

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      ):
						A__							:   Tuple					       =		AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""      )
						A__							:   Dict					       =		AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2"""      ).to(A__      )
						A__							:   List[Any]					       =		-1

						A__							:   Optional[Any]					       =		ids_tensor((1, 5)   ,	vocab_size=model.config.vocab_size      ).to(A__      )
						A__							:   Dict					       =		model.generate(A__   ,	max_new_tokens=10   ,	do_sample=A__      )
						A__							:   Tuple					       =		tokenizer.decode(greedy_ids[0]      )

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

						self.assertEqual(A__   ,	A__      )






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

						A__							:   List[Any]					       =		ids_tensor((1, 5)   ,	vocab_size=model.config.vocab_size      ).to(A__      )
						A__							:   Any					       =		model.generate(A__   ,	max_new_tokens=10   ,	do_sample=A__      )
						A__							:   Any					       =		tokenizer.decode(greedy_ids[0]      )

						A__							:   List[Any]					       =		TextIteratorStreamer(A__      )
						A__							:   Optional[int]					       =		{"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
						A__							:   List[str]					       =		Thread(target=model.generate   ,	kwargs=A__      )
						thread.start()
						A__							:   List[Any]					       =		""""""
						for new_text in streamer:
							streamer_text += new_text

						self.assertEqual(A__   ,	A__      )






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

						A__							:   Optional[int]					       =		ids_tensor((1, 5)   ,	vocab_size=model.config.vocab_size      ).to(A__      )
						A__							:   Optional[int]					       =		model.generate(A__   ,	max_new_tokens=10   ,	do_sample=A__      )
						A__							:   Dict					       =		greedy_ids[:, input_ids.shape[1] :]
						A__							:   Tuple					       =		tokenizer.decode(new_greedy_ids[0]      )

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

						self.assertEqual(A__   ,	A__      )






					def   __A      (			self      ):
						# 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__							:   Tuple					       =		AutoTokenizer.from_pretrained("""distilgpt2"""      )
						A__							:   List[str]					       =		AutoModelForCausalLM.from_pretrained("""distilgpt2"""      ).to(A__      )
						A__							:   int					       =		-1

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

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






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

						A__							:   List[Any]					       =		ids_tensor((1, 5)   ,	vocab_size=model.config.vocab_size      ).to(A__      )
						A__							:   Union[str, Any]					       =		TextIteratorStreamer(A__   ,	timeout=0.0_0_1      )
						A__							:   Any					       =		{"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
						A__							:   Dict					       =		Thread(target=model.generate   ,	kwargs=A__      )
						thread.start()

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






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
	from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



@require_torch
@require_sentencepiece
@require_tokenizers
class    _a						(unittest.TestCase    ):


					'''simple docstring'''






					@slow
					def   __A      (			self      ):
						A__							:   Optional[Any]					       =		AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""   ,	return_dict=A__      ).to(A__      )
						A__							:   str					       =		AutoTokenizer.from_pretrained("""google/mt5-small"""      )

						A__							:   int					       =		tokenizer("""Hello there"""   ,	return_tensors="""pt"""      ).input_ids
						A__							:   List[Any]					       =		tokenizer("""Hi I am"""   ,	return_tensors="""pt"""      ).input_ids

						A__							:   Union[str, Any]					       =		model(input_ids.to(A__      )   ,	labels=labels.to(A__      )      ).loss
						A__							:   Union[str, Any]					       =		-(labels.shape[-1] * loss.item())

						A__							:   Any					       =		-8_4.9_1_2_7
						self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1e-4      )



