

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				):




      """simple docstring"""

      def 		__A	(   self							:				Union[str, Any]	):
            A_						  =				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"	)
            A_						  =				AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"	).to(UpperCAmelCase	)
            A_						  =				-1

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

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

            self.assertEqual(UpperCAmelCase						,			UpperCAmelCase	)





      def 		__A	(   self							:				Tuple	):
            A_						  =				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2"	)
            A_						  =				AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2"	).to(UpperCAmelCase	)
            A_						  =				-1

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

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

            self.assertEqual(UpperCAmelCase						,			UpperCAmelCase	)





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

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

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

            self.assertEqual(UpperCAmelCase						,			UpperCAmelCase	)





      def 		__A	(   self							:				Optional[Any]	):
            # 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
            A_						  =				AutoTokenizer.from_pretrained("distilgpt2"	)
            A_						  =				AutoModelForCausalLM.from_pretrained("distilgpt2"	).to(UpperCAmelCase	)
            A_						  =				-1

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

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







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

            A_						  =				ids_tensor((1, 5)						,			vocab_size=model.config.vocab_size	).to(UpperCAmelCase	)
            A_						  =				TextIteratorStreamer(UpperCAmelCase						,			timeout=0.001	)
            A_						  =				{"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
            A_						  =				Thread(target=model.generate						,			kwargs=UpperCAmelCase	)
            thread.start()

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







from __future__ import annotations







def     a_   (__A      )				->						int:



					"""simple docstring"""
					__a					:						List[Any]      =					len(__A      ) // 2

					# choose the middle 3 elements
					__a					:						Any      =					lst[m - 1 : m + 2]

					# if middle element is peak
					if three[1] > three[0] and three[1] > three[2]:
										return three[1]

					# if increasing, recurse on right
					elif three[0] < three[2]:
										if len(lst[:m]      ) == 2:
															m -= 1
										return peak(lst[m:]      )

					# decreasing
					else:
										if len(lst[:m]      ) == 2:
															m += 1
										return peak(lst[:m]      )


if __name__ == "__main__":
			import doctest

			doctest.testmod()





