




'''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    __lowerCAmelCase       (							unittest.TestCase):






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         lowercase    :Dict      	=						AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"				)
         lowercase    :List[str]      	=						AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"				).to(_lowerCAmelCase				)
         lowercase    :Optional[int]      	=						-1

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

         with CaptureStdout() as cs:
           lowercase    :Optional[Any]      	=						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
         lowercase    :Optional[int]      	=						cs.out[:-1]

         self.assertEqual(_lowerCAmelCase       ,					_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         lowercase    :str      	=						AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"				)
         lowercase    :Dict      	=						AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"				).to(_lowerCAmelCase				)
         lowercase    :Dict      	=						-1

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

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

         self.assertEqual(_lowerCAmelCase       ,					_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         lowercase    :List[Any]      	=						AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"				)
         lowercase    :List[Any]      	=						AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"				).to(_lowerCAmelCase				)
         lowercase    :Any      	=						-1

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

         with CaptureStdout() as cs:
           lowercase    :Tuple      	=						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
         lowercase    :Any      	=						cs.out[:-1]

         self.assertEqual(_lowerCAmelCase       ,					_lowerCAmelCase				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         lowercase    :Dict      	=						AutoTokenizer.from_pretrained("distilgpt2"				)
         lowercase    :int      	=						AutoModelForCausalLM.from_pretrained("distilgpt2"				).to(_lowerCAmelCase				)
         lowercase    :List[str]      	=						-1

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






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         lowercase    :Optional[Any]      	=						AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"				)
         lowercase    :str      	=						AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"				).to(_lowerCAmelCase				)
         lowercase    :int      	=						-1

         lowercase    :List[Any]      	=						ids_tensor((1, 5)       ,					vocab_size=model.config.vocab_size				).to(_lowerCAmelCase				)
         lowercase    :Any      	=						TextIteratorStreamer(_lowerCAmelCase       ,					timeout=0.0_01				)
         lowercase    :Optional[Any]      	=						{"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
         lowercase    :List[str]      	=						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				):
           lowercase    :Tuple      	=						""""""
           for new_text in streamer:
             streamer_text += new_text









def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase = False   ):
  if n == 2:
    return True
  if not n % 2 or n < 2:
    return False
  if n > 5 and n % 10 not in (1, 3, 7, 9):  # can quickly check last digit
    return False
  if n > 3317044064679887385961981 and not allow_probable:
    raise ValueError(
        "Warning: upper bound of deterministic test is exceeded. "
        "Pass allow_probable=True to allow probabilistic test. "
        "A return value of True indicates a probable prime."   )
  # array bounds provided by analysis
  lowercase    :Optional[Any]      	=						[
      2047,
      1373653,
      25326001,
      3215031751,
      2152302898747,
      3474749660383,
      341550071728321,
      1,
      3825123056546413051,
      1,
      1,
      318665857834031151167461,
      3317044064679887385961981,
  ]

  lowercase    :List[Any]      	=						[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
  for idx, _p in enumerate(lowerCamelCase,			1   ):
    if n < _p:
      # then we have our last prime to check
      lowercase    :Tuple      	=						primes[:idx]
      break
  lowercase	,    lowercase    :str      	=						n - 1, 0
  # break up n -1 into a power of 2 (s) and
  # remaining odd component
  # essentially, solve for d * 2 ** s == n - 1
  while d % 2 == 0:
    d //= 2
    s += 1
  for prime in plist:
    lowercase    :Tuple      	=						False
    for r in range(lowerCamelCase   ):
      lowercase    :Any      	=						pow(lowerCamelCase,			d * 2**r,			lowerCamelCase   )
      # see article for analysis explanation for m
      if (r == 0 and m == 1) or ((m + 1) % n == 0):
        lowercase    :Any      	=						True
        # this loop will not determine compositeness
        break
    if pr:
      continue
    # if pr is False, then the above loop never evaluated to true,
    # and the n MUST be composite
    return False
  return True



def       UpperCAmelCase__  (     ):
  assert not miller_rabin(561   )
  assert miller_rabin(563   )
  # 2047

  assert not miller_rabin(838201   )
  assert miller_rabin(838207   )
  # 1_373_653

  assert not miller_rabin(17316001   )
  assert miller_rabin(17316017   )
  # 25_326_001

  assert not miller_rabin(3078386641   )
  assert miller_rabin(3078386653   )
  # 3_215_031_751

  assert not miller_rabin(1713045574801   )
  assert miller_rabin(1713045574819   )
  # 2_152_302_898_747

  assert not miller_rabin(2779799728307   )
  assert miller_rabin(2779799728327   )
  # 3_474_749_660_383

  assert not miller_rabin(113850023909441   )
  assert miller_rabin(113850023909527   )
  # 341_550_071_728_321

  assert not miller_rabin(1275041018848804351   )
  assert miller_rabin(1275041018848804391   )
  # 3_825_123_056_546_413_051

  assert not miller_rabin(79666464458507787791867   )
  assert miller_rabin(79666464458507787791951   )
  # 318_665_857_834_031_151_167_461

  assert not miller_rabin(552840677446647897660333   )
  assert miller_rabin(552840677446647897660359   )
  # 3_317_044_064_679_887_385_961_981
  # upper limit for probabilistic test


if __name__ == "__main__":
     test_miller_rabin()



