


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 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      str:
             a_ :		List[str]							=  AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'					)
             a_ :		Tuple							=  AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'					).to(SCREAMING_SNAKE_CASE__					)
             a_ :		int							=  -1

             a_ :		Union[str, Any]							=  ids_tensor((1, 5)					,							vocab_size=model.config.vocab_size					).to(SCREAMING_SNAKE_CASE__					)
             a_ :		str							=  model.generate(SCREAMING_SNAKE_CASE__					,							max_new_tokens=1_0					,							do_sample=SCREAMING_SNAKE_CASE__					)
             a_ :		List[str]							=  tokenizer.decode(greedy_ids[0]					)

             with CaptureStdout() as cs:
                    a_ :		Union[str, Any]							=  TextStreamer(SCREAMING_SNAKE_CASE__					)
                    model.generate(SCREAMING_SNAKE_CASE__					,							max_new_tokens=1_0					,							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
             a_ :		str							=  cs.out[:-1]

             self.assertEqual(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      Tuple:
             a_ :		Dict							=  AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'					)
             a_ :		Any							=  AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'					).to(SCREAMING_SNAKE_CASE__					)
             a_ :		int							=  -1

             a_ :		Optional[Any]							=  ids_tensor((1, 5)					,							vocab_size=model.config.vocab_size					).to(SCREAMING_SNAKE_CASE__					)
             a_ :		Tuple							=  model.generate(SCREAMING_SNAKE_CASE__					,							max_new_tokens=1_0					,							do_sample=SCREAMING_SNAKE_CASE__					)
             a_ :		int							=  tokenizer.decode(greedy_ids[0]					)

             a_ :		Union[str, Any]							=  TextIteratorStreamer(SCREAMING_SNAKE_CASE__					)
             a_ :		List[str]							=  {'input_ids': input_ids, 'max_new_tokens': 1_0, 'do_sample': False, 'streamer': streamer}
             a_ :		Optional[int]							=  Thread(target=model.generate					,							kwargs=SCREAMING_SNAKE_CASE__					)
             thread.start()
             a_ :		Tuple							=  ''
             for new_text in streamer:
                    streamer_text += new_text

             self.assertEqual(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      List[Any]:
             a_ :		str							=  AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'					)
             a_ :		List[str]							=  AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'					).to(SCREAMING_SNAKE_CASE__					)
             a_ :		Optional[Any]							=  -1

             a_ :		List[Any]							=  ids_tensor((1, 5)					,							vocab_size=model.config.vocab_size					).to(SCREAMING_SNAKE_CASE__					)
             a_ :		List[Any]							=  model.generate(SCREAMING_SNAKE_CASE__					,							max_new_tokens=1_0					,							do_sample=SCREAMING_SNAKE_CASE__					)
             a_ :		Dict							=  greedy_ids[:, input_ids.shape[1] :]
             a_ :		Optional[int]							=  tokenizer.decode(new_greedy_ids[0]					)

             with CaptureStdout() as cs:
                    a_ :		int							=  TextStreamer(SCREAMING_SNAKE_CASE__					,							skip_prompt=SCREAMING_SNAKE_CASE__					)
                    model.generate(SCREAMING_SNAKE_CASE__					,							max_new_tokens=1_0					,							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
             a_ :		Tuple							=  cs.out[:-1]

             self.assertEqual(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      Dict:
             # 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_ :		Dict							=  AutoTokenizer.from_pretrained('distilgpt2'					)
             a_ :		List[Any]							=  AutoModelForCausalLM.from_pretrained('distilgpt2'					).to(SCREAMING_SNAKE_CASE__					)
             a_ :		str							=  -1

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

      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      Optional[int]:
             a_ :		Any							=  AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'					)
             a_ :		Optional[int]							=  AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'					).to(SCREAMING_SNAKE_CASE__					)
             a_ :		Optional[int]							=  -1

             a_ :		int							=  ids_tensor((1, 5)					,							vocab_size=model.config.vocab_size					).to(SCREAMING_SNAKE_CASE__					)
             a_ :		List[Any]							=  TextIteratorStreamer(SCREAMING_SNAKE_CASE__					,							timeout=0.001					)
             a_ :		Tuple							=  {'input_ids': input_ids, 'max_new_tokens': 1_0, 'do_sample': False, 'streamer': streamer}
             a_ :		Tuple							=  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__					):
                    a_ :		Dict							=  ''
                    for new_text in streamer:
                           streamer_text += new_text






def 	SCREAMING_SNAKE_CASE_						(      __A			:      list    )     ->		list:

       """simple docstring"""

       a_ :		int							=  len(__A    )
       for _ in range(__A    ):
              for i in range(_ % 2  ,   arr_size - 1  ,   2    ):
                     if arr[i + 1] < arr[i]:
                            a_							,		a_ :		int							=  arr[i + 1], arr[i]

       return arr


if __name__ == "__main__":
   UpperCAmelCase_				:			int   =	list(range(10, 0, -1))
   print(F'Original: {arr}. Sorted: {odd_even_transposition(arr)}')


