






# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
			import jax
			import jaxlib

UpperCAmelCase__      			=						get_logger()

UpperCAmelCase__      			=						None




class 							snake_case_       (    TensorFormatter[Mapping, """jax.Array""", Mapping]      ):

						"""simple docstring"""



						def __init__(self:					List[Any] ,					__UpperCAmelCase:					Union[str, Any]=None ,					__UpperCAmelCase:					Any=None ,					**__UpperCAmelCase:					Any   )    ->     Union[str, Any]:




											'''simple docstring'''



											super().__init__(features=__UpperCAmelCase   )
											import jax
											from jaxlib.xla_client import Device

											if isinstance(__UpperCAmelCase ,					__UpperCAmelCase   ):
																raise ValueError(
																    f'Expected {device} to be a `str` not {type(__UpperCAmelCase   )}, as `jaxlib.xla_extension.Device` '
																    "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
																    "the device with `str()` to get its string identifier that will be internally mapped "
																    "to the actual `jaxlib.xla_extension.Device`."   )
											__a					:						Optional[Any]      =					device if isinstance(__UpperCAmelCase ,					__UpperCAmelCase   ) else str(jax.devices()[0]   )
											# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
											# with `pickle` nor with `dill`, so we need to use a global variable instead
											global DEVICE_MAPPING
											if DEVICE_MAPPING is None:
																__a					:						Tuple      =					self._map_devices_to_str()
											if self.device not in list(DEVICE_MAPPING.keys()   ):
																logger.warning(
																    f'Device with string identifier {self.device} not listed among the available '
																    f'devices: {list(DEVICE_MAPPING.keys()   )}, so falling back to the default '
																    f'device: {str(jax.devices()[0]   )}.'   )
																__a					:						str      =					str(jax.devices()[0]   )
											__a					:						List[str]      =					jnp_array_kwargs



						@staticmethod
						def        UpperCAmelCase__					()    ->     Dict[str, "jaxlib.xla_extension.Device"]:




											'''simple docstring'''



											import jax

											return {str(__UpperCAmelCase   ): device for device in jax.devices()}



						def        UpperCAmelCase__					(self:					str ,					__UpperCAmelCase:					Dict   )    ->     Optional[Any]:




											'''simple docstring'''



											import jax
											import jax.numpy as jnp

											if isinstance(__UpperCAmelCase ,					__UpperCAmelCase   ) and column:
																if all(
																    isinstance(__UpperCAmelCase ,					jax.Array   ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column   ):
																					return jnp.stack(__UpperCAmelCase ,					axis=0   )
											return column



						def        UpperCAmelCase__					(self:					List[str] ,					__UpperCAmelCase:					List[Any]   )    ->     List[str]:




											'''simple docstring'''



											import jax
											import jax.numpy as jnp

											if isinstance(__UpperCAmelCase ,					(str, bytes, type(__UpperCAmelCase   ))   ):
																return value
											elif isinstance(__UpperCAmelCase ,					(np.character, np.ndarray)   ) and np.issubdtype(value.dtype ,					np.character   ):
																return value.tolist()

											__a					:						Dict      =					{}

											if isinstance(__UpperCAmelCase ,					(np.number, np.ndarray)   ) and np.issubdtype(value.dtype ,					np.integer   ):
																# the default int precision depends on the jax config
																# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
																if jax.config.jax_enable_xaa:
																					__a					:						Optional[Any]      =					{"dtype": jnp.intaa}
																else:
																					__a					:						str      =					{"dtype": jnp.intaa}
											elif isinstance(__UpperCAmelCase ,					(np.number, np.ndarray)   ) and np.issubdtype(value.dtype ,					np.floating   ):
																__a					:						List[Any]      =					{"dtype": jnp.floataa}
											elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																import PIL.Image

																if isinstance(__UpperCAmelCase ,					PIL.Image.Image   ):
																					__a					:						Dict      =					np.asarray(__UpperCAmelCase   )

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
											global DEVICE_MAPPING
											if DEVICE_MAPPING is None:
																__a					:						str      =					self._map_devices_to_str()

											with jax.default_device(DEVICE_MAPPING[self.device]   ):
																# calling jnp.array on a np.ndarray does copy the data
																# see https://github.com/google/jax/issues/4486
																return jnp.array(__UpperCAmelCase ,					**{**default_dtype, **self.jnp_array_kwargs}   )



						def        UpperCAmelCase__					(self:					Dict ,					__UpperCAmelCase:					int   )    ->     Dict:




											'''simple docstring'''



											import jax

											# support for torch, tf, jax etc.
											if config.TORCH_AVAILABLE and "torch" in sys.modules:
																import torch

																if isinstance(__UpperCAmelCase ,					torch.Tensor   ):
																					return self._tensorize(data_struct.detach().cpu().numpy()[()]   )
											if hasattr(__UpperCAmelCase ,					"__array__"   ) and not isinstance(__UpperCAmelCase ,					jax.Array   ):
																__a					:						Dict      =					data_struct.__array__()
											# support for nested types like struct of list of struct
											if isinstance(__UpperCAmelCase ,					np.ndarray   ):
																if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
																					return self._consolidate([self.recursive_tensorize(__UpperCAmelCase   ) for substruct in data_struct]   )
											elif isinstance(__UpperCAmelCase ,					(list, tuple)   ):
																return self._consolidate([self.recursive_tensorize(__UpperCAmelCase   ) for substruct in data_struct]   )
											return self._tensorize(__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					Dict ,					__UpperCAmelCase:					dict   )    ->     Optional[Any]:




											'''simple docstring'''



											return map_nested(self._recursive_tensorize ,					__UpperCAmelCase ,					map_list=__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					Union[str, Any] ,					__UpperCAmelCase:					pa.Table   )    ->     Mapping:




											'''simple docstring'''



											__a					:						Any      =					self.numpy_arrow_extractor().extract_row(__UpperCAmelCase   )
											__a					:						Optional[Any]      =					self.python_features_decoder.decode_row(__UpperCAmelCase   )
											return self.recursive_tensorize(__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					Dict ,					__UpperCAmelCase:					pa.Table   )    ->     "jax.Array":




											'''simple docstring'''



											__a					:						Optional[int]      =					self.numpy_arrow_extractor().extract_column(__UpperCAmelCase   )
											__a					:						Optional[Any]      =					self.python_features_decoder.decode_column(__UpperCAmelCase ,					pa_table.column_names[0]   )
											__a					:						Optional[int]      =					self.recursive_tensorize(__UpperCAmelCase   )
											__a					:						Optional[int]      =					self._consolidate(__UpperCAmelCase   )
											return column



						def        UpperCAmelCase__					(self:					List[Any] ,					__UpperCAmelCase:					pa.Table   )    ->     Mapping:




											'''simple docstring'''



											__a					:						Dict      =					self.numpy_arrow_extractor().extract_batch(__UpperCAmelCase   )
											__a					:						Union[str, Any]      =					self.python_features_decoder.decode_batch(__UpperCAmelCase   )
											__a					:						Union[str, Any]      =					self.recursive_tensorize(__UpperCAmelCase   )
											for column_name in batch:
																__a					:						List[Any]      =					self._consolidate(batch[column_name]   )
											return batch













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
			UpperCAmelCase__      			=						'''platform'''
			import jax
			import jax.numpy as jnp
			import numpy as np

			from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel




@require_flax
class 							snake_case_       :

						"""simple docstring"""



						snake_case__        =      PegasusConfig
						snake_case__        =      {}
						snake_case__        =      """gelu"""



						def __init__(self:					Union[str, Any] ,					__UpperCAmelCase:					Dict ,					__UpperCAmelCase:					Union[str, Any]=13 ,					__UpperCAmelCase:					Any=7 ,					__UpperCAmelCase:					Tuple=True ,					__UpperCAmelCase:					List[Any]=False ,					__UpperCAmelCase:					Tuple=99 ,					__UpperCAmelCase:					Optional[int]=32 ,					__UpperCAmelCase:					str=5 ,					__UpperCAmelCase:					Optional[Any]=4 ,					__UpperCAmelCase:					str=37 ,					__UpperCAmelCase:					str=0.1 ,					__UpperCAmelCase:					List[str]=0.1 ,					__UpperCAmelCase:					Union[str, Any]=20 ,					__UpperCAmelCase:					Any=2 ,					__UpperCAmelCase:					List[Any]=1 ,					__UpperCAmelCase:					Union[str, Any]=0 ,					)    ->     Any:




											'''simple docstring'''



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

											__a					:						List[Any]      =					hidden_dropout_prob
											__a					:						Union[str, Any]      =					attention_probs_dropout_prob
											__a					:						List[str]      =					max_position_embeddings
											__a					:						str      =					eos_token_id
											__a					:						Any      =					pad_token_id
											__a					:						Optional[Any]      =					bos_token_id



						def        UpperCAmelCase__					(self:					Optional[Any]   )    ->     int:




											'''simple docstring'''



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

											__a					:						List[Any]      =					ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size   )

											__a					:						List[Any]      =					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 ,					)
											__a					:						int      =					prepare_pegasus_inputs_dict(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   )
											return config, inputs_dict



						def        UpperCAmelCase__					(self:					str ,					__UpperCAmelCase:					Any ,					__UpperCAmelCase:					int ,					__UpperCAmelCase:					List[Any]   )    ->     int:




											'''simple docstring'''



											__a					:						List[Any]      =					20
											__a					:						List[Any]      =					model_class_name(__UpperCAmelCase   )

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

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

											__a					:						Union[str, Any]      =					model.init_cache(decoder_input_ids.shape[0] ,					__UpperCAmelCase ,					__UpperCAmelCase   )
											__a					:						Tuple      =					jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,					dtype="i4"   )

											__a					:						int      =					jnp.broadcast_to(
											    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :] ,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,					)
											__a					:						Any      =					model.decode(
											    decoder_input_ids[:, :-1] ,					__UpperCAmelCase ,					decoder_attention_mask=__UpperCAmelCase ,					past_key_values=__UpperCAmelCase ,					decoder_position_ids=__UpperCAmelCase ,					)

											__a					:						Dict      =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,					dtype="i4"   )
											__a					:						Tuple      =					model.decode(
											    decoder_input_ids[:, -1:] ,					__UpperCAmelCase ,					decoder_attention_mask=__UpperCAmelCase ,					past_key_values=outputs_cache.past_key_values ,					decoder_position_ids=__UpperCAmelCase ,					)

											__a					:						Optional[int]      =					model.decode(__UpperCAmelCase ,					__UpperCAmelCase   )

											__a					:						List[str]      =					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        UpperCAmelCase__					(self:					List[Any] ,					__UpperCAmelCase:					Optional[int] ,					__UpperCAmelCase:					Union[str, Any] ,					__UpperCAmelCase:					Tuple   )    ->     List[str]:




											'''simple docstring'''



											__a					:						str      =					20
											__a					:						Tuple      =					model_class_name(__UpperCAmelCase   )

											__a					:						Optional[int]      =					model.encode(inputs_dict["input_ids"]   )

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

											__a					:						Union[str, Any]      =					jnp.concatenate(
											    [
											        decoder_attention_mask,
											        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])   ),
											    ] ,					axis=-1 ,					)

											__a					:						Any      =					model.init_cache(decoder_input_ids.shape[0] ,					__UpperCAmelCase ,					__UpperCAmelCase   )
											__a					:						Tuple      =					jnp.broadcast_to(
											    jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :] ,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,					)

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

											__a					:						str      =					model.decode(__UpperCAmelCase ,					__UpperCAmelCase ,					decoder_attention_mask=__UpperCAmelCase   )

											__a					:						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     a_   (__A				,       __A				,       __A				,       __A=None				,       __A=None				,       )				->						List[str]:



					"""simple docstring"""
					if attention_mask is None:
										__a					:						str      =					np.not_equal(__A				,       config.pad_token_id      ).astype(np.inta      )
					if decoder_attention_mask is None:
										__a					:						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_       (    __UpperCamelCase		,							unittest.TestCase      ):

						"""simple docstring"""



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



						def        UpperCAmelCase__					(self:					int   )    ->     Optional[Any]:




											'''simple docstring'''



											__a					:						Any      =					FlaxPegasusModelTester(self   )
											__a					:						Union[str, Any]      =					ConfigTester(self ,					config_class=__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					List[str]   )    ->     Union[str, Any]:




											'''simple docstring'''



											self.config_tester.run_common_tests()



						def        UpperCAmelCase__					(self:					Dict   )    ->     Optional[int]:




											'''simple docstring'''



											__a      ,			__a					:						Tuple      =					self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
																self.model_tester.check_use_cache_forward(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					Optional[int]   )    ->     str:




											'''simple docstring'''



											__a      ,			__a					:						Optional[int]      =					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(__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					Union[str, Any]   )    ->     List[Any]:




											'''simple docstring'''



											__a      ,			__a					:						List[str]      =					self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__   ):
																					__a					:						Union[str, Any]      =					self._prepare_for_class(__UpperCAmelCase ,					__UpperCAmelCase   )
																					__a					:						Tuple      =					model_class(__UpperCAmelCase   )

																					@jax.jit
																					def encode_jitted(__UpperCAmelCase:					Dict ,					__UpperCAmelCase:					Tuple=None ,					**__UpperCAmelCase:					List[Any]   ):
																										return model.encode(input_ids=__UpperCAmelCase ,					attention_mask=__UpperCAmelCase   )

																					with self.subTest("JIT Enabled"   ):
																										__a					:						Any      =					encode_jitted(**__UpperCAmelCase   ).to_tuple()

																					with self.subTest("JIT Disabled"   ):
																										with jax.disable_jit():
																															__a					:						str      =					encode_jitted(**__UpperCAmelCase   ).to_tuple()

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



						def        UpperCAmelCase__					(self:					Optional[int]   )    ->     List[str]:




											'''simple docstring'''



											__a      ,			__a					:						Union[str, Any]      =					self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__   ):
																					__a					:						Tuple      =					model_class(__UpperCAmelCase   )
																					__a					:						Optional[Any]      =					model.encode(inputs_dict["input_ids"] ,					inputs_dict["attention_mask"]   )

																					__a					:						Dict      =					{
																					    "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(__UpperCAmelCase:					int ,					__UpperCAmelCase:					Any ,					__UpperCAmelCase:					List[str]   ):
																										return model.decode(
																										    decoder_input_ids=__UpperCAmelCase ,					decoder_attention_mask=__UpperCAmelCase ,					encoder_outputs=__UpperCAmelCase ,					)

																					with self.subTest("JIT Enabled"   ):
																										__a					:						Any      =					decode_jitted(**__UpperCAmelCase   ).to_tuple()

																					with self.subTest("JIT Disabled"   ):
																										with jax.disable_jit():
																															__a					:						Optional[Any]      =					decode_jitted(**__UpperCAmelCase   ).to_tuple()

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



						@slow
						def        UpperCAmelCase__					(self:					Dict   )    ->     Tuple:




											'''simple docstring'''



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



						@slow
						def        UpperCAmelCase__					(self:					Optional[Any]   )    ->     Tuple:




											'''simple docstring'''



											__a					:						List[str]      =					FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum"   )
											__a					:						Tuple      =					PegasusTokenizer.from_pretrained("google/pegasus-xsum"   )

											__a					:						Tuple      =					[
											    " 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!\" ",
											]

											__a					:						List[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.",
											]

											__a					:						Any      =					tokenizer(__UpperCAmelCase ,					return_tensors="np" ,					truncation=__UpperCAmelCase ,					max_length=512 ,					padding=__UpperCAmelCase   )
											__a					:						List[str]      =					model.generate(**__UpperCAmelCase ,					num_beams=2   ).sequences
											__a					:						str      =					tokenizer.batch_decode(__UpperCAmelCase ,					skip_special_tokens=__UpperCAmelCase   )
											assert tgt_text == decoded





