



'''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):


 '''simple docstring'''




 def       lowercase_  (   self) ->      Union[str, Any]:



      """simple docstring"""







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

      a_       		=ids_tensor((1, 5)							,  vocab_size=model.config.vocab_size).to(lowerCAmelCase_)
      a_       		=model.generate(lowerCAmelCase_							,  max_new_tokens=1_0							,  do_sample=lowerCAmelCase_)
      a_       		=tokenizer.decode(greedy_ids[0])

      with CaptureStdout() as cs:
           a_       		=TextStreamer(lowerCAmelCase_)
           model.generate(lowerCAmelCase_							,  max_new_tokens=1_0							,  do_sample=lowerCAmelCase_							,  streamer=lowerCAmelCase_)
      # The greedy text should be printed to stdout, except for the final "\n" in the streamer
      a_       		=cs.out[:-1]

      self.assertEqual(lowerCAmelCase_							,  lowerCAmelCase_)




 def       lowercase_  (   self) ->      int:



      """simple docstring"""







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

      a_       		=ids_tensor((1, 5)							,  vocab_size=model.config.vocab_size).to(lowerCAmelCase_)
      a_       		=model.generate(lowerCAmelCase_							,  max_new_tokens=1_0							,  do_sample=lowerCAmelCase_)
      a_       		=tokenizer.decode(greedy_ids[0])

      a_       		=TextIteratorStreamer(lowerCAmelCase_)
      a_       		={"input_ids": input_ids, "max_new_tokens": 1_0, "do_sample": False, "streamer": streamer}
      a_       		=Thread(target=model.generate							,  kwargs=lowerCAmelCase_)
      thread.start()
      a_       		=""
      for new_text in streamer:
           streamer_text += new_text

      self.assertEqual(lowerCAmelCase_							,  lowerCAmelCase_)




 def       lowercase_  (   self) ->      Optional[int]:



      """simple docstring"""







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

      a_       		=ids_tensor((1, 5)							,  vocab_size=model.config.vocab_size).to(lowerCAmelCase_)
      a_       		=model.generate(lowerCAmelCase_							,  max_new_tokens=1_0							,  do_sample=lowerCAmelCase_)
      a_       		=greedy_ids[:, input_ids.shape[1] :]
      a_       		=tokenizer.decode(new_greedy_ids[0])

      with CaptureStdout() as cs:
           a_       		=TextStreamer(lowerCAmelCase_							,  skip_prompt=lowerCAmelCase_)
           model.generate(lowerCAmelCase_							,  max_new_tokens=1_0							,  do_sample=lowerCAmelCase_							,  streamer=lowerCAmelCase_)
      # The greedy text should be printed to stdout, except for the final "\n" in the streamer
      a_       		=cs.out[:-1]

      self.assertEqual(lowerCAmelCase_							,  lowerCAmelCase_)




 def       lowercase_  (   self) ->      int:



      """simple docstring"""







      a_       		=AutoTokenizer.from_pretrained("distilgpt2")
      a_       		=AutoModelForCausalLM.from_pretrained("distilgpt2").to(lowerCAmelCase_)
      a_       		=-1

      a_       		=torch.ones((1, 5)							,  device=lowerCAmelCase_).long() * model.config.bos_token_id
      with CaptureStdout() as cs:
           a_       		=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
      a_       		=cs.out[:-1]  # Remove the final "\n"
      a_       		=tokenizer(lowerCAmelCase_							,  return_tensors="pt")
      self.assertEqual(streamer_text_tokenized.input_ids.shape							,  (1, 1))




 def       lowercase_  (   self) ->      Optional[int]:



      """simple docstring"""







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

      a_       		=ids_tensor((1, 5)							,  vocab_size=model.config.vocab_size).to(lowerCAmelCase_)
      a_       		=TextIteratorStreamer(lowerCAmelCase_							,  timeout=0.0_0_1)
      a_       		={"input_ids": input_ids, "max_new_tokens": 1_0, "do_sample": False, "streamer": streamer}
      a_       		=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_):
           a_       		=""
           for new_text in streamer:
                streamer_text += new_text







'''simple docstring'''


from collections.abc import Generator






def  UpperCAmelCase_       (		):


     '''simple docstring'''







     a_		,						a_       		=0, 1
     while True:
          a_		,						a_       		=b, a + b
          yield b






def  UpperCAmelCase_       (		lowercase__ = 1_0_0_0       ):


     '''simple docstring'''







     a_       		=1
     a_       		=fibonacci_generator()
     while len(str(next(lowercase__       )       )       ) < n:
          answer += 1
     return answer + 1


if __name__ == "__main__":
  print(solution(int(str(input()).strip())))


