				def   UpperCamelCase__   (  self     :					Optional[int]					):
								_a								= AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"					)
								_a								= AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"					).to(__a					)
								_a								= -1

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

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

								self.assertEqual(__a					,				__a					)




				def   UpperCamelCase__   (  self     :					str					):
								_a								= AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"					)
								_a								= AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"					).to(__a					)
								_a								= -1

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

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

								self.assertEqual(__a					,				__a					)




				def   UpperCamelCase__   (  self     :					int					):
								# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
								# with actual models -- the dummy models' tokenizers are not aligned with their models, and
								# `skip_special_tokens=True` has no effect on them
								_a								= AutoTokenizer.from_pretrained("distilgpt2"					)
								_a								= AutoModelForCausalLM.from_pretrained("distilgpt2"					).to(__a					)
								_a								= -1

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

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




				def   UpperCamelCase__   (  self     :					Any					):
								_a								= AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"					)
								_a								= AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"					).to(__a					)
								_a								= -1

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

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

"""simple docstring"""
def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Union[str, Any]:
  if height >= 1:
    move_tower(height - 1		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
    move_disk(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
    move_tower(height - 1		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->Union[str, Any]:
  print('moving disk from'		,     _SCREAMING_SNAKE_CASE		,     'to'		,     _SCREAMING_SNAKE_CASE   )







def 						__a   (	)	->List[str]:
  a__:     Dict										=	int(input('Height of hanoi: '   ).strip()   )
  move_tower(_SCREAMING_SNAKE_CASE		,     'A'		,     'B'		,     'C'   )


if __name__ == "__main__":
 main()


