
import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCAmelCase					:			Union[str, Any]			      =		get_tests_dir("fixtures/test_sentencepiece.model")


@require_sentencepiece
@require_tokenizers
class 						__lowerCAmelCase	(	lowerCAmelCase_ , unittest.TestCase      ):



      """simple docstring"""







      A__	:			Tuple        =    XLMRobertaTokenizer
      A__	:			Dict        =    XLMRobertaTokenizerFast
      A__	:			Tuple        =    True
      A__	:			Any        =    True




      def 		snake_case_			(      self		:       Optional[Any]				):
          super().setUp()

          # We have a SentencePiece fixture for testing
          __lowercase		:			List[str]	    =       XLMRobertaTokenizer(_snake_case				,       keep_accents=_snake_case				)
          tokenizer.save_pretrained(self.tmpdirname				)




      def 		snake_case_			(      self		:       int				):
          __lowercase		:			Any	    =       '''<pad>'''
          __lowercase		:			Optional[int]	    =       1

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(_snake_case				)				,       _snake_case				)
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(_snake_case				)				,       _snake_case				)




      def 		snake_case_			(      self		:       List[Any]				):
          __lowercase		:			Any	    =       list(self.get_tokenizer().get_vocab().keys()				)

          self.assertEqual(vocab_keys[0]				,       '''<s>'''				)
          self.assertEqual(vocab_keys[1]				,       '''<pad>'''				)
          self.assertEqual(vocab_keys[-1]				,       '''<mask>'''				)
          self.assertEqual(len(_snake_case				)				,       1002				)




      def 		snake_case_			(      self		:       str				):
          self.assertEqual(self.get_tokenizer().vocab_size				,       1002				)




      def 		snake_case_			(      self		:       str				):
          __lowercase		:			Tuple	    =       XLMRobertaTokenizer(_snake_case				,       keep_accents=_snake_case				)

          __lowercase		:			Any	    =       tokenizer.tokenize('''This is a test'''				)
          self.assertListEqual(_snake_case				,       ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(_snake_case				)				,       [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]				,       )

          __lowercase		:			Dict	    =       tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
          self.assertListEqual(
              _snake_case				,       [
                  SPIECE_UNDERLINE + '''I''',
                  SPIECE_UNDERLINE + '''was''',
                  SPIECE_UNDERLINE + '''b''',
                  '''or''',
                  '''n''',
                  SPIECE_UNDERLINE + '''in''',
                  SPIECE_UNDERLINE + '''''',
                  '''9''',
                  '''2''',
                  '''0''',
                  '''0''',
                  '''0''',
                  ''',''',
                  SPIECE_UNDERLINE + '''and''',
                  SPIECE_UNDERLINE + '''this''',
                  SPIECE_UNDERLINE + '''is''',
                  SPIECE_UNDERLINE + '''f''',
                  '''al''',
                  '''s''',
                  '''é''',
                  '''.''',
              ]				,       )
          __lowercase		:			str	    =       tokenizer.convert_tokens_to_ids(_snake_case				)
          self.assertListEqual(
              _snake_case				,       [
                  value + tokenizer.fairseq_offset
                  for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                  #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
              ]				,       )

          __lowercase		:			str	    =       tokenizer.convert_ids_to_tokens(_snake_case				)
          self.assertListEqual(
              _snake_case				,       [
                  SPIECE_UNDERLINE + '''I''',
                  SPIECE_UNDERLINE + '''was''',
                  SPIECE_UNDERLINE + '''b''',
                  '''or''',
                  '''n''',
                  SPIECE_UNDERLINE + '''in''',
                  SPIECE_UNDERLINE + '''''',
                  '''<unk>''',
                  '''2''',
                  '''0''',
                  '''0''',
                  '''0''',
                  ''',''',
                  SPIECE_UNDERLINE + '''and''',
                  SPIECE_UNDERLINE + '''this''',
                  SPIECE_UNDERLINE + '''is''',
                  SPIECE_UNDERLINE + '''f''',
                  '''al''',
                  '''s''',
                  '''<unk>''',
                  '''.''',
              ]				,       )




      def 		snake_case_			(      self		:       Dict				):
          if not self.test_slow_tokenizer:
              # as we don't have a slow version, we can't compare the outputs between slow and fast versions
              return

          __lowercase		:			Dict	    =       (self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'				):
                  __lowercase		:			Optional[int]	    =       self.rust_tokenizer_class.from_pretrained(_snake_case				,       **_snake_case				)
                  __lowercase		:			str	    =       self.tokenizer_class.from_pretrained(_snake_case				,       **_snake_case				)

                  __lowercase		:			Any	    =       tempfile.mkdtemp()

                  __lowercase		:			Optional[Any]	    =       tokenizer_r.save_pretrained(_snake_case				)
                  __lowercase		:			List[Any]	    =       tokenizer_p.save_pretrained(_snake_case				)

                  # Checks it save with the same files + the tokenizer.json file for the fast one
                  self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files				)				)
                  __lowercase		:			List[str]	    =       tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f				)
                  self.assertSequenceEqual(_snake_case				,       _snake_case				)

                  # Checks everything loads correctly in the same way
                  __lowercase		:			Any	    =       tokenizer_r.from_pretrained(_snake_case				)
                  __lowercase		:			List[Any]	    =       tokenizer_p.from_pretrained(_snake_case				)

                  # Check special tokens are set accordingly on Rust and Python
                  for key in tokenizer_pp.special_tokens_map:
                      self.assertTrue(hasattr(_snake_case				,       _snake_case				)				)
                      # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                      # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                  shutil.rmtree(_snake_case				)

                  # Save tokenizer rust, legacy_format=True
                  __lowercase		:			List[Any]	    =       tempfile.mkdtemp()

                  __lowercase		:			int	    =       tokenizer_r.save_pretrained(_snake_case				,       legacy_format=_snake_case				)
                  __lowercase		:			List[Any]	    =       tokenizer_p.save_pretrained(_snake_case				)

                  # Checks it save with the same files
                  self.assertSequenceEqual(_snake_case				,       _snake_case				)

                  # Checks everything loads correctly in the same way
                  __lowercase		:			Tuple	    =       tokenizer_r.from_pretrained(_snake_case				)
                  __lowercase		:			List[str]	    =       tokenizer_p.from_pretrained(_snake_case				)

                  # Check special tokens are set accordingly on Rust and Python
                  for key in tokenizer_pp.special_tokens_map:
                      self.assertTrue(hasattr(_snake_case				,       _snake_case				)				)

                  shutil.rmtree(_snake_case				)

                  # Save tokenizer rust, legacy_format=False
                  __lowercase		:			Dict	    =       tempfile.mkdtemp()

                  __lowercase		:			Dict	    =       tokenizer_r.save_pretrained(_snake_case				,       legacy_format=_snake_case				)
                  __lowercase		:			List[str]	    =       tokenizer_p.save_pretrained(_snake_case				)

                  # Checks it saved the tokenizer.json file
                  self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files				)				)

                  # Checks everything loads correctly in the same way
                  __lowercase		:			Optional[int]	    =       tokenizer_r.from_pretrained(_snake_case				)
                  __lowercase		:			Dict	    =       tokenizer_p.from_pretrained(_snake_case				)

                  # Check special tokens are set accordingly on Rust and Python
                  for key in tokenizer_pp.special_tokens_map:
                      self.assertTrue(hasattr(_snake_case				,       _snake_case				)				)

                  shutil.rmtree(_snake_case				)




      @cached_property
      def 		snake_case_			(      self		:       str				):
          return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base'''				)




      def 		snake_case_			(      self		:       List[Any]				):
          with tempfile.NamedTemporaryFile() as f:
              shutil.copyfile(_snake_case				,       f.name				)
              __lowercase		:			str	    =       XLMRobertaTokenizer(f.name				,       keep_accents=_snake_case				)
              __lowercase		:			Optional[int]	    =       pickle.dumps(_snake_case				)
          pickle.loads(_snake_case				)




      def 		snake_case_			(      self		:       Any				):
          if not self.test_rust_tokenizer:
              return

          __lowercase		:			Tuple	    =       self.get_tokenizer()
          __lowercase		:			int	    =       self.get_rust_tokenizer()

          __lowercase		:			str	    =       '''I was born in 92000, and this is falsé.'''

          __lowercase		:			List[Any]	    =       tokenizer.tokenize(_snake_case				)
          __lowercase		:			Optional[Any]	    =       rust_tokenizer.tokenize(_snake_case				)
          self.assertListEqual(_snake_case				,       _snake_case				)

          __lowercase		:			str	    =       tokenizer.encode(_snake_case				,       add_special_tokens=_snake_case				)
          __lowercase		:			Optional[int]	    =       rust_tokenizer.encode(_snake_case				,       add_special_tokens=_snake_case				)
          self.assertListEqual(_snake_case				,       _snake_case				)

          __lowercase		:			List[Any]	    =       self.get_rust_tokenizer()
          __lowercase		:			Optional[Any]	    =       tokenizer.encode(_snake_case				)
          __lowercase		:			Optional[Any]	    =       rust_tokenizer.encode(_snake_case				)
          self.assertListEqual(_snake_case				,       _snake_case				)




      @slow
      def 		snake_case_			(      self		:       int				):
          __lowercase		:			Optional[int]	    =       '''Hello World!'''
          __lowercase		:			Dict	    =       [0, 3_5378, 6661, 38, 2]
          # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
          # xlmr.eval()
          # xlmr.encode(symbols)

          self.assertListEqual(_snake_case				,       self.big_tokenizer.encode(_snake_case				)				)




      @slow
      def 		snake_case_			(      self		:       str				):
          __lowercase		:			Optional[int]	    =       (
              '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
              ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
          )
          __lowercase		:			str	    =       [
              0,
              3293,
              83,
              10,
              4552,
              4989,
              7986,
              678,
              10,
              5915,
              111,
              17_9459,
              12_4850,
              4,
              6044,
              237,
              12,
              6,
              5,
              6,
              4,
              6780,
              705,
              15,
              1388,
              44,
              378,
              1_0114,
              711,
              152,
              20,
              6,
              5,
              2_2376,
              642,
              1221,
              1_5190,
              3_4153,
              450,
              5608,
              959,
              1119,
              5_7702,
              136,
              186,
              47,
              1098,
              2_9367,
              47,
              # 4426, # What fairseq tokenizes from "<unk>": "_<"
              # 3678, # What fairseq tokenizes from "<unk>": "unk"
              # 2740, # What fairseq tokenizes from "<unk>": ">"
              3,  # What we tokenize from "<unk>": "<unk>"
              6,  # Residue from the tokenization: an extra sentencepiece underline
              4,
              6044,
              237,
              6284,
              5_0901,
              528,
              31,
              90,
              34,
              927,
              2,
          ]
          # xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
          # xlmr.eval()
          # xlmr.encode(symbols)

          self.assertListEqual(_snake_case				,       self.big_tokenizer.encode(_snake_case				)				)




      @slow
      def 		snake_case_			(      self		:       int				):
          # fmt: off
          __lowercase		:			List[str]	    =       {'''input_ids''': [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
          # fmt: on

          self.tokenizer_integration_test_util(
              expected_encoding=_snake_case				,       model_name='''xlm-roberta-base'''				,       revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'''				,       )




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 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      def 		snake_case_			(      self		:       Union[str, Any]				):
          __lowercase		:			Optional[Any]	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
          __lowercase		:			Any	    =       AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(_snake_case				)
          __lowercase		:			List[str]	    =       -1

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

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

          self.assertEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       Optional[int]				):
          __lowercase		:			Optional[int]	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
          __lowercase		:			Optional[Any]	    =       AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(_snake_case				)
          __lowercase		:			List[str]	    =       -1

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

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

          self.assertEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       List[str]				):
          __lowercase		:			Dict	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
          __lowercase		:			int	    =       AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(_snake_case				)
          __lowercase		:			List[Any]	    =       -1

          __lowercase		:			List[Any]	    =       ids_tensor((1, 5)				,       vocab_size=model.config.vocab_size				).to(_snake_case				)
          __lowercase		:			str	    =       model.generate(_snake_case				,       max_new_tokens=10				,       do_sample=_snake_case				)
          __lowercase		:			List[Any]	    =       greedy_ids[:, input_ids.shape[1] :]
          __lowercase		:			Optional[Any]	    =       tokenizer.decode(new_greedy_ids[0]				)

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

          self.assertEqual(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       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
          __lowercase		:			Dict	    =       AutoTokenizer.from_pretrained('''distilgpt2'''				)
          __lowercase		:			List[str]	    =       AutoModelForCausalLM.from_pretrained('''distilgpt2'''				).to(_snake_case				)
          __lowercase		:			str	    =       -1

          __lowercase		:			Dict	    =       torch.ones((1, 5)				,       device=_snake_case				).long() * model.config.bos_token_id
          with CaptureStdout() as cs:
              __lowercase		:			Optional[Any]	    =       TextStreamer(_snake_case				,       skip_special_tokens=_snake_case				)
              model.generate(_snake_case				,       max_new_tokens=1				,       do_sample=_snake_case				,       streamer=_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
          __lowercase		:			Dict	    =       cs.out[:-1]  # Remove the final "\n"
          __lowercase		:			Any	    =       tokenizer(_snake_case				,       return_tensors='''pt'''				)
          self.assertEqual(streamer_text_tokenized.input_ids.shape				,       (1, 1)				)




      def 		snake_case_			(      self		:       Any				):
          __lowercase		:			List[str]	    =       AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				)
          __lowercase		:			Union[str, Any]	    =       AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''				).to(_snake_case				)
          __lowercase		:			Dict	    =       -1

          __lowercase		:			List[Any]	    =       ids_tensor((1, 5)				,       vocab_size=model.config.vocab_size				).to(_snake_case				)
          __lowercase		:			Tuple	    =       TextIteratorStreamer(_snake_case				,       timeout=0.0_01				)
          __lowercase		:			Tuple	    =       {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
          __lowercase		:			Optional[int]	    =       Thread(target=model.generate				,       kwargs=_snake_case				)
          thread.start()

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


