
'''simple docstring'''






import unittest

from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    XLMForMultipleChoice,
					    XLMForQuestionAnswering,
					    XLMForQuestionAnsweringSimple,
					    XLMForSequenceClassification,
					    XLMForTokenClassification,
					    XLMModel,
					    XLMWithLMHeadModel,
					)
					from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST




class     __snake_case		:
					def __init__(     self,    A,    A=13,    A=7,    A=True,    A=True,    A=True,    A=True,    A=True,    A=False,    A=False,    A=False,    A=2,    A=99,    A=0,    A=32,    A=5,    A=4,    A=0.1,    A=0.1,    A=512,    A=2,    A=0.02,    A=2,    A=4,    A="last",    A=True,    A=None,    A=0,    ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= parent
									lowerCamelCase					:			Union[str, Any]									= batch_size
									lowerCamelCase					:			List[str]									= seq_length
									lowerCamelCase					:			List[str]									= is_training
									lowerCamelCase					:			Optional[Any]									= use_input_lengths
									lowerCamelCase					:			Optional[Any]									= use_token_type_ids
									lowerCamelCase					:			str									= use_labels
									lowerCamelCase					:			Optional[int]									= gelu_activation
									lowerCamelCase					:			Union[str, Any]									= sinusoidal_embeddings
									lowerCamelCase					:			List[str]									= causal
									lowerCamelCase					:			Dict									= asm
									lowerCamelCase					:			Union[str, Any]									= n_langs
									lowerCamelCase					:			Optional[int]									= vocab_size
									lowerCamelCase					:			List[str]									= n_special
									lowerCamelCase					:			str									= hidden_size
									lowerCamelCase					:			Dict									= num_hidden_layers
									lowerCamelCase					:			List[Any]									= num_attention_heads
									lowerCamelCase					:			Union[str, Any]									= hidden_dropout_prob
									lowerCamelCase					:			int									= attention_probs_dropout_prob
									lowerCamelCase					:			Union[str, Any]									= max_position_embeddings
									lowerCamelCase					:			int									= type_sequence_label_size
									lowerCamelCase					:			List[str]									= initializer_range
									lowerCamelCase					:			Tuple									= num_labels
									lowerCamelCase					:			int									= num_choices
									lowerCamelCase					:			Dict									= summary_type
									lowerCamelCase					:			List[Any]									= use_proj
									lowerCamelCase					:			str									= scope
									lowerCamelCase					:			int									= bos_token_id
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= ids_tensor([self.batch_size, self.seq_length],    self.vocab_size      )
									lowerCamelCase					:			Union[str, Any]									= random_attention_mask([self.batch_size, self.seq_length]      )

									lowerCamelCase					:			List[Any]									= None
									if self.use_input_lengths:
													lowerCamelCase					:			Optional[int]									= (
													    ids_tensor([self.batch_size],    vocab_size=2      ) + self.seq_length - 2
													)  # small variation of seq_length

									lowerCamelCase					:			Tuple									= None
									if self.use_token_type_ids:
													lowerCamelCase					:			List[str]									= ids_tensor([self.batch_size, self.seq_length],    self.n_langs      )

									lowerCamelCase					:			Dict									= None
									lowerCamelCase					:			Optional[int]									= None
									lowerCamelCase					:			List[Any]									= None
									if self.use_labels:
													lowerCamelCase					:			Union[str, Any]									= ids_tensor([self.batch_size],    self.type_sequence_label_size      )
													lowerCamelCase					:			Tuple									= ids_tensor([self.batch_size, self.seq_length],    self.num_labels      )
													lowerCamelCase					:			List[str]									= ids_tensor([self.batch_size],    2      ).float()
													lowerCamelCase					:			List[str]									= ids_tensor([self.batch_size],    self.num_choices      )

									lowerCamelCase					:			int									= self.get_config()

									return (
									    config,
									    input_ids,
									    token_type_ids,
									    input_lengths,
									    sequence_labels,
									    token_labels,
									    is_impossible_labels,
									    choice_labels,
									    input_mask,
									)
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return XLMConfig(
									    vocab_size=self.vocab_size,    n_special=self.n_special,    emb_dim=self.hidden_size,    n_layers=self.num_hidden_layers,    n_heads=self.num_attention_heads,    dropout=self.hidden_dropout_prob,    attention_dropout=self.attention_probs_dropout_prob,    gelu_activation=self.gelu_activation,    sinusoidal_embeddings=self.sinusoidal_embeddings,    asm=self.asm,    causal=self.causal,    n_langs=self.n_langs,    max_position_embeddings=self.max_position_embeddings,    initializer_range=self.initializer_range,    summary_type=self.summary_type,    use_proj=self.use_proj,    num_labels=self.num_labels,    bos_token_id=self.bos_token_id,    )
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A,    A,    A,    A,    A,    A,    ):

									"""simple docstring"""


									lowerCamelCase					:			str									= XLMModel(config=A      )
									model.to(A      )
									model.eval()
									lowerCamelCase					:			str									= model(A,    lengths=A,    langs=A      )
									lowerCamelCase					:			str									= model(A,    langs=A      )
									lowerCamelCase					:			str									= model(A      )
									self.parent.assertEqual(result.last_hidden_state.shape,    (self.batch_size, self.seq_length, self.hidden_size)      )
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A,    A,    A,    A,    A,    A,    ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= XLMWithLMHeadModel(A      )
									model.to(A      )
									model.eval()

									lowerCamelCase					:			Tuple									= model(A,    token_type_ids=A,    labels=A      )
									self.parent.assertEqual(result.loss.shape,    ()      )
									self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.seq_length, self.vocab_size)      )
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A,    A,    A,    A,    A,    A,    ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= XLMForQuestionAnsweringSimple(A      )
									model.to(A      )
									model.eval()

									lowerCamelCase					:			Any									= model(A      )

									lowerCamelCase					:			Union[str, Any]									= model(A,    start_positions=A,    end_positions=A      )
									lowerCamelCase					:			Dict									= outputs
									self.parent.assertEqual(result.start_logits.shape,    (self.batch_size, self.seq_length)      )
									self.parent.assertEqual(result.end_logits.shape,    (self.batch_size, self.seq_length)      )
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A,    A,    A,    A,    A,    A,    ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= XLMForQuestionAnswering(A      )
									model.to(A      )
									model.eval()

									lowerCamelCase					:			Dict									= model(A      )

									lowerCamelCase					:			Tuple									= model(
									    A,    start_positions=A,    end_positions=A,    cls_index=A,    is_impossible=A,    p_mask=A,    )

									lowerCamelCase					:			List[str]									= model(
									    A,    start_positions=A,    end_positions=A,    cls_index=A,    is_impossible=A,    )

									((lowerCamelCase)			, )					:			Tuple									= result_with_labels.to_tuple()

									lowerCamelCase					:			Tuple									= model(A,    start_positions=A,    end_positions=A      )

									((lowerCamelCase)			, )					:			Optional[int]									= result_with_labels.to_tuple()

									self.parent.assertEqual(result_with_labels.loss.shape,    ()      )
									self.parent.assertEqual(result.start_top_log_probs.shape,    (self.batch_size, model.config.start_n_top)      )
									self.parent.assertEqual(result.start_top_index.shape,    (self.batch_size, model.config.start_n_top)      )
									self.parent.assertEqual(
									    result.end_top_log_probs.shape,    (self.batch_size, model.config.start_n_top * model.config.end_n_top)      )
									self.parent.assertEqual(
									    result.end_top_index.shape,    (self.batch_size, model.config.start_n_top * model.config.end_n_top)      )
									self.parent.assertEqual(result.cls_logits.shape,    (self.batch_size,)      )
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A,    A,    A,    A,    A,    A,    ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= XLMForSequenceClassification(A      )
									model.to(A      )
									model.eval()

									lowerCamelCase					:			int									= model(A      )
									lowerCamelCase					:			Any									= model(A,    labels=A      )
									self.parent.assertEqual(result.loss.shape,    ()      )
									self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.type_sequence_label_size)      )
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A,    A,    A,    A,    A,    A,    ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= self.num_labels
									lowerCamelCase					:			Dict									= XLMForTokenClassification(A      )
									model.to(A      )
									model.eval()

									lowerCamelCase					:			str									= model(A,    attention_mask=A,    labels=A      )
									self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.seq_length, self.num_labels)      )
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A,    A,    A,    A,    A,    A,    ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= self.num_choices
									lowerCamelCase					:			str									= XLMForMultipleChoice(config=A      )
									model.to(A      )
									model.eval()
									lowerCamelCase					:			int									= input_ids.unsqueeze(1      ).expand(-1,    self.num_choices,    -1      ).contiguous()
									lowerCamelCase					:			List[str]									= token_type_ids.unsqueeze(1      ).expand(-1,    self.num_choices,    -1      ).contiguous()
									lowerCamelCase					:			Tuple									= input_mask.unsqueeze(1      ).expand(-1,    self.num_choices,    -1      ).contiguous()
									lowerCamelCase					:			Any									= model(
									    A,    attention_mask=A,    token_type_ids=A,    labels=A,    )
									self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.num_choices)      )




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= self.prepare_config_and_inputs()
									(
									    (
									    lowerCamelCase
									)			, (
									    lowerCamelCase
									)			, (
									    lowerCamelCase
									)			, (
									    lowerCamelCase
									)			, (
									    lowerCamelCase
									)			, (
									    lowerCamelCase
									)			, (
									    lowerCamelCase
									)			, (
									    lowerCamelCase
									)			, (
									    lowerCamelCase
									)			, 
									)					:			str									= config_and_inputs
									lowerCamelCase					:			Union[str, Any]									= {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
									return config, inputs_dict





@require_torch
class     __snake_case		(   a__    ,   a__    ,   a__    ,   unittest.TestCase):
					_lowerCAmelCase			     =	(
					    (
					        XLMModel,
					        XLMWithLMHeadModel,
					        XLMForQuestionAnswering,
					        XLMForSequenceClassification,
					        XLMForQuestionAnsweringSimple,
					        XLMForTokenClassification,
					        XLMForMultipleChoice,
					    )
					    if is_torch_available()
					    else ()
					)
					_lowerCAmelCase			     =	(
					    (XLMWithLMHeadModel,) if is_torch_available() else ()
					)  # TODO (PVP): Check other models whether language generation is also applicable
					_lowerCAmelCase			     =	(
					    {
					        '''feature-extraction''': XLMModel,
					        '''fill-mask''': XLMWithLMHeadModel,
					        '''question-answering''': XLMForQuestionAnsweringSimple,
					        '''text-classification''': XLMForSequenceClassification,
					        '''text-generation''': XLMWithLMHeadModel,
					        '''token-classification''': XLMForTokenClassification,
					        '''zero-shot''': XLMForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A,    A      ):

									"""simple docstring"""


									if (
									    pipeline_test_casse_name == "QAPipelineTests"
									    and tokenizer_name is not None
									    and not tokenizer_name.endswith('Fast'      )
									):
													# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
													# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
													# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
													return True

									return False
					def     UpperCAmelCase_							(     self,    A,    A,    A=False      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= super()._prepare_for_class(A,    A,    return_labels=A      )

									if return_labels:
													if model_class.__name__ == "XLMForQuestionAnswering":
																	lowerCamelCase					:			Any									= torch.zeros(
																	    self.model_tester.batch_size,    dtype=torch.long,    device=A      )
																	lowerCamelCase					:			Tuple									= torch.zeros(
																	    self.model_tester.batch_size,    dtype=torch.long,    device=A      )

									return inputs_dict
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= XLMModelTester(self      )
									lowerCamelCase					:			int									= ConfigTester(self,    config_class=A,    emb_dim=37      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									self.config_tester.run_common_tests()
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_model(*A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_lm_head(*A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_simple_qa(*A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_qa(*A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_sequence_classif(*A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_token_classif(*A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_xlm_for_multiple_choice(*A      )
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A,    A,    A=False,    A=1      ):

									"""simple docstring"""


									self.assertIsInstance(A,    A      )
									self.assertListEqual(
									    [isinstance(A,    A      ) for iter_attentions in attentions],    [True] * len(A      )      )
									self.assertEqual(len(A      ),    (max_length - min_length) * num_beam_groups      )

									for idx, iter_attentions in enumerate(A      ):
													# adds PAD dummy token
													lowerCamelCase					:			str									= min_length + idx + 1
													lowerCamelCase					:			Dict									= min_length + idx + 1

													lowerCamelCase					:			Optional[int]									= (
													    batch_size * num_beam_groups,
													    config.num_attention_heads,
													    tgt_len,
													    src_len,
													)
													# check attn size
													self.assertListEqual(
													    [layer_attention.shape for layer_attention in iter_attentions],    [expected_shape] * len(A      )      )
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A,    A,    A=False,    A=1      ):

									"""simple docstring"""


									self.assertIsInstance(A,    A      )
									self.assertListEqual(
									    [isinstance(A,    A      ) for iter_hidden_states in hidden_states],    [True] * len(A      ),    )
									self.assertEqual(len(A      ),    (max_length - min_length) * num_beam_groups      )

									for idx, iter_hidden_states in enumerate(A      ):
													# adds PAD dummy token
													lowerCamelCase					:			List[str]									= min_length + idx + 1
													lowerCamelCase					:			int									= (batch_size * num_beam_groups, seq_len, config.hidden_size)
													# check hidden size
													self.assertListEqual(
													    [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states],    [expected_shape] * len(A      ),    )
									pass




					@slow
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCamelCase					:			Optional[Any]									= XLMModel.from_pretrained(A      )
													self.assertIsNotNone(A      )





@require_torch
class     __snake_case		(   unittest.TestCase):
					@slow
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'      )
									model.to(A      )
									lowerCamelCase					:			Union[str, Any]									= torch.tensor([[14, 447]],    dtype=torch.long,    device=A      )  # the president
									lowerCamelCase					:			int									= [
									    14,
									    447,
									    14,
									    447,
									    14,
									    447,
									    14,
									    447,
									    14,
									    447,
									    14,
									    447,
									    14,
									    447,
									    14,
									    447,
									    14,
									    447,
									    14,
									    447,
									]  # the president the president the president the president the president the president the president the president the president the president
									# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
									lowerCamelCase					:			List[str]									= model.generate(A,    do_sample=A      )
									self.assertListEqual(output_ids[0].cpu().numpy().tolist(),    A      )


'''simple docstring'''






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):
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'      )
									lowerCamelCase					:			Optional[int]									= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'      ).to(A      )
									lowerCamelCase					:			Union[str, Any]									= -1

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

									with CaptureStdout() as cs:
													lowerCamelCase					:			Optional[int]									= 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
									lowerCamelCase					:			int									= cs.out[:-1]

									self.assertEqual(A,    A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'      )
									lowerCamelCase					:			Dict									= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'      ).to(A      )
									lowerCamelCase					:			List[Any]									= -1

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

									lowerCamelCase					:			Union[str, Any]									= TextIteratorStreamer(A      )
									lowerCamelCase					:			Tuple									= {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
									lowerCamelCase					:			List[Any]									= Thread(target=model.generate,    kwargs=A      )
									thread.start()
									lowerCamelCase					:			List[Any]									= ''
									for new_text in streamer:
													streamer_text += new_text

									self.assertEqual(A,    A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'      )
									lowerCamelCase					:			int									= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'      ).to(A      )
									lowerCamelCase					:			Optional[Any]									= -1

									lowerCamelCase					:			Union[str, Any]									= ids_tensor((1, 5),    vocab_size=model.config.vocab_size      ).to(A      )
									lowerCamelCase					:			Union[str, Any]									= model.generate(A,    max_new_tokens=10,    do_sample=A      )
									lowerCamelCase					:			int									= greedy_ids[:, input_ids.shape[1] :]
									lowerCamelCase					:			Dict									= tokenizer.decode(new_greedy_ids[0]      )

									with CaptureStdout() as cs:
													lowerCamelCase					:			Tuple									= 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
									lowerCamelCase					:			Optional[int]									= cs.out[:-1]

									self.assertEqual(A,    A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= AutoTokenizer.from_pretrained('distilgpt2'      )
									lowerCamelCase					:			Dict									= AutoModelForCausalLM.from_pretrained('distilgpt2'      ).to(A      )
									lowerCamelCase					:			Dict									= -1

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




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'      )
									lowerCamelCase					:			List[str]									= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'      ).to(A      )
									lowerCamelCase					:			Any									= -1

									lowerCamelCase					:			int									= ids_tensor((1, 5),    vocab_size=model.config.vocab_size      ).to(A      )
									lowerCamelCase					:			int									= TextIteratorStreamer(A,    timeout=0.001      )
									lowerCamelCase					:			Any									= {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
									lowerCamelCase					:			Optional[Any]									= 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      ):
													lowerCamelCase					:			Dict									= ''
													for new_text in streamer:
																	streamer_text += new_text
