



import math
import os
import unittest

from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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 (
				    MODEL_FOR_PRETRAINING_MAPPING,
				    MegatronBertForCausalLM,
				    MegatronBertForMaskedLM,
				    MegatronBertForMultipleChoice,
				    MegatronBertForNextSentencePrediction,
				    MegatronBertForPreTraining,
				    MegatronBertForQuestionAnswering,
				    MegatronBertForSequenceClassification,
				    MegatronBertForTokenClassification,
				    MegatronBertModel,
				)



class        lowerCAmelCase__					:


				def __init__(							self      :  List[Any]						, __UpperCamelCase      :  Optional[Any]						, __UpperCamelCase      :  Optional[int]=13						, __UpperCamelCase      :  int=7						, __UpperCamelCase      :  List[Any]=True						, __UpperCamelCase      :  Union[str, Any]=True						, __UpperCamelCase      :  Dict=True						, __UpperCamelCase      :  str=True						, __UpperCamelCase      :  Optional[Any]=99						, __UpperCamelCase      :  List[str]=64						, __UpperCamelCase      :  Optional[int]=32						, __UpperCamelCase      :  Tuple=5						, __UpperCamelCase      :  int=4						, __UpperCamelCase      :  Any=37						, __UpperCamelCase      :  Optional[int]="gelu"						, __UpperCamelCase      :  int=0.1						, __UpperCamelCase      :  Union[str, Any]=0.1						, __UpperCamelCase      :  List[str]=512						, __UpperCamelCase      :  Any=16						, __UpperCamelCase      :  Optional[Any]=2						, __UpperCamelCase      :  Optional[int]=0.0_2						, __UpperCamelCase      :  int=3						, __UpperCamelCase      :  List[Any]=4						, __UpperCamelCase      :  List[str]=None						, )		->     List[Any]:
											A      						=    parent
											A      						=    batch_size
											A      						=    seq_length
											A      						=    is_training
											A      						=    use_input_mask
											A      						=    use_token_type_ids
											A      						=    use_labels
											A      						=    vocab_size
											A      						=    hidden_size
											A      						=    embedding_size
											A      						=    num_hidden_layers
											A      						=    num_attention_heads
											A      						=    intermediate_size
											A      						=    hidden_act
											A      						=    hidden_dropout_prob
											A      						=    attention_probs_dropout_prob
											A      						=    max_position_embeddings
											A      						=    type_vocab_size
											A      						=    type_sequence_label_size
											A      						=    initializer_range
											A      						=    num_labels
											A      						=    num_choices
											A      						=    scope


				def __UpperCamelCase						(							self      :  Dict       )		->     Optional[int]:
											A      						=    ids_tensor([self.batch_size, self.seq_length]						, self.vocab_size       )

											A      						=    None
											if self.use_input_mask:
																		A      						=    random_attention_mask([self.batch_size, self.seq_length]       )

											A      						=    None
											if self.use_token_type_ids:
																		A      						=    ids_tensor([self.batch_size, self.seq_length]						, self.type_vocab_size       )

											A      						=    None
											A      						=    None
											A      						=    None
											if self.use_labels:
																		A      						=    ids_tensor([self.batch_size]						, self.type_sequence_label_size       )
																		A      						=    ids_tensor([self.batch_size, self.seq_length]						, self.num_labels       )
																		A      						=    ids_tensor([self.batch_size]						, self.num_choices       )

											A      						=    self.get_config()

											return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


				def __UpperCamelCase						(							self      :  List[Any]       )		->     List[Any]:
											return MegatronBertConfig(
											    vocab_size=self.vocab_size						, hidden_size=self.hidden_size						, num_hidden_layers=self.num_hidden_layers						, num_attention_heads=self.num_attention_heads						, intermediate_size=self.intermediate_size						, embedding_size=self.embedding_size						, hidden_act=self.hidden_act						, hidden_dropout_prob=self.hidden_dropout_prob						, attention_probs_dropout_prob=self.attention_probs_dropout_prob						, max_position_embeddings=self.max_position_embeddings						, type_vocab_size=self.type_vocab_size						, is_decoder=__UpperCamelCase						, initializer_range=self.initializer_range						, )


				def __UpperCamelCase						(							self      :  Tuple						, __UpperCamelCase      :  Optional[Any]						, __UpperCamelCase      :  Union[str, Any]						, __UpperCamelCase      :  str						, __UpperCamelCase      :  List[str]						, __UpperCamelCase      :  List[Any]						, __UpperCamelCase      :  Any						, __UpperCamelCase      :  Optional[Any]       )		->     Optional[int]:
											A      						=    MegatronBertModel(config=__UpperCamelCase       )
											model.to(__UpperCamelCase       )
											model.eval()
											A      						=    model(__UpperCamelCase						, attention_mask=__UpperCamelCase						, token_type_ids=__UpperCamelCase       )
											A      						=    model(__UpperCamelCase						, token_type_ids=__UpperCamelCase       )
											A      						=    model(__UpperCamelCase       )

											self.parent.assertEqual(result.last_hidden_state.shape						, (self.batch_size, self.seq_length, self.hidden_size)       )
											self.parent.assertEqual(result.pooler_output.shape						, (self.batch_size, self.hidden_size)       )


				def __UpperCamelCase						(							self      :  Optional[int]						, __UpperCamelCase      :  str						, __UpperCamelCase      :  List[str]						, __UpperCamelCase      :  Any						, __UpperCamelCase      :  Tuple						, __UpperCamelCase      :  int						, __UpperCamelCase      :  Optional[Any]						, __UpperCamelCase      :  Tuple       )		->     Union[str, Any]:
											A      						=    MegatronBertForMaskedLM(config=__UpperCamelCase       )
											model.to(__UpperCamelCase       )
											model.eval()
											A      						=    model(__UpperCamelCase						, attention_mask=__UpperCamelCase						, token_type_ids=__UpperCamelCase						, labels=__UpperCamelCase       )
											self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.seq_length, self.vocab_size)       )


				def __UpperCamelCase						(							self      :  Optional[Any]						, __UpperCamelCase      :  Union[str, Any]						, __UpperCamelCase      :  Optional[int]						, __UpperCamelCase      :  str						, __UpperCamelCase      :  List[str]						, __UpperCamelCase      :  str						, __UpperCamelCase      :  List[str]						, __UpperCamelCase      :  Optional[Any]       )		->     int:
											A      						=    MegatronBertForCausalLM(config=__UpperCamelCase       )
											model.to(__UpperCamelCase       )
											model.eval()
											A      						=    model(__UpperCamelCase						, attention_mask=__UpperCamelCase						, token_type_ids=__UpperCamelCase						, labels=__UpperCamelCase       )
											self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.seq_length, self.vocab_size)       )


				def __UpperCamelCase						(							self      :  List[Any]						, __UpperCamelCase      :  Union[str, Any]						, __UpperCamelCase      :  Union[str, Any]						, __UpperCamelCase      :  List[Any]						, __UpperCamelCase      :  List[Any]						, __UpperCamelCase      :  Dict						, __UpperCamelCase      :  Tuple						, __UpperCamelCase      :  List[str]       )		->     Any:
											A      						=    MegatronBertForNextSentencePrediction(config=__UpperCamelCase       )
											model.to(__UpperCamelCase       )
											model.eval()
											A      						=    model(
											    __UpperCamelCase						, attention_mask=__UpperCamelCase						, token_type_ids=__UpperCamelCase						, labels=__UpperCamelCase						, )
											self.parent.assertEqual(result.logits.shape						, (self.batch_size, 2)       )


				def __UpperCamelCase						(							self      :  List[Any]						, __UpperCamelCase      :  Optional[Any]						, __UpperCamelCase      :  List[str]						, __UpperCamelCase      :  Optional[Any]						, __UpperCamelCase      :  Optional[int]						, __UpperCamelCase      :  Optional[int]						, __UpperCamelCase      :  str						, __UpperCamelCase      :  List[Any]       )		->     Optional[Any]:
											A      						=    MegatronBertForPreTraining(config=__UpperCamelCase       )
											model.to(__UpperCamelCase       )
											model.eval()
											A      						=    model(
											    __UpperCamelCase						, attention_mask=__UpperCamelCase						, token_type_ids=__UpperCamelCase						, labels=__UpperCamelCase						, next_sentence_label=__UpperCamelCase						, )
											self.parent.assertEqual(result.prediction_logits.shape						, (self.batch_size, self.seq_length, self.vocab_size)       )
											self.parent.assertEqual(result.seq_relationship_logits.shape						, (self.batch_size, 2)       )


				def __UpperCamelCase						(							self      :  List[Any]						, __UpperCamelCase      :  Optional[int]						, __UpperCamelCase      :  Union[str, Any]						, __UpperCamelCase      :  List[Any]						, __UpperCamelCase      :  str						, __UpperCamelCase      :  int						, __UpperCamelCase      :  Union[str, Any]						, __UpperCamelCase      :  int       )		->     Union[str, Any]:
											A      						=    MegatronBertForQuestionAnswering(config=__UpperCamelCase       )
											model.to(__UpperCamelCase       )
											model.eval()
											A      						=    model(
											    __UpperCamelCase						, attention_mask=__UpperCamelCase						, token_type_ids=__UpperCamelCase						, start_positions=__UpperCamelCase						, end_positions=__UpperCamelCase						, )
											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      :  List[str]						, __UpperCamelCase      :  Union[str, Any]						, __UpperCamelCase      :  Any						, __UpperCamelCase      :  Dict						, __UpperCamelCase      :  Any						, __UpperCamelCase      :  Optional[Any]						, __UpperCamelCase      :  List[str]						, __UpperCamelCase      :  int       )		->     List[str]:
											A      						=    self.num_labels
											A      						=    MegatronBertForSequenceClassification(__UpperCamelCase       )
											model.to(__UpperCamelCase       )
											model.eval()
											A      						=    model(__UpperCamelCase						, attention_mask=__UpperCamelCase						, token_type_ids=__UpperCamelCase						, labels=__UpperCamelCase       )
											self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.num_labels)       )


				def __UpperCamelCase						(							self      :  Tuple						, __UpperCamelCase      :  Any						, __UpperCamelCase      :  Optional[Any]						, __UpperCamelCase      :  Any						, __UpperCamelCase      :  int						, __UpperCamelCase      :  int						, __UpperCamelCase      :  Dict						, __UpperCamelCase      :  List[Any]       )		->     Tuple:
											A      						=    self.num_labels
											A      						=    MegatronBertForTokenClassification(config=__UpperCamelCase       )
											model.to(__UpperCamelCase       )
											model.eval()
											A      						=    model(__UpperCamelCase						, attention_mask=__UpperCamelCase						, token_type_ids=__UpperCamelCase						, labels=__UpperCamelCase       )
											self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.seq_length, self.num_labels)       )


				def __UpperCamelCase						(							self      :  Optional[int]						, __UpperCamelCase      :  Optional[Any]						, __UpperCamelCase      :  int						, __UpperCamelCase      :  Optional[int]						, __UpperCamelCase      :  Union[str, Any]						, __UpperCamelCase      :  int						, __UpperCamelCase      :  Optional[int]						, __UpperCamelCase      :  List[Any]       )		->     List[Any]:
											A      						=    self.num_choices
											A      						=    MegatronBertForMultipleChoice(config=__UpperCamelCase       )
											model.to(__UpperCamelCase       )
											model.eval()
											A      						=    input_ids.unsqueeze(1       ).expand(-1						, self.num_choices						, -1       ).contiguous()
											A      						=    token_type_ids.unsqueeze(1       ).expand(-1						, self.num_choices						, -1       ).contiguous()
											A      						=    input_mask.unsqueeze(1       ).expand(-1						, self.num_choices						, -1       ).contiguous()
											A      						=    model(
											    __UpperCamelCase						, attention_mask=__UpperCamelCase						, token_type_ids=__UpperCamelCase						, labels=__UpperCamelCase						, )
											self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.num_choices)       )


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Dict:
											A      						=    self.prepare_config_and_inputs()
											(
											    (
											    A
											)						,   (
											    A
											)						,   (
											    A
											)						,   (
											    A
											)						,   (
											    A
											)						,   (
											    A
											)						,   (
											    A
											)						,   
											)      						=    config_and_inputs
											A      						=    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
											return config, inputs_dict





@require_torch
class        lowerCAmelCase__					(  _lowerCamelCase ,				_lowerCamelCase ,				unittest.TestCase       ):
				A_   :     Dict								=     (
				    (
				        MegatronBertModel,
				        MegatronBertForMaskedLM,
				        MegatronBertForCausalLM,
				        MegatronBertForMultipleChoice,
				        MegatronBertForNextSentencePrediction,
				        MegatronBertForPreTraining,
				        MegatronBertForQuestionAnswering,
				        MegatronBertForSequenceClassification,
				        MegatronBertForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				A_   :     Optional[Any]								=     (
				    {
				        'feature-extraction': MegatronBertModel,
				        'fill-mask': MegatronBertForMaskedLM,
				        'question-answering': MegatronBertForQuestionAnswering,
				        'text-classification': MegatronBertForSequenceClassification,
				        'text-generation': MegatronBertForCausalLM,
				        'token-classification': MegatronBertForTokenClassification,
				        'zero-shot': MegatronBertForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				A_   :     Optional[int]								=     True
				# test_resize_embeddings = False
				A_   :     Union[str, Any]								=     False


				def __UpperCamelCase						(							self      :  List[str]						, __UpperCamelCase      :  Optional[int]						, __UpperCamelCase      :  Any						, __UpperCamelCase      :  Tuple=False       )		->     Any:
											A      						=    super()._prepare_for_class(__UpperCamelCase						, __UpperCamelCase						, return_labels=__UpperCamelCase       )

											if return_labels:
																		if model_class in get_values(__UpperCamelCase       ):
																									A      						=    torch.zeros(
																									    (self.model_tester.batch_size, self.model_tester.seq_length)						, dtype=torch.long						, device=__UpperCamelCase       )
																									A      						=    torch.zeros(
																									    self.model_tester.batch_size						, dtype=torch.long						, device=__UpperCamelCase       )
											return inputs_dict


				def __UpperCamelCase						(							self      :  str       )		->     str:
											A      						=    MegatronBertModelTester(self       )
											A      						=    ConfigTester(self						, config_class=__UpperCamelCase						, hidden_size=37       )


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Tuple:
											self.config_tester.run_common_tests()


				def __UpperCamelCase						(							self      :  int       )		->     List[str]:
											A      						=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_megatron_bert_model(*__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Optional[int]       )		->     Union[str, Any]:
											A      						=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_megatron_bert_for_masked_lm(*__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     str:
											A      						=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  List[Any]       )		->     List[str]:
											A      						=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  List[str]       )		->     Tuple:
											A      						=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_megatron_bert_for_pretraining(*__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Any       )		->     Dict:
											A      						=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_megatron_bert_for_question_answering(*__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Dict       )		->     List[Any]:
											A      						=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     List[str]:
											A      						=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_megatron_bert_for_token_classification(*__UpperCamelCase       )






def  lowerCamelCase_	(      lowerCAmelCase__	:	Union[str, Any]				)       ->							Optional[int]:

							'''simple docstring'''






							return torch.tensor(
							    lowerCAmelCase__			,	dtype=torch.long			,	device=lowerCAmelCase__			,	)


__snake_case     :Optional[Any]			       =1E-4



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


				@slow
				@unittest.skip('Model is not available.'       )
				def __UpperCamelCase						(							self      :  Optional[int]       )		->     str:
											A      						=    'nvidia/megatron-bert-uncased-345m'
											if "MYDIR" in os.environ:
																		A      						=    os.path.join(os.environ['MYDIR']						, __UpperCamelCase       )
											A      						=    MegatronBertModel.from_pretrained(__UpperCamelCase       )
											model.to(__UpperCamelCase       )
											model.half()
											A      						=    _long_tensor([[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]]       )
											with torch.no_grad():
																		A      						=    model(__UpperCamelCase       )[0]
											A      						=    torch.Size((1, 9, 1_024)       )
											self.assertEqual(output.shape						, __UpperCamelCase       )

											A      						=    [-0.6_0_4_0, -0.2_5_1_7, -0.1_0_2_5, 0.3_4_2_0, -0.6_7_5_8, -0.0_0_1_7, -0.1_0_8_9, -0.1_9_9_0, 0.5_7_2_8]
											for ii in range(3       ):
																		for jj in range(3       ):
																									A      						=    output[0, ii, jj]
																									A      						=    expected[3 * ii + jj]
																									A      						=    'ii={} jj={} a={} b={}'.format(__UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase       )
																									self.assertTrue(math.isclose(__UpperCamelCase						, __UpperCamelCase						, rel_tol=__UpperCamelCase						, abs_tol=__UpperCamelCase       )						, msg=__UpperCamelCase       )




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       ):


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Optional[Any]:
											A      						=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    ids_tensor((1, 5)						, vocab_size=model.config.vocab_size       ).to(__UpperCamelCase       )
											A      						=    model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase       )
											A      						=    tokenizer.decode(greedy_ids[0]       )

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

											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  int       )		->     List[Any]:
											A      						=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    ids_tensor((1, 5)						, vocab_size=model.config.vocab_size       ).to(__UpperCamelCase       )
											A      						=    model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase       )
											A      						=    tokenizer.decode(greedy_ids[0]       )

											A      						=    TextIteratorStreamer(__UpperCamelCase       )
											A      						=    {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
											A      						=    Thread(target=model.generate						, kwargs=__UpperCamelCase       )
											thread.start()
											A      						=    ''
											for new_text in streamer:
																		streamer_text += new_text

											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  int       )		->     Tuple:
											A      						=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    ids_tensor((1, 5)						, vocab_size=model.config.vocab_size       ).to(__UpperCamelCase       )
											A      						=    model.generate(__UpperCamelCase						, max_new_tokens=10						, do_sample=__UpperCamelCase       )
											A      						=    greedy_ids[:, input_ids.shape[1] :]
											A      						=    tokenizer.decode(new_greedy_ids[0]       )

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

											self.assertEqual(__UpperCamelCase						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Tuple:
											# 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      						=    AutoTokenizer.from_pretrained('distilgpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('distilgpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    torch.ones((1, 5)						, device=__UpperCamelCase       ).long() * model.config.bos_token_id
											with CaptureStdout() as cs:
																		A      						=    TextStreamer(__UpperCamelCase						, skip_special_tokens=__UpperCamelCase       )
																		model.generate(__UpperCamelCase						, max_new_tokens=1						, do_sample=__UpperCamelCase						, streamer=__UpperCamelCase       )

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


				def __UpperCamelCase						(							self      :  Any       )		->     Dict:
											A      						=    AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'       )
											A      						=    AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'       ).to(__UpperCamelCase       )
											A      						=    -1

											A      						=    ids_tensor((1, 5)						, vocab_size=model.config.vocab_size       ).to(__UpperCamelCase       )
											A      						=    TextIteratorStreamer(__UpperCamelCase						, timeout=0.0_0_1       )
											A      						=    {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
											A      						=    Thread(target=model.generate						, kwargs=__UpperCamelCase       )
											thread.start()

											# The streamer will timeout after 0.001 seconds, so an exception will be raised
											with self.assertRaises(__UpperCamelCase       ):
																		A      						=    ''
																		for new_text in streamer:
																									streamer_text += new_text