


"""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 			__snake_case   (unittest.TestCase  ):




							def   __a   (     self:    str   ):
										__lowerCamelCase          =      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""   )
										__lowerCamelCase          =      AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""   ).to(A_   )
										__lowerCamelCase          =      -1

										__lowerCamelCase          =      ids_tensor((1, 5)     ,							vocab_size=model.config.vocab_size   ).to(A_   )
										__lowerCamelCase          =      model.generate(A_     ,							max_new_tokens=10     ,							do_sample=A_   )
										__lowerCamelCase          =      tokenizer.decode(greedy_ids[0]   )

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

										self.assertEqual(A_     ,							A_   )




							def   __a   (     self:    int   ):
										__lowerCamelCase          =      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""   )
										__lowerCamelCase          =      AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""   ).to(A_   )
										__lowerCamelCase          =      -1

										__lowerCamelCase          =      ids_tensor((1, 5)     ,							vocab_size=model.config.vocab_size   ).to(A_   )
										__lowerCamelCase          =      model.generate(A_     ,							max_new_tokens=10     ,							do_sample=A_   )
										__lowerCamelCase          =      tokenizer.decode(greedy_ids[0]   )

										__lowerCamelCase          =      TextIteratorStreamer(A_   )
										__lowerCamelCase          =      {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
										__lowerCamelCase          =      Thread(target=model.generate     ,							kwargs=A_   )
										thread.start()
										__lowerCamelCase          =      """"""
										for new_text in streamer:
													streamer_text += new_text

										self.assertEqual(A_     ,							A_   )




							def   __a   (     self:    Optional[Any]   ):
										__lowerCamelCase          =      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""   )
										__lowerCamelCase          =      AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""   ).to(A_   )
										__lowerCamelCase          =      -1

										__lowerCamelCase          =      ids_tensor((1, 5)     ,							vocab_size=model.config.vocab_size   ).to(A_   )
										__lowerCamelCase          =      model.generate(A_     ,							max_new_tokens=10     ,							do_sample=A_   )
										__lowerCamelCase          =      greedy_ids[:, input_ids.shape[1] :]
										__lowerCamelCase          =      tokenizer.decode(new_greedy_ids[0]   )

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

										self.assertEqual(A_     ,							A_   )




							def   __a   (     self:    Optional[int]   ):
										# 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
										__lowerCamelCase          =      AutoTokenizer.from_pretrained("""distilgpt2"""   )
										__lowerCamelCase          =      AutoModelForCausalLM.from_pretrained("""distilgpt2"""   ).to(A_   )
										__lowerCamelCase          =      -1

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




							def   __a   (     self:    List[Any]   ):
										__lowerCamelCase          =      AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""   )
										__lowerCamelCase          =      AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""   ).to(A_   )
										__lowerCamelCase          =      -1

										__lowerCamelCase          =      ids_tensor((1, 5)     ,							vocab_size=model.config.vocab_size   ).to(A_   )
										__lowerCamelCase          =      TextIteratorStreamer(A_     ,							timeout=0.001   )
										__lowerCamelCase          =      {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
										__lowerCamelCase          =      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_   ):
													__lowerCamelCase          =      """"""
													for new_text in streamer:
																streamer_text += new_text








"""simple docstring"""




from collections import defaultdict





def       a_ (	lowercase__ :int     ):
			__lowerCamelCase          =      1
			__lowerCamelCase          =      True
			for v in tree[start]:
						if v not in visited:
									ret += dfs(lowercase__     )
			if ret % 2 == 0:
						cuts.append(lowercase__     )
			return ret





def       a_ (	):
			dfs(1     )


if __name__ == "__main__":
					__magic_name__  , __magic_name__						: Tuple    =				1_0, 9
					__magic_name__						: Tuple    =				defaultdict(list)
					__magic_name__						: dict[int, bool]    =				{}
					__magic_name__						: list[int]    =				[]
					__magic_name__						: List[str]    =				0
					__magic_name__						: Tuple    =				[(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (1_0, 8)]
					for u, v in edges:
										tree[u].append(v)
										tree[v].append(u)
					even_tree()
					print(len(cuts) - 1)




