





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 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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

				lowerCamelCase__								=		ids_tensor((1, 5)       ,  vocab_size=model.config.vocab_size							).to(__lowerCAmelCase							)
				lowerCamelCase__								=		model.generate(__lowerCAmelCase       ,  max_new_tokens=1_0       ,  do_sample=__lowerCAmelCase							)
				lowerCamelCase__								=		tokenizer.decode(greedy_ids[0]							)

				with CaptureStdout() as cs:
					lowerCamelCase__								=		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
				lowerCamelCase__								=		cs.out[:-1]

				self.assertEqual(__lowerCAmelCase       ,  __lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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

				lowerCamelCase__								=		ids_tensor((1, 5)       ,  vocab_size=model.config.vocab_size							).to(__lowerCAmelCase							)
				lowerCamelCase__								=		model.generate(__lowerCAmelCase       ,  max_new_tokens=1_0       ,  do_sample=__lowerCAmelCase							)
				lowerCamelCase__								=		tokenizer.decode(greedy_ids[0]							)

				lowerCamelCase__								=		TextIteratorStreamer(__lowerCAmelCase							)
				lowerCamelCase__								=		{'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
				lowerCamelCase__								=		Thread(target=model.generate       ,  kwargs=__lowerCAmelCase							)
				thread.start()
				lowerCamelCase__								=		''''''
				for new_text in streamer:
					streamer_text += new_text

				self.assertEqual(__lowerCAmelCase       ,  __lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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

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

				with CaptureStdout() as cs:
					lowerCamelCase__								=		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
				lowerCamelCase__								=		cs.out[:-1]

				self.assertEqual(__lowerCAmelCase       ,  __lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		AutoTokenizer.from_pretrained('''distilgpt2'''							)
				lowerCamelCase__								=		AutoModelForCausalLM.from_pretrained('''distilgpt2'''							).to(__lowerCAmelCase							)
				lowerCamelCase__								=		-1

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





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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

				lowerCamelCase__								=		ids_tensor((1, 5)       ,  vocab_size=model.config.vocab_size							).to(__lowerCAmelCase							)
				lowerCamelCase__								=		TextIteratorStreamer(__lowerCAmelCase       ,  timeout=0.001							)
				lowerCamelCase__								=		{'''input_ids''': input_ids, '''max_new_tokens''': 1_0, '''do_sample''': False, '''streamer''': streamer}
				lowerCamelCase__								=		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							):
					lowerCamelCase__								=		''''''
					for new_text in streamer:
						streamer_text += new_text










def lowerCAmelCase__(__snake_case							)				->      str:







	'''simple docstring'''







	return "".join(chr(ord(__snake_case							) - 32							) if '''a''' <= char <= '''z''' else char for char in word							)


if __name__ == "__main__":
			from doctest import testmod

			testmod()



