




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        _lowercase  (   self							)       ->						Dict:



        '''simple docstring'''




        a__     : Optional[int]  =AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"							)
        a__     : Tuple  =AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"							).to(_lowerCamelCase							)
        a__     : Dict  =-1

        a__     : List[str]  =ids_tensor((1, 5)		,				vocab_size=model.config.vocab_size							).to(_lowerCamelCase							)
        a__     : Any  =model.generate(_lowerCamelCase		,				max_new_tokens=1_0		,				do_sample=_lowerCamelCase							)
        a__     : List[str]  =tokenizer.decode(greedy_ids[0]							)

        with CaptureStdout() as cs:
          a__     : List[str]  =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__     : Dict  =cs.out[:-1]

        self.assertEqual(_lowerCamelCase		,				_lowerCamelCase							)







      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : Optional[int]  =AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"							)
        a__     : List[str]  =AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"							).to(_lowerCamelCase							)
        a__     : Dict  =-1

        a__     : List[str]  =ids_tensor((1, 5)		,				vocab_size=model.config.vocab_size							).to(_lowerCamelCase							)
        a__     : Optional[int]  =model.generate(_lowerCamelCase		,				max_new_tokens=1_0		,				do_sample=_lowerCamelCase							)
        a__     : str  =tokenizer.decode(greedy_ids[0]							)

        a__     : int  =TextIteratorStreamer(_lowerCamelCase							)
        a__     : List[Any]  ={'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
        a__     : List[Any]  =Thread(target=model.generate		,				kwargs=_lowerCamelCase							)
        thread.start()
        a__     : List[Any]  =''''''
        for new_text in streamer:
          streamer_text += new_text

        self.assertEqual(_lowerCamelCase		,				_lowerCamelCase							)







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        a__     : List[str]  =AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"							)
        a__     : List[Any]  =AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"							).to(_lowerCamelCase							)
        a__     : List[str]  =-1

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

        with CaptureStdout() as cs:
          a__     : Any  =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__     : Any  =cs.out[:-1]

        self.assertEqual(_lowerCamelCase		,				_lowerCamelCase							)







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        a__     : List[Any]  =AutoTokenizer.from_pretrained("distilgpt2"							)
        a__     : Tuple  =AutoModelForCausalLM.from_pretrained("distilgpt2"							).to(_lowerCamelCase							)
        a__     : List[Any]  =-1

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







      def        _lowercase  (   self							)       ->						Dict:



        '''simple docstring'''




        a__     : str  =AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"							)
        a__     : str  =AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"							).to(_lowerCamelCase							)
        a__     : Union[str, Any]  =-1

        a__     : Union[str, Any]  =ids_tensor((1, 5)		,				vocab_size=model.config.vocab_size							).to(_lowerCamelCase							)
        a__     : List[str]  =TextIteratorStreamer(_lowerCamelCase		,				timeout=0.0_01							)
        a__     : str  ={'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
        a__     : 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							):
          a__     : str  =''''''
          for new_text in streamer:
            streamer_text += new_text













def 					_A	(		SCREAMING_SNAKE_CASE					:       int = 1	,	SCREAMING_SNAKE_CASE					:       int = 1_000						):





  """simple docstring"""



  a__     : Any  =1
  a__     : Any  =0

  for divide_by_number in range(SCREAMING_SNAKE_CASE	,	digit + 1						):
    a__     : list[int]  =[]
    a__     : int  =numerator
    for _ in range(1	,	digit + 1						):
      if now_divide in has_been_divided:
        if longest_list_length < len(SCREAMING_SNAKE_CASE						):
          a__     : Optional[Any]  =len(SCREAMING_SNAKE_CASE						)
          a__     : List[str]  =divide_by_number
      else:
        has_been_divided.append(SCREAMING_SNAKE_CASE						)
        a__     : List[Any]  =now_divide * 10 % divide_by_number

  return the_digit


# Tests
if __name__ == "__main__":
     import doctest

     doctest.testmod()






