




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       a_       (     unittest.TestCase     ):





	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'      )
				SCREAMING_SNAKE_CASE_								=						AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'      ).to(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						-1

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

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

				self.assertEqual(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )





	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'      )
				SCREAMING_SNAKE_CASE_								=						AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'      ).to(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						-1

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

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

				self.assertEqual(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )





	def 						A_(							self      )  ->					List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'      )
				SCREAMING_SNAKE_CASE_								=						AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'      ).to(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						-1

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

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

				self.assertEqual(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )





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




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						AutoTokenizer.from_pretrained('distilgpt2'      )
				SCREAMING_SNAKE_CASE_								=						AutoModelForCausalLM.from_pretrained('distilgpt2'      ).to(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						-1

				SCREAMING_SNAKE_CASE_								=						torch.ones((1, 5)      ,     device=SCREAMING_SNAKE_CASE      ).long() * model.config.bos_token_id
				with CaptureStdout() as cs:
							SCREAMING_SNAKE_CASE_								=						TextStreamer(SCREAMING_SNAKE_CASE      ,     skip_special_tokens=SCREAMING_SNAKE_CASE      )
							model.generate(SCREAMING_SNAKE_CASE      ,     max_new_tokens=1      ,     do_sample=SCREAMING_SNAKE_CASE      ,     streamer=SCREAMING_SNAKE_CASE      )

				# 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
				SCREAMING_SNAKE_CASE_								=						cs.out[:-1]  # Remove the final "\n"
				SCREAMING_SNAKE_CASE_								=						tokenizer(SCREAMING_SNAKE_CASE      ,     return_tensors='pt'      )
				self.assertEqual(streamer_text_tokenized.input_ids.shape      ,     (1, 1)      )





	def 						A_(							self      )  ->					List[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'      )
				SCREAMING_SNAKE_CASE_								=						AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'      ).to(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						-1

				SCREAMING_SNAKE_CASE_								=						ids_tensor((1, 5)      ,     vocab_size=model.config.vocab_size      ).to(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						TextIteratorStreamer(SCREAMING_SNAKE_CASE      ,     timeout=0.0_0_1      )
				SCREAMING_SNAKE_CASE_								=						{'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
				SCREAMING_SNAKE_CASE_								=						Thread(target=model.generate      ,     kwargs=SCREAMING_SNAKE_CASE      )
				thread.start()

				# The streamer will timeout after 0.001 seconds, so an exception will be raised
				with self.assertRaises(SCREAMING_SNAKE_CASE      ):
							SCREAMING_SNAKE_CASE_								=						''
							for new_text in streamer:
										streamer_text += new_text






from __future__ import annotations

from typing import Any

def lowercase    (  SCREAMING_SNAKE_CASE )			->							None:


			'''simple docstring'''







			create_state_space_tree(SCREAMING_SNAKE_CASE       ,    []       ,    0 )

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							None:


			'''simple docstring'''







			if index == len(SCREAMING_SNAKE_CASE ):
						print(SCREAMING_SNAKE_CASE )
						return

			create_state_space_tree(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    index + 1 )
			current_subsequence.append(sequence[index] )
			create_state_space_tree(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    index + 1 )
			current_subsequence.pop()


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     list[Any]		    =       [3, 1, 2, 4]
				generate_all_subsequences(seq)

				seq.clear()
				seq.extend(["A", "B", "C"])
				generate_all_subsequences(seq)
