

'''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 							lowercase_						(		unittest.TestCase			):

  '''simple docstring'''





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


    """simple docstring"""
    UpperCAmelCase    				=						AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'  )
    UpperCAmelCase    				=						AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'  ).to(a_  )
    UpperCAmelCase    				=						-1

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

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

    self.assertEqual(a_ ,			a_  )
  def     snake_case_      (  self  )				->					Any:


    """simple docstring"""
    UpperCAmelCase    				=						AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'  )
    UpperCAmelCase    				=						AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'  ).to(a_  )
    UpperCAmelCase    				=						-1

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

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

    self.assertEqual(a_ ,			a_  )
  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'  )
    UpperCAmelCase    				=						AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'  ).to(a_  )
    UpperCAmelCase    				=						-1

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

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

    self.assertEqual(a_ ,			a_  )
  def     snake_case_      (  self  )				->					Optional[Any]:


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

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





  def     snake_case_      (  self  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'  )
    UpperCAmelCase    				=						AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'  ).to(a_  )
    UpperCAmelCase    				=						-1

    UpperCAmelCase    				=						ids_tensor((1, 5) ,			vocab_size=model.config.vocab_size  ).to(a_  )
    UpperCAmelCase    				=						TextIteratorStreamer(a_ ,			timeout=0.001  )
    UpperCAmelCase    				=						{'input_ids': input_ids, 'max_new_tokens': 1_0, 'do_sample': False, 'streamer': streamer}
    UpperCAmelCase    				=						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_  ):
      UpperCAmelCase    				=						''
      for new_text in streamer:
        streamer_text += new_text





'''simple docstring'''




def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : List[Any]							, SCREAMING_SNAKE_CASE   : Union[str, Any]    ):
  # "extended trapezoidal rule"
  # int(f) = dx/2 * (f1 + 2f2 + ... + fn)
  UpperCAmelCase    				=						(boundary[1] - boundary[0]) / steps
  UpperCAmelCase    				=						boundary[0]
  UpperCAmelCase    				=						boundary[1]
  UpperCAmelCase    				=						make_points(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						0.0
  y += (h / 2.0) * f(SCREAMING_SNAKE_CASE    )
  for i in x_i:
    # print(i)
    y += h * f(SCREAMING_SNAKE_CASE    )
  y += (h / 2.0) * f(SCREAMING_SNAKE_CASE    )
  return y


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Tuple							, SCREAMING_SNAKE_CASE   : Dict							, SCREAMING_SNAKE_CASE   : Tuple    ):
  UpperCAmelCase    				=						a + h
  while x < (b - h):
    yield x
    UpperCAmelCase    				=						x + h


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Optional[Any]    ):  # enter your function here
  UpperCAmelCase    				=						(x - 0) * (x - 0)
  return y


def    lowerCamelCase__		(    ):
  UpperCAmelCase    				=						0.0  # Lower bound of integration
  UpperCAmelCase    				=						1.0  # Upper bound of integration
  UpperCAmelCase    				=						10.0  # define number of steps or resolution
  UpperCAmelCase    				=						[a, b]  # define boundary of integration
  UpperCAmelCase    				=						method_a(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )
  print(f'''y = {y}'''    )


if __name__ == "__main__":
  main()


