"""simple docstring"""





import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
			import os

			# The slow tests are often failing with OOM error on GPU
			# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
			# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
			lowerCAmelCase_     =	'''platform'''
			import jax
			import jax.numpy as jnp
			import numpy as np

			from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel




@require_flax
class  _snake_case		:







				"""simple docstring"""






				a			=						PegasusConfig
				a			=						{}
				a			=						"gelu"





				def __init__(      self							:							List[str]      ,    _A							:							str      ,    _A							:							str=1_3      ,    _A							:							Dict=7      ,    _A							:							Tuple=True      ,    _A							:							Tuple=False      ,    _A							:							Any=9_9      ,    _A							:							Union[str, Any]=3_2      ,    _A							:							Dict=5      ,    _A							:							List[str]=4      ,    _A							:							Union[str, Any]=3_7      ,    _A							:							int=0.1      ,    _A							:							Union[str, Any]=0.1      ,    _A							:							Any=2_0      ,    _A							:							List[str]=2      ,    _A							:							Optional[int]=1      ,    _A							:							Optional[Any]=0      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    parent
											_SCREAMING_SNAKE_CASE		:						int			  =    batch_size
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    seq_length
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    is_training
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    use_labels
											_SCREAMING_SNAKE_CASE		:						Dict			  =    vocab_size
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    hidden_size
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    num_hidden_layers
											_SCREAMING_SNAKE_CASE		:						str			  =    num_attention_heads
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    intermediate_size

											_SCREAMING_SNAKE_CASE		:						int			  =    hidden_dropout_prob
											_SCREAMING_SNAKE_CASE		:						int			  =    attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    max_position_embeddings
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    eos_token_id
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    pad_token_id
											_SCREAMING_SNAKE_CASE		:						Any			  =    bos_token_id





				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Dict			  =    ids_tensor([self.batch_size, self.seq_length - 1]      ,    self.vocab_size).clip(3      ,    self.vocab_size)
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    np.expand_dims(np.array([self.eos_token_id] * self.batch_size)      ,    1)
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    np.concatenate([input_ids, eos_tensor]      ,    axis=1)

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    ids_tensor([self.batch_size, self.seq_length]      ,    self.vocab_size)

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.config_cls(
											    vocab_size=self.vocab_size      ,    d_model=self.hidden_size      ,    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      ,    max_position_embeddings=self.max_position_embeddings      ,    eos_token_ids=[2]      ,    bos_token_id=self.bos_token_id      ,    pad_token_id=self.pad_token_id      ,    decoder_start_token_id=self.pad_token_id      ,    **self.config_updates      ,    )
											_SCREAMING_SNAKE_CASE		:						Any			  =    prepare_pegasus_inputs_dict(_A      ,    _A      ,    _A)
											return config, inputs_dict





				def 		_lowerCAmelCase		(      self							:							Dict      ,    _A							:							Optional[int]      ,    _A							:							int      ,    _A							:							Union[str, Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    2_0
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    model_class_name(_A)

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model.encode(inputs_dict["""input_ids"""])

											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    (
											    inputs_dict["""decoder_input_ids"""],
											    inputs_dict["""decoder_attention_mask"""],
											)

											_SCREAMING_SNAKE_CASE		:						Dict			  =    model.init_cache(decoder_input_ids.shape[0]      ,    _A      ,    _A)
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,    dtype="""i4""")

											_SCREAMING_SNAKE_CASE		:						str			  =    jnp.broadcast_to(
											    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]      ,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,    )
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    model.decode(
											    decoder_input_ids[:, :-1]      ,    _A      ,    decoder_attention_mask=_A      ,    past_key_values=_A      ,    decoder_position_ids=_A      ,    )

											_SCREAMING_SNAKE_CASE		:						Tuple			  =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,    dtype="""i4""")
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    model.decode(
											    decoder_input_ids[:, -1:]      ,    _A      ,    decoder_attention_mask=_A      ,    past_key_values=outputs_cache.past_key_values      ,    decoder_position_ids=_A      ,    )

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    model.decode(_A      ,    _A)

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
											self.parent.assertTrue(diff < 1e-3      ,    msg=f"""Max diff is {diff}""")





				def 		_lowerCAmelCase		(      self							:							Optional[int]      ,    _A							:							Optional[Any]      ,    _A							:							List[Any]      ,    _A							:							Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    2_0
											_SCREAMING_SNAKE_CASE		:						int			  =    model_class_name(_A)

											_SCREAMING_SNAKE_CASE		:						Tuple			  =    model.encode(inputs_dict["""input_ids"""])

											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Any			  =    (
											    inputs_dict["""decoder_input_ids"""],
											    inputs_dict["""decoder_attention_mask"""],
											)

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    jnp.concatenate(
											    [
											        decoder_attention_mask,
											        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
											    ]      ,    axis=-1      ,    )

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model.init_cache(decoder_input_ids.shape[0]      ,    _A      ,    _A)
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    jnp.broadcast_to(
											    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]      ,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,    )

											_SCREAMING_SNAKE_CASE		:						int			  =    model.decode(
											    decoder_input_ids[:, :-1]      ,    _A      ,    decoder_attention_mask=_A      ,    past_key_values=_A      ,    decoder_position_ids=_A      ,    )
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,    dtype="""i4""")
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    model.decode(
											    decoder_input_ids[:, -1:]      ,    _A      ,    past_key_values=outputs_cache.past_key_values      ,    decoder_attention_mask=_A      ,    decoder_position_ids=_A      ,    )

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    model.decode(_A      ,    _A      ,    decoder_attention_mask=_A)

											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
											self.parent.assertTrue(diff < 1e-3      ,    msg=f"""Max diff is {diff}""")



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	)->    Tuple:
							if attention_mask is None:
														_SCREAMING_SNAKE_CASE		:						int			  =    np.not_equal(__SCREAMING_SNAKE_CASE     ,	config.pad_token_id  ).astype(np.inta  )
							if decoder_attention_mask is None:
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    np.concatenate(
														    [
														        np.ones(decoder_input_ids[:, :1].shape     ,	dtype=np.inta  ),
														        np.not_equal(decoder_input_ids[:, 1:]     ,	config.pad_token_id  ).astype(np.inta  ),
														    ]     ,	axis=-1     ,	)
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": decoder_attention_mask,
							}




@require_flax
class  _snake_case		(    __snake_case   ,       unittest.TestCase ):







				"""simple docstring"""






				a			=						(
				    (
				        FlaxPegasusForConditionalGeneration,
				        FlaxPegasusModel,
				    )
				    if is_flax_available()
				    else ()
				)
				a			=						(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
				a			=						True
				a			=						False
				a			=						False
				a			=						False





				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    FlaxPegasusModelTester(self)
											_SCREAMING_SNAKE_CASE		:						Any			  =    ConfigTester(self      ,    config_class=_A)





				def 		_lowerCAmelCase		(      self							:							List[str]):



											"""simple docstring"""







											self.config_tester.run_common_tests()





				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Any			  =    self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward(_A      ,    _A      ,    _A)





				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Dict			  =    self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward_with_attn_mask(_A      ,    _A      ,    _A)





				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Tuple			  =    self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__):
																									_SCREAMING_SNAKE_CASE		:						List[Any]			  =    self._prepare_for_class(_A      ,    _A)
																									_SCREAMING_SNAKE_CASE		:						str			  =    model_class(_A)

																									@jax.jit
																									def encode_jitted(_A							:							str      ,    _A							:							Tuple=None      ,    **_A							:							Any):
																																return model.encode(input_ids=_A      ,    attention_mask=_A)

																									with self.subTest("""JIT Enabled"""):
																																_SCREAMING_SNAKE_CASE		:						Any			  =    encode_jitted(**_A).to_tuple()

																									with self.subTest("""JIT Disabled"""):
																																with jax.disable_jit():
																																							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    encode_jitted(**_A).to_tuple()

																									self.assertEqual(len(_A)      ,    len(_A))
																									for jitted_output, output in zip(_A      ,    _A):
																																self.assertEqual(jitted_output.shape      ,    output.shape)





				def 		_lowerCAmelCase		(      self							:							Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Dict			  =    self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__):
																									_SCREAMING_SNAKE_CASE		:						Any			  =    model_class(_A)
																									_SCREAMING_SNAKE_CASE		:						str			  =    model.encode(inputs_dict["""input_ids"""]      ,    inputs_dict["""attention_mask"""])

																									_SCREAMING_SNAKE_CASE		:						List[str]			  =    {
																									    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
																									    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
																									    """encoder_outputs""": encoder_outputs,
																									}

																									@jax.jit
																									def decode_jitted(_A							:							Optional[Any]      ,    _A							:							Optional[int]      ,    _A							:							int):
																																return model.decode(
																																    decoder_input_ids=_A      ,    decoder_attention_mask=_A      ,    encoder_outputs=_A      ,    )

																									with self.subTest("""JIT Enabled"""):
																																_SCREAMING_SNAKE_CASE		:						Dict			  =    decode_jitted(**_A).to_tuple()

																									with self.subTest("""JIT Disabled"""):
																																with jax.disable_jit():
																																							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    decode_jitted(**_A).to_tuple()

																									self.assertEqual(len(_A)      ,    len(_A))
																									for jitted_output, output in zip(_A      ,    _A):
																																self.assertEqual(jitted_output.shape      ,    output.shape)





				@slow
				def 		_lowerCAmelCase		(      self							:							Dict):



											"""simple docstring"""







											for model_class_name in self.all_model_classes:
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model_class_name.from_pretrained("""google/pegasus-large"""      ,    from_pt=_A)
																		_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    np.ones((1, 1))
																		_SCREAMING_SNAKE_CASE		:						List[str]			  =    model(_A)
																		self.assertIsNotNone(_A)





				@slow
				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum""")
											_SCREAMING_SNAKE_CASE		:						Any			  =    PegasusTokenizer.from_pretrained("""google/pegasus-xsum""")

											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    [
											    """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
											    """ The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
											]

											_SCREAMING_SNAKE_CASE		:						Any			  =    [
											    """California's largest electricity provider has turned off power to hundreds of thousands of customers.""",
											    """Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.""",
											]

											_SCREAMING_SNAKE_CASE		:						Tuple			  =    tokenizer(_A      ,    return_tensors="""np"""      ,    truncation=_A      ,    max_length=5_1_2      ,    padding=_A)
											_SCREAMING_SNAKE_CASE		:						int			  =    model.generate(**_A      ,    num_beams=2).sequences
											_SCREAMING_SNAKE_CASE		:						int			  =    tokenizer.batch_decode(_A      ,    skip_special_tokens=_A)
											assert tgt_text == decoded



"""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 ):







				"""simple docstring"""






				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""")
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""").to(_A)
											_SCREAMING_SNAKE_CASE		:						Any			  =    -1

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    ids_tensor((1, 5)      ,    vocab_size=model.config.vocab_size).to(_A)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    model.generate(_A      ,    max_new_tokens=1_0      ,    do_sample=_A)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    tokenizer.decode(greedy_ids[0])

											with CaptureStdout() as cs:
																		_SCREAMING_SNAKE_CASE		:						Any			  =    TextStreamer(_A)
																		model.generate(_A      ,    max_new_tokens=1_0      ,    do_sample=_A      ,    streamer=_A)
											# The greedy text should be printed to stdout, except for the final "\n" in the streamer
											_SCREAMING_SNAKE_CASE		:						str			  =    cs.out[:-1]

											self.assertEqual(_A      ,    _A)





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""")
											_SCREAMING_SNAKE_CASE		:						Any			  =    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""").to(_A)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    -1

											_SCREAMING_SNAKE_CASE		:						str			  =    ids_tensor((1, 5)      ,    vocab_size=model.config.vocab_size).to(_A)
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model.generate(_A      ,    max_new_tokens=1_0      ,    do_sample=_A)
											_SCREAMING_SNAKE_CASE		:						Any			  =    tokenizer.decode(greedy_ids[0])

											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    TextIteratorStreamer(_A)
											_SCREAMING_SNAKE_CASE		:						Any			  =    {"""input_ids""": input_ids, """max_new_tokens""": 1_0, """do_sample""": False, """streamer""": streamer}
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    Thread(target=model.generate      ,    kwargs=_A)
											thread.start()
											_SCREAMING_SNAKE_CASE		:						Any			  =    """"""
											for new_text in streamer:
																		streamer_text += new_text

											self.assertEqual(_A      ,    _A)





				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""")
											_SCREAMING_SNAKE_CASE		:						Dict			  =    AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""").to(_A)
											_SCREAMING_SNAKE_CASE		:						Any			  =    -1

											_SCREAMING_SNAKE_CASE		:						Any			  =    ids_tensor((1, 5)      ,    vocab_size=model.config.vocab_size).to(_A)
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model.generate(_A      ,    max_new_tokens=1_0      ,    do_sample=_A)
											_SCREAMING_SNAKE_CASE		:						str			  =    greedy_ids[:, input_ids.shape[1] :]
											_SCREAMING_SNAKE_CASE		:						Dict			  =    tokenizer.decode(new_greedy_ids[0])

											with CaptureStdout() as cs:
																		_SCREAMING_SNAKE_CASE		:						Any			  =    TextStreamer(_A      ,    skip_prompt=_A)
																		model.generate(_A      ,    max_new_tokens=1_0      ,    do_sample=_A      ,    streamer=_A)
											# The greedy text should be printed to stdout, except for the final "\n" in the streamer
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    cs.out[:-1]

											self.assertEqual(_A      ,    _A)





				def 		_lowerCAmelCase		(      self							:							Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						int			  =    AutoTokenizer.from_pretrained("""distilgpt2""")
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    AutoModelForCausalLM.from_pretrained("""distilgpt2""").to(_A)
											_SCREAMING_SNAKE_CASE		:						int			  =    -1

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





				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







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

											_SCREAMING_SNAKE_CASE		:						Dict			  =    ids_tensor((1, 5)      ,    vocab_size=model.config.vocab_size).to(_A)
											_SCREAMING_SNAKE_CASE		:						int			  =    TextIteratorStreamer(_A      ,    timeout=0.001)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    {"""input_ids""": input_ids, """max_new_tokens""": 1_0, """do_sample""": False, """streamer""": streamer}
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    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):
																		_SCREAMING_SNAKE_CASE		:						str			  =    """"""
																		for new_text in streamer:
																									streamer_text += new_text


