






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      ):



				"""simple docstring"""



				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







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

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

											with CaptureStdout() as cs:
																		snake_case__	  =       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
											snake_case__	  =       cs.out[:-1]

											self.assertEqual(A_			,		A_)



				def 					__magic_name__							(      self    :    Any):






											'''simple docstring'''







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

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

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

											self.assertEqual(A_			,		A_)



				def 					__magic_name__							(      self    :    Optional[int]):






											'''simple docstring'''







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

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

											with CaptureStdout() as cs:
																		snake_case__	  =       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
											snake_case__	  =       cs.out[:-1]

											self.assertEqual(A_			,		A_)



				def 					__magic_name__							(      self    :    List[Any]):






											'''simple docstring'''







											snake_case__	  =       AutoTokenizer.from_pretrained("""distilgpt2""")
											snake_case__	  =       AutoModelForCausalLM.from_pretrained("""distilgpt2""").to(A_)
											snake_case__	  =       -1

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



				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







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

											snake_case__	  =       ids_tensor((1, 5)			,		vocab_size=model.config.vocab_size).to(A_)
											snake_case__	  =       TextIteratorStreamer(A_			,		timeout=0.0_01)
											snake_case__	  =       {"input_ids": input_ids, "max_new_tokens": 1_0, "do_sample": False, "streamer": streamer}
											snake_case__	  =       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_):
																		snake_case__	  =       ""
																		for new_text in streamer:
																									streamer_text += new_text








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

def _UpperCAmelCase   (	a						:	Dict  ,					a						:	Union[str, Any]  ,					a						:	Union[str, Any] ):
							snake_case__	  =       a + h
							while x < (b - h):
														yield x
														snake_case__	  =       x + h

def _UpperCAmelCase   (	a						:	Any ):  # enter your function here
							snake_case__	  =       (x - 0) * (x - 0)
							return y

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


if __name__ == "__main__":
	main()
