



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     ):






     """simple docstring"""


     def    lowercase_   (       self  :  List[str]					)   ->Union[str, Any]:
            snake_case__							:	Dict   	=	AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'					)
            snake_case__							:	int   	=	AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'					).to(__lowerCAmelCase					)
            snake_case__							:	Union[str, Any]   	=	-1

            snake_case__							:	Dict   	=	ids_tensor((1, 5), vocab_size=model.config.vocab_size					).to(__lowerCAmelCase					)
            snake_case__							:	int   	=	model.generate(__lowerCAmelCase, max_new_tokens=1_0, do_sample=__lowerCAmelCase					)
            snake_case__							:	List[str]   	=	tokenizer.decode(greedy_ids[0]					)

            with CaptureStdout() as cs:
                   snake_case__							:	List[str]   	=	TextStreamer(__lowerCAmelCase					)
                   model.generate(__lowerCAmelCase, max_new_tokens=1_0, do_sample=__lowerCAmelCase, streamer=__lowerCAmelCase					)
            # The greedy text should be printed to stdout, except for the final "\n" in the streamer
            snake_case__							:	int   	=	cs.out[:-1]

            self.assertEqual(__lowerCAmelCase, __lowerCAmelCase					)






     def    lowercase_   (       self  :  Dict					)   ->Union[str, Any]:
            snake_case__							:	Union[str, Any]   	=	AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'					)
            snake_case__							:	Optional[Any]   	=	AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'					).to(__lowerCAmelCase					)
            snake_case__							:	Optional[Any]   	=	-1

            snake_case__							:	List[str]   	=	ids_tensor((1, 5), vocab_size=model.config.vocab_size					).to(__lowerCAmelCase					)
            snake_case__							:	Tuple   	=	model.generate(__lowerCAmelCase, max_new_tokens=1_0, do_sample=__lowerCAmelCase					)
            snake_case__							:	Optional[int]   	=	tokenizer.decode(greedy_ids[0]					)

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

            self.assertEqual(__lowerCAmelCase, __lowerCAmelCase					)






     def    lowercase_   (       self  :  Tuple					)   ->Union[str, Any]:
            snake_case__							:	List[Any]   	=	AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'					)
            snake_case__							:	Any   	=	AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'					).to(__lowerCAmelCase					)
            snake_case__							:	Optional[int]   	=	-1

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

            with CaptureStdout() as cs:
                   snake_case__							:	str   	=	TextStreamer(__lowerCAmelCase, skip_prompt=__lowerCAmelCase					)
                   model.generate(__lowerCAmelCase, max_new_tokens=1_0, do_sample=__lowerCAmelCase, streamer=__lowerCAmelCase					)
            # The greedy text should be printed to stdout, except for the final "\n" in the streamer
            snake_case__							:	Optional[int]   	=	cs.out[:-1]

            self.assertEqual(__lowerCAmelCase, __lowerCAmelCase					)






     def    lowercase_   (       self  :  str					)   ->Union[str, Any]:
            snake_case__							:	Any   	=	AutoTokenizer.from_pretrained('distilgpt2'					)
            snake_case__							:	Any   	=	AutoModelForCausalLM.from_pretrained('distilgpt2'					).to(__lowerCAmelCase					)
            snake_case__							:	Optional[int]   	=	-1

            snake_case__							:	Union[str, Any]   	=	torch.ones((1, 5), device=__lowerCAmelCase					).long() * model.config.bos_token_id
            with CaptureStdout() as cs:
                   snake_case__							:	Optional[int]   	=	TextStreamer(__lowerCAmelCase, skip_special_tokens=__lowerCAmelCase					)
                   model.generate(__lowerCAmelCase, max_new_tokens=1, do_sample=__lowerCAmelCase, streamer=__lowerCAmelCase					)

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






     def    lowercase_   (       self  :  Tuple					)   ->Optional[int]:
            snake_case__							:	str   	=	AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'					)
            snake_case__							:	Tuple   	=	AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'					).to(__lowerCAmelCase					)
            snake_case__							:	List[Any]   	=	-1

            snake_case__							:	Any   	=	ids_tensor((1, 5), vocab_size=model.config.vocab_size					).to(__lowerCAmelCase					)
            snake_case__							:	str   	=	TextIteratorStreamer(__lowerCAmelCase, timeout=0.0_0_1					)
            snake_case__							:	List[Any]   	=	{'input_ids': input_ids, 'max_new_tokens': 1_0, 'do_sample': False, 'streamer': streamer}
            snake_case__							:	Dict   	=	Thread(target=model.generate, kwargs=__lowerCAmelCase					)
            thread.start()

            # The streamer will timeout after 0.001 seconds, so an exception will be raised
            with self.assertRaises(__lowerCAmelCase					):
                   snake_case__							:	List[str]   	=	''
                   for new_text in streamer:
                          streamer_text += new_text













def lowerCAmelCase__(__snake_case							)				->      list:







	'''simple docstring'''







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

	return arr


if __name__ == "__main__":
			_a 						=      list(range(10, 0, -1))
			print(f"""Original: {arr}. Sorted: {odd_even_transposition(arr)}""")



