import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


A__           =							1E-4

if is_torch_available():
			import torch

			from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
			from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class   _lowerCAmelCase		:







							def __init__(				self     :    Dict	,					__snake_case     :    Tuple	,					__snake_case     :    Optional[int]=16	,					__snake_case     :    Optional[int]=13	,					__snake_case     :    Tuple=7	,					__snake_case     :    Optional[Any]=14	,					__snake_case     :    int=10	,					__snake_case     :    Optional[int]=19	,					__snake_case     :    int=5	,					__snake_case     :    List[str]=4	,					__snake_case     :    int=True	,					__snake_case     :    List[str]=16	,					__snake_case     :    List[Any]=2	,					__snake_case     :    Dict=4	,					__snake_case     :    List[str]=4	,					__snake_case     :    Dict="gelu"	,					__snake_case     :    Optional[Any]=0.1	,					__snake_case     :    Any=0.1	,					__snake_case     :    Union[str, Any]=[1, 2, 3, 4, 5]	,					__snake_case     :    Dict=25	,					__snake_case     :    Optional[int]=5	,					):
										lowerCamelCase						:int						  =     d_model
										lowerCamelCase						:int						  =     parent
										lowerCamelCase						:Optional[Any]						  =     batch_size
										lowerCamelCase						:str						  =     prediction_length
										lowerCamelCase						:Optional[int]						  =     context_length
										lowerCamelCase						:Optional[Any]						  =     cardinality
										lowerCamelCase						:str						  =     num_time_features
										lowerCamelCase						:Tuple						  =     lags_sequence
										lowerCamelCase						:Optional[Any]						  =     embedding_dimension
										lowerCamelCase						:Optional[Any]						  =     is_training
										lowerCamelCase						:Any						  =     hidden_size
										lowerCamelCase						:Dict						  =     num_hidden_layers
										lowerCamelCase						:List[Any]						  =     num_attention_heads
										lowerCamelCase						:Optional[Any]						  =     intermediate_size
										lowerCamelCase						:Optional[int]						  =     hidden_act
										lowerCamelCase						:int						  =     hidden_dropout_prob
										lowerCamelCase						:List[str]						  =     attention_probs_dropout_prob

										lowerCamelCase						:Tuple						  =     context_length
										lowerCamelCase						:List[str]						  =     prediction_length + label_length
										lowerCamelCase						:int						  =     label_length

										lowerCamelCase						:List[str]						  =     moving_average
										lowerCamelCase						:List[str]						  =     autocorrelation_factor







							def 				snake_case  (				self     :    List[str]		):
										return AutoformerConfig(
										    d_model=self.d_model	,					encoder_layers=self.num_hidden_layers	,					decoder_layers=self.num_hidden_layers	,					encoder_attention_heads=self.num_attention_heads	,					decoder_attention_heads=self.num_attention_heads	,					encoder_ffn_dim=self.intermediate_size	,					decoder_ffn_dim=self.intermediate_size	,					dropout=self.hidden_dropout_prob	,					attention_dropout=self.attention_probs_dropout_prob	,					prediction_length=self.prediction_length	,					context_length=self.context_length	,					label_length=self.label_length	,					lags_sequence=self.lags_sequence	,					num_time_features=self.num_time_features	,					num_static_categorical_features=1	,					cardinality=[self.cardinality]	,					embedding_dimension=[self.embedding_dimension]	,					moving_average=self.moving_average	,					)







							def 				snake_case  (				self     :    Tuple	,					__snake_case     :    Union[str, Any]		):
										lowerCamelCase						:Dict						  =     config.context_length + max(config.lags_sequence		)

										lowerCamelCase						:Optional[int]						  =     ids_tensor([self.batch_size, 1]	,					config.cardinality[0]		)
										lowerCamelCase						:Any						  =     floats_tensor([self.batch_size, _past_length, config.num_time_features]		)
										lowerCamelCase						:Union[str, Any]						  =     floats_tensor([self.batch_size, _past_length]		)
										lowerCamelCase						:int						  =     floats_tensor([self.batch_size, _past_length]		) > 0.5

										# decoder inputs
										lowerCamelCase						:Union[str, Any]						  =     floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]		)
										lowerCamelCase						:List[Any]						  =     floats_tensor([self.batch_size, config.prediction_length]		)

										lowerCamelCase						:List[str]						  =     {
										    "past_values": past_values,
										    "static_categorical_features": static_categorical_features,
										    "past_time_features": past_time_features,
										    "past_observed_mask": past_observed_mask,
										    "future_time_features": future_time_features,
										    "future_values": future_values,
										}
										return inputs_dict







							def 				snake_case  (				self     :    Optional[Any]		):
										lowerCamelCase						:List[str]						  =     self.get_config()
										lowerCamelCase						:Tuple						  =     self.prepare_autoformer_inputs_dict(a_		)
										return config, inputs_dict







							def 				snake_case  (				self     :    Union[str, Any]		):
										lowerCamelCase						:List[Any]						  =     self.prepare_config_and_inputs()
										return config, inputs_dict







							def 				snake_case  (				self     :    Optional[int]	,					__snake_case     :    Union[str, Any]	,					__snake_case     :    Any		):
										lowerCamelCase						:List[str]						  =     AutoformerModel(config=a_		).to(a_		).eval()
										lowerCamelCase						:Union[str, Any]						  =     model(**a_		)

										lowerCamelCase						:List[str]						  =     outputs.encoder_last_hidden_state
										lowerCamelCase						:Optional[int]						  =     outputs.last_hidden_state

										with tempfile.TemporaryDirectory() as tmpdirname:
													lowerCamelCase						:List[str]						  =     model.get_encoder()
													encoder.save_pretrained(a_		)
													lowerCamelCase						:Optional[Any]						  =     AutoformerEncoder.from_pretrained(a_		).to(a_		)

										lowerCamelCase						:int						  =     model.create_network_inputs(**a_		)
										lowerCamelCase						:Tuple						  =     model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]		)

										lowerCamelCase						:List[Any]						  =     torch.cat(
										    (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])	,					dim=-1	,					)
										lowerCamelCase						:List[Any]						  =     encoder(inputs_embeds=a_		)[0]
										self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3		)

										lowerCamelCase						:Union[str, Any]						  =     (
										    torch.mean(transformer_inputs[:, : config.context_length, ...]	,					dim=1		)
										    .unsqueeze(1		)
										    .repeat(1	,					config.prediction_length	,					1		)
										)
										lowerCamelCase						:Tuple						  =     torch.zeros(
										    [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]]	,					device=enc_input.device	,					)

										lowerCamelCase						:Optional[Any]						  =     torch.cat(
										    (
										        torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)	,					dim=1		),
										        feature[:, config.context_length - config.label_length :, ...],
										    )	,					dim=-1	,					)
										lowerCamelCase						:str						  =     torch.cat(
										    (
										        torch.cat((trend_input[:, -config.label_length :, ...], mean)	,					dim=1		),
										        feature[:, config.context_length - config.label_length :, ...],
										    )	,					dim=-1	,					)

										with tempfile.TemporaryDirectory() as tmpdirname:
													lowerCamelCase						:Optional[int]						  =     model.get_decoder()
													decoder.save_pretrained(a_		)
													lowerCamelCase						:List[str]						  =     AutoformerDecoder.from_pretrained(a_		).to(a_		)

										lowerCamelCase						:Dict						  =     decoder(
										    trend=a_	,					inputs_embeds=a_	,					encoder_hidden_states=a_	,					)[0]

										self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3		)


@require_torch
class   _lowerCAmelCase		(     __lowerCamelCase      ,   __lowerCamelCase      ,   unittest.TestCase				):
							_UpperCAmelCase						    =     (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
							_UpperCAmelCase						    =     (AutoformerForPrediction,) if is_torch_available() else ()
							_UpperCAmelCase						    =     {'''feature-extraction''': AutoformerModel} if is_torch_available() else {}
							_UpperCAmelCase						    =     False
							_UpperCAmelCase						    =     False
							_UpperCAmelCase						    =     False
							_UpperCAmelCase						    =     False
							_UpperCAmelCase						    =     False
							_UpperCAmelCase						    =     False







							def 				snake_case  (				self     :    Dict		):
										lowerCamelCase						:Tuple						  =     AutoformerModelTester(self		)
										lowerCamelCase						:Union[str, Any]						  =     ConfigTester(self	,					config_class=a_	,					has_text_modality=a_		)







							def 				snake_case  (				self     :    List[Any]		):
										self.config_tester.run_common_tests()







							def 				snake_case  (				self     :    Optional[int]		):
										lowerCamelCase						:int						  =     self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
													lowerCamelCase						:Dict						  =     model_class(a_		)

													with tempfile.TemporaryDirectory() as tmpdirname:
																model.save_pretrained(a_		)
																lowerCamelCase						:str						  =     model_class.from_pretrained(a_	,					output_loading_info=a_		)
													self.assertEqual(info['''missing_keys''']	,					[]		)







							def 				snake_case  (				self     :    Union[str, Any]		):
										lowerCamelCase						:Dict						  =     self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.check_encoder_decoder_model_standalone(*a_		)







							@unittest.skip(reason='''Model has no tokens embeddings'''		)
							def 				snake_case  (				self     :    List[Any]		):
										pass







							def 				snake_case  (				self     :    Union[str, Any]		):
										lowerCamelCase						:Union[str, Any]						  =     inspect.signature(getattr(a_	,					'''forward'''		)		)
										# The main input is the name of the argument after `self`
										lowerCamelCase						:Optional[int]						  =     list(model_signature.parameters.keys()		)[1]
										self.assertEqual(AutoformerModel.main_input_name	,					a_		)







							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase						:Union[str, Any]						  =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
													lowerCamelCase						:Tuple						  =     model_class(a_		)
													lowerCamelCase						:List[Any]						  =     inspect.signature(model.forward		)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowerCamelCase						:Union[str, Any]						  =     [*signature.parameters.keys()]

													lowerCamelCase						:List[Any]						  =     [
													    "past_values",
													    "past_time_features",
													    "past_observed_mask",
													    "static_categorical_features",
													    "static_real_features",
													    "future_values",
													    "future_time_features",
													]

													if model.__class__.__name__ in ["AutoformerForPrediction"]:
																expected_arg_names.append('''future_observed_mask'''		)

													expected_arg_names.extend(
													    [
													        '''decoder_attention_mask''',
													        '''head_mask''',
													        '''decoder_head_mask''',
													        '''cross_attn_head_mask''',
													        '''encoder_outputs''',
													        '''past_key_values''',
													        '''output_hidden_states''',
													        '''output_attentions''',
													        '''use_cache''',
													        '''return_dict''',
													    ]		)

													self.assertListEqual(arg_names[: len(a_		)]	,					a_		)







							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase						:str						  =     self.model_tester.prepare_config_and_inputs_for_common()
										lowerCamelCase						:Optional[Any]						  =     True

										lowerCamelCase						:Any						  =     getattr(self.model_tester	,					'''seq_length'''	,					a_		)
										lowerCamelCase						:Union[str, Any]						  =     getattr(self.model_tester	,					'''decoder_seq_length'''	,					a_		)
										lowerCamelCase						:Tuple						  =     getattr(self.model_tester	,					'''encoder_seq_length'''	,					a_		)
										lowerCamelCase						:Tuple						  =     getattr(self.model_tester	,					'''d_model'''	,					a_		)
										lowerCamelCase						:Optional[int]						  =     getattr(self.model_tester	,					'''num_attention_heads'''	,					a_		)
										lowerCamelCase						:Dict						  =     d_model // num_attention_heads

										for model_class in self.all_model_classes:
													lowerCamelCase						:Optional[Any]						  =     True
													lowerCamelCase						:Dict						  =     False
													lowerCamelCase						:Dict						  =     True
													lowerCamelCase						:List[Any]						  =     model_class(a_		)
													model.to(a_		)
													model.eval()
													with torch.no_grad():
																lowerCamelCase						:Tuple						  =     model(**self._prepare_for_class(a_	,					a_		)		)
													lowerCamelCase						:Dict						  =     outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
													self.assertEqual(len(a_		)	,					self.model_tester.num_hidden_layers		)

													# check that output_attentions also work using config
													del inputs_dict["output_attentions"]
													lowerCamelCase						:List[Any]						  =     True
													lowerCamelCase						:Tuple						  =     model_class(a_		)
													model.to(a_		)
													model.eval()
													with torch.no_grad():
																lowerCamelCase						:Union[str, Any]						  =     model(**self._prepare_for_class(a_	,					a_		)		)
													lowerCamelCase						:Optional[Any]						  =     outputs.encoder_attentions
													self.assertEqual(len(a_		)	,					self.model_tester.num_hidden_layers		)

													self.assertListEqual(
													    list(attentions[0].shape[-3:]		)	,					[self.model_tester.num_attention_heads, encoder_seq_length, dim]	,					)
													lowerCamelCase						:List[str]						  =     len(a_		)

													lowerCamelCase						:List[Any]						  =     7

													if "last_hidden_state" in outputs:
																correct_outlen += 1

													if "trend" in outputs:
																correct_outlen += 1

													if "past_key_values" in outputs:
																correct_outlen += 1  # past_key_values have been returned

													if "loss" in outputs:
																correct_outlen += 1

													if "params" in outputs:
																correct_outlen += 1

													self.assertEqual(a_	,					a_		)

													# decoder attentions
													lowerCamelCase						:int						  =     outputs.decoder_attentions
													self.assertIsInstance(a_	,					(list, tuple)		)
													self.assertEqual(len(a_		)	,					self.model_tester.num_hidden_layers		)
													self.assertListEqual(
													    list(decoder_attentions[0].shape[-3:]		)	,					[self.model_tester.num_attention_heads, decoder_seq_length, dim]	,					)

													# cross attentions
													lowerCamelCase						:Optional[Any]						  =     outputs.cross_attentions
													self.assertIsInstance(a_	,					(list, tuple)		)
													self.assertEqual(len(a_		)	,					self.model_tester.num_hidden_layers		)
													self.assertListEqual(
													    list(cross_attentions[0].shape[-3:]		)	,					[self.model_tester.num_attention_heads, decoder_seq_length, dim]	,					)

										# Check attention is always last and order is fine
										lowerCamelCase						:Union[str, Any]						  =     True
										lowerCamelCase						:Tuple						  =     True
										lowerCamelCase						:str						  =     model_class(a_		)
										model.to(a_		)
										model.eval()
										with torch.no_grad():
													lowerCamelCase						:str						  =     model(**self._prepare_for_class(a_	,					a_		)		)

										self.assertEqual(out_len + 2	,					len(a_		)		)

										lowerCamelCase						:Union[str, Any]						  =     outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

										self.assertEqual(len(a_		)	,					self.model_tester.num_hidden_layers		)
										self.assertListEqual(
										    list(self_attentions[0].shape[-3:]		)	,					[self.model_tester.num_attention_heads, encoder_seq_length, dim]	,					)







							@is_flaky()
							def 				snake_case  (				self     :    List[Any]		):
										super().test_retain_grad_hidden_states_attentions()





def  _lowerCamelCase					(   a_				: Optional[Any]="train-batch.pt"):
			lowerCamelCase						:Any						  =     hf_hub_download(repo_id='''hf-internal-testing/tourism-monthly-batch'''						,			filename=lowerCAmelCase_						,			repo_type='''dataset''')
			lowerCamelCase						:Union[str, Any]						  =     torch.load(lowerCAmelCase_						,			map_location=lowerCAmelCase_)
			return batch







@require_torch
@slow
class   _lowerCAmelCase		(     unittest.TestCase				):







							def 				snake_case  (				self     :    Any		):
										lowerCamelCase						:Union[str, Any]						  =     AutoformerModel.from_pretrained('''huggingface/autoformer-tourism-monthly'''		).to(a_		)
										lowerCamelCase						:Union[str, Any]						  =     prepare_batch()

										with torch.no_grad():
													lowerCamelCase						:str						  =     model(
													    past_values=batch['''past_values''']	,					past_time_features=batch['''past_time_features''']	,					past_observed_mask=batch['''past_observed_mask''']	,					static_categorical_features=batch['''static_categorical_features''']	,					future_values=batch['''future_values''']	,					future_time_features=batch['''future_time_features''']	,					)[0]

										lowerCamelCase						:Dict						  =     torch.Size(
										    (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)		)
										self.assertEqual(output.shape	,					a_		)

										lowerCamelCase						:str						  =     torch.tensor(
										    [[0.3_5_9_3, -1.3_3_9_8, 0.6_3_3_0], [0.2_2_7_9, 1.5_3_9_6, -0.1_7_9_2], [0.0_4_5_0, 1.3_2_2_5, -0.2_3_3_5]]	,					device=a_		)
										self.assertTrue(torch.allclose(output[0, :3, :3]	,					a_	,					atol=a_		)		)







							def 				snake_case  (				self     :    Union[str, Any]		):
										lowerCamelCase						:List[Any]						  =     AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly'''		).to(a_		)
										lowerCamelCase						:Union[str, Any]						  =     prepare_batch('''val-batch.pt'''		)
										with torch.no_grad():
													lowerCamelCase						:int						  =     model(
													    past_values=batch['''past_values''']	,					past_time_features=batch['''past_time_features''']	,					past_observed_mask=batch['''past_observed_mask''']	,					static_categorical_features=batch['''static_categorical_features''']	,					).encoder_last_hidden_state
										lowerCamelCase						:str						  =     torch.Size((64, model.config.context_length, model.config.d_model)		)
										self.assertEqual(output.shape	,					a_		)

										lowerCamelCase						:str						  =     torch.tensor(
										    [[-0.0_7_3_4, -0.9_0_3_6, 0.8_3_5_8], [4.7_1_8_6, 2.4_1_1_3, 1.9_5_8_1], [1.7_9_5_3, 2.3_5_5_8, 1.2_9_7_0]]	,					device=a_		)
										self.assertTrue(torch.allclose(output[0, :3, :3]	,					a_	,					atol=a_		)		)







							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:Optional[int]						  =     AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly'''		).to(a_		)
										lowerCamelCase						:Optional[int]						  =     prepare_batch('''val-batch.pt'''		)
										with torch.no_grad():
													lowerCamelCase						:Dict						  =     model.generate(
													    static_categorical_features=batch['''static_categorical_features''']	,					past_time_features=batch['''past_time_features''']	,					past_values=batch['''past_values''']	,					future_time_features=batch['''future_time_features''']	,					past_observed_mask=batch['''past_observed_mask''']	,					)
										lowerCamelCase						:Union[str, Any]						  =     torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)		)
										self.assertEqual(outputs.sequences.shape	,					a_		)

										lowerCamelCase						:int						  =     torch.tensor([3130.6763, 4056.5293, 7053.0786]	,					device=a_		)
										lowerCamelCase						:List[str]						  =     outputs.sequences.mean(dim=1		)
										self.assertTrue(torch.allclose(mean_prediction[0, -3:]	,					a_	,					rtol=1e-1		)		)




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

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

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

										self.assertEqual(__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    Dict		):
										lowerCamelCase						:Tuple						  =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
										lowerCamelCase						:int						  =     AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(__snake_case		)
										lowerCamelCase						:List[Any]						  =     -1

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

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

										self.assertEqual(__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:int						  =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
										lowerCamelCase						:Dict						  =     AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(__snake_case		)
										lowerCamelCase						:List[str]						  =     -1

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

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

										self.assertEqual(__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    Optional[int]		):
										# 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
										lowerCamelCase						:List[Any]						  =     AutoTokenizer.from_pretrained('''distilgpt2'''		)
										lowerCamelCase						:Union[str, Any]						  =     AutoModelForCausalLM.from_pretrained('''distilgpt2'''		).to(__snake_case		)
										lowerCamelCase						:Optional[int]						  =     -1

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







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

										lowerCamelCase						:Tuple						  =     ids_tensor((1, 5)	,					vocab_size=model.config.vocab_size		).to(__snake_case		)
										lowerCamelCase						:List[Any]						  =     TextIteratorStreamer(__snake_case	,					timeout=0.0_0_1		)
										lowerCamelCase						:Dict						  =     {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
										lowerCamelCase						:Tuple						  =     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		):
													lowerCamelCase						:Dict						  =     ''''''
													for new_text in streamer:
																streamer_text += new_text



