

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 						_A     (				unittest.TestCase				):





  '''simple docstring'''



  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		List[Any]        =		AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""    )
      snake_case							:		List[str]        =		AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""    ).to(SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[str]        =		-1

      snake_case							:		Union[str, Any]        =		ids_tensor((1, 5)   ,vocab_size=model.config.vocab_size    ).to(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[int]        =		model.generate(SCREAMING_SNAKE_CASE_   ,max_new_tokens=10   ,do_sample=SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[Any]        =		tokenizer.decode(greedy_ids[0]    )

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

      self.assertEqual(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Union[str, Any]        =		AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""    )
      snake_case							:		Dict        =		AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""    ).to(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[int]        =		-1

      snake_case							:		Optional[int]        =		ids_tensor((1, 5)   ,vocab_size=model.config.vocab_size    ).to(SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[Any]        =		model.generate(SCREAMING_SNAKE_CASE_   ,max_new_tokens=10   ,do_sample=SCREAMING_SNAKE_CASE_    )
      snake_case							:		Any        =		tokenizer.decode(greedy_ids[0]    )

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

      self.assertEqual(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		List[Any]        =		AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""    )
      snake_case							:		Optional[int]        =		AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""    ).to(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Any        =		-1

      snake_case							:		Dict        =		ids_tensor((1, 5)   ,vocab_size=model.config.vocab_size    ).to(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[int]        =		model.generate(SCREAMING_SNAKE_CASE_   ,max_new_tokens=10   ,do_sample=SCREAMING_SNAKE_CASE_    )
      snake_case							:		str        =		greedy_ids[:, input_ids.shape[1] :]
      snake_case							:		Dict        =		tokenizer.decode(new_greedy_ids[0]    )

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

      self.assertEqual(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      # 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
      snake_case							:		Dict        =		AutoTokenizer.from_pretrained("""distilgpt2"""    )
      snake_case							:		Tuple        =		AutoModelForCausalLM.from_pretrained("""distilgpt2"""    ).to(SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[Any]        =		-1

      snake_case							:		Any        =		torch.ones((1, 5)   ,device=SCREAMING_SNAKE_CASE_    ).long() * model.config.bos_token_id
      with CaptureStdout() as cs:
          snake_case							:		List[Any]        =		TextStreamer(SCREAMING_SNAKE_CASE_   ,skip_special_tokens=SCREAMING_SNAKE_CASE_    )
          model.generate(SCREAMING_SNAKE_CASE_   ,max_new_tokens=1   ,do_sample=SCREAMING_SNAKE_CASE_   ,streamer=SCREAMING_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
      snake_case							:		Dict        =		cs.out[:-1]  # Remove the final "\n"
      snake_case							:		Tuple        =		tokenizer(SCREAMING_SNAKE_CASE_   ,return_tensors="""pt"""    )
      self.assertEqual(streamer_text_tokenized.input_ids.shape   ,(1, 1)    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      snake_case							:		Tuple        =		AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""    )
      snake_case							:		Optional[int]        =		AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""    ).to(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Tuple        =		-1

      snake_case							:		int        =		ids_tensor((1, 5)   ,vocab_size=model.config.vocab_size    ).to(SCREAMING_SNAKE_CASE_    )
      snake_case							:		List[str]        =		TextIteratorStreamer(SCREAMING_SNAKE_CASE_   ,timeout=0.0_01    )
      snake_case							:		Optional[int]        =		{"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
      snake_case							:		List[str]        =		Thread(target=model.generate   ,kwargs=SCREAMING_SNAKE_CASE_    )
      thread.start()

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





def 		UpperCamelCase     (       _UpperCAmelCase  :   str  )   ->			bool:






						'''simple docstring'''

						_lowercase       :	List[str]           =   [int(_UpperCAmelCase  ) for i in ip_va_address.split("."  ) if i.isdigit()]
						return len(_UpperCAmelCase  ) == 4 and all(0 <= int(_UpperCAmelCase  ) <= 254 for octet in octets  )


if __name__ == "__main__":
							UpperCamelCase_						:     Dict	       =							input().strip()
							UpperCamelCase_						:     List[Any]	       =							"""valid""" if is_ip_va_address_valid(ip) else """invalid"""
							print(f'''{ip} is a {valid_or_invalid} IP v4 address.''')




