
"""simple docstring"""



import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class 		a_						( _UpperCAmelCase	):
    a      :				Any     =				['image_processor', 'tokenizer']
    a      :				Optional[int]     =				'AutoImageProcessor'
    a      :				Any     =				'AutoTokenizer'







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






        '''simple docstring'''






        _UpperCAmelCase          =   None
        if "feature_extractor" in kwargs:
            warnings.warn(
                """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                """ instead."""     ,					__UpperCamelCase     ,					)
            _UpperCAmelCase          =   kwargs.pop("""feature_extractor"""     )

        _UpperCAmelCase          =   image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
            raise ValueError("""You need to specify an `image_processor`."""     )
        if tokenizer is None:
            raise ValueError("""You need to specify a `tokenizer`."""     )

        super().__init__(__UpperCamelCase     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   self.image_processor
        _UpperCAmelCase          =   False







    def __call__(			self			:       Union[str, Any]     ,					*__UpperCamelCase			:       str     ,					**__UpperCamelCase			:       Optional[Any]     )		->List[str]:






        '''simple docstring'''






        if self._in_target_context_manager:
            return self.current_processor(*__UpperCamelCase     ,					**__UpperCamelCase     )

        _UpperCAmelCase          =   kwargs.pop("""images"""     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   kwargs.pop("""text"""     ,					__UpperCamelCase     )
        if len(__UpperCamelCase     ) > 0:
            _UpperCAmelCase          =   args[0]
            _UpperCAmelCase          =   args[1:]

        if images is None and text is None:
            raise ValueError("""You need to specify either an `images` or `text` input to process."""     )

        if images is not None:
            _UpperCAmelCase          =   self.image_processor(__UpperCamelCase     ,					*__UpperCamelCase     ,					**__UpperCamelCase     )
        if text is not None:
            _UpperCAmelCase          =   self.tokenizer(__UpperCamelCase     ,					**__UpperCamelCase     )

        if text is None:
            return inputs
        elif images is None:
            return encodings
        else:
            _UpperCAmelCase          =   encodings["""input_ids"""]
            return inputs







    def      _snake_case					(			self			:       Union[str, Any]     ,					*__UpperCamelCase			:       int     ,					**__UpperCamelCase			:       Tuple     )		->Tuple:






        '''simple docstring'''






        return self.tokenizer.batch_decode(*__UpperCamelCase     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       Tuple     ,					*__UpperCamelCase			:       int     ,					**__UpperCamelCase			:       Union[str, Any]     )		->int:






        '''simple docstring'''






        return self.tokenizer.decode(*__UpperCamelCase     ,					**__UpperCamelCase     )







    @contextmanager
    def      _snake_case					(			self			:       Tuple     )		->Union[str, Any]:






        '''simple docstring'''






        warnings.warn(
            """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
            """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
            """your images inputs, or in a separate call."""     )
        _UpperCAmelCase          =   True
        _UpperCAmelCase          =   self.tokenizer
        yield
        _UpperCAmelCase          =   self.image_processor
        _UpperCAmelCase          =   False







    def      _snake_case					(			self			:       str     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Optional[Any]=False     ,					__UpperCamelCase			:       Union[str, Any]=None     )		->List[str]:






        '''simple docstring'''






        if added_vocab is None:
            _UpperCAmelCase          =   self.tokenizer.get_added_vocab()

        _UpperCAmelCase          =   {}

        while tokens:
            _UpperCAmelCase          =   re.search(r"""<s_(.*?)>"""     ,					__UpperCamelCase     ,					re.IGNORECASE     )
            if start_token is None:
                break
            _UpperCAmelCase          =   start_token.group(1     )
            _UpperCAmelCase          =   re.search(rf"""</s_{key}>"""     ,					__UpperCamelCase     ,					re.IGNORECASE     )
            _UpperCAmelCase          =   start_token.group()
            if end_token is None:
                _UpperCAmelCase          =   tokens.replace(__UpperCamelCase     ,					""""""     )
            else:
                _UpperCAmelCase          =   end_token.group()
                _UpperCAmelCase          =   re.escape(__UpperCamelCase     )
                _UpperCAmelCase          =   re.escape(__UpperCamelCase     )
                _UpperCAmelCase          =   re.search(f"""{start_token_escaped}(.*?){end_token_escaped}"""     ,					__UpperCamelCase     ,					re.IGNORECASE     )
                if content is not None:
                    _UpperCAmelCase          =   content.group(1     ).strip()
                    if r"<s_" in content and r"</s_" in content:  # non-leaf node
                        _UpperCAmelCase          =   self.tokenajson(__UpperCamelCase     ,					is_inner_value=__UpperCamelCase     ,					added_vocab=__UpperCamelCase     )
                        if value:
                            if len(__UpperCamelCase     ) == 1:
                                _UpperCAmelCase          =   value[0]
                            _UpperCAmelCase          =   value
                    else:  # leaf nodes
                        _UpperCAmelCase          =   []
                        for leaf in content.split(r"""<sep/>"""     ):
                            _UpperCAmelCase          =   leaf.strip()
                            if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                                _UpperCAmelCase          =   leaf[1:-2]  # for categorical special tokens
                            output[key].append(__UpperCamelCase     )
                        if len(output[key]     ) == 1:
                            _UpperCAmelCase          =   output[key][0]

                _UpperCAmelCase          =   tokens[tokens.find(__UpperCamelCase     ) + len(__UpperCamelCase     ) :].strip()
                if tokens[:6] == r"<sep/>":  # non-leaf nodes
                    return [output] + self.tokenajson(tokens[6:]     ,					is_inner_value=__UpperCamelCase     ,					added_vocab=__UpperCamelCase     )

        if len(__UpperCamelCase     ):
            return [output] if is_inner_value else output
        else:
            return [] if is_inner_value else {"text_sequence": tokens}







    @property
    def      _snake_case					(			self			:       Tuple     )		->Optional[int]:






        '''simple docstring'''






        warnings.warn(
            """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""     ,					__UpperCamelCase     ,					)
        return self.image_processor_class







    @property
    def      _snake_case					(			self			:       List[str]     )		->Dict:






        '''simple docstring'''






        warnings.warn(
            """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""     ,					__UpperCamelCase     ,					)
        return self.image_processor

"""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
 a					:	str							=		'''platform'''
 import jax
 import jax.numpy as jnp
 import numpy as np

 from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel




@require_flax
class 		a_						:
    a      :				List[Any]     =				PegasusConfig
    a      :				Dict     =				{}
    a      :				List[Any]     =				'gelu'







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






        '''simple docstring'''






        _UpperCAmelCase          =   parent
        _UpperCAmelCase          =   batch_size
        _UpperCAmelCase          =   seq_length
        _UpperCAmelCase          =   is_training
        _UpperCAmelCase          =   use_labels
        _UpperCAmelCase          =   vocab_size
        _UpperCAmelCase          =   hidden_size
        _UpperCAmelCase          =   num_hidden_layers
        _UpperCAmelCase          =   num_attention_heads
        _UpperCAmelCase          =   intermediate_size

        _UpperCAmelCase          =   hidden_dropout_prob
        _UpperCAmelCase          =   attention_probs_dropout_prob
        _UpperCAmelCase          =   max_position_embeddings
        _UpperCAmelCase          =   eos_token_id
        _UpperCAmelCase          =   pad_token_id
        _UpperCAmelCase          =   bos_token_id







    def      _snake_case					(			self			:       Union[str, Any]     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   ids_tensor([self.batch_size, self.seq_length - 1]     ,					self.vocab_size     ).clip(3     ,					self.vocab_size     )
        _UpperCAmelCase          =   np.expand_dims(np.array([self.eos_token_id] * self.batch_size     )     ,					1     )
        _UpperCAmelCase          =   np.concatenate([input_ids, eos_tensor]     ,					axis=1     )

        _UpperCAmelCase          =   ids_tensor([self.batch_size, self.seq_length]     ,					self.vocab_size     )

        _UpperCAmelCase          =   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     ,					)
        _UpperCAmelCase          =   prepare_pegasus_inputs_dict(__UpperCamelCase     ,					__UpperCamelCase     ,					__UpperCamelCase     )
        return config, inputs_dict







    def      _snake_case					(			self			:       Tuple     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       List[Any]     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   20
        _UpperCAmelCase          =   model_class_name(__UpperCamelCase     )

        _UpperCAmelCase          =   model.encode(inputs_dict["""input_ids"""]     )

        _UpperCAmelCase    ,_UpperCAmelCase          =   (
            inputs_dict["""decoder_input_ids"""],
            inputs_dict["""decoder_attention_mask"""],
        )

        _UpperCAmelCase          =   model.init_cache(decoder_input_ids.shape[0]     ,					__UpperCamelCase     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,					dtype="""i4"""     )

        _UpperCAmelCase          =   jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]     ,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,					)
        _UpperCAmelCase          =   model.decode(
            decoder_input_ids[:, :-1]     ,					__UpperCamelCase     ,					decoder_attention_mask=__UpperCamelCase     ,					past_key_values=__UpperCamelCase     ,					decoder_position_ids=__UpperCamelCase     ,					)

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

        _UpperCAmelCase          =   model.decode(__UpperCamelCase     ,					__UpperCamelCase     )

        _UpperCAmelCase          =   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      _snake_case					(			self			:       Any     ,					__UpperCamelCase			:       List[str]     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Union[str, Any]     )		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   20
        _UpperCAmelCase          =   model_class_name(__UpperCamelCase     )

        _UpperCAmelCase          =   model.encode(inputs_dict["""input_ids"""]     )

        _UpperCAmelCase    ,_UpperCAmelCase          =   (
            inputs_dict["""decoder_input_ids"""],
            inputs_dict["""decoder_attention_mask"""],
        )

        _UpperCAmelCase          =   jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])     ),
            ]     ,					axis=-1     ,					)

        _UpperCAmelCase          =   model.init_cache(decoder_input_ids.shape[0]     ,					__UpperCamelCase     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]     ,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,					)

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

        _UpperCAmelCase          =   model.decode(__UpperCamelCase     ,					__UpperCamelCase     ,					decoder_attention_mask=__UpperCamelCase     )

        _UpperCAmelCase          =   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	(						_A					,      _A					,      _A					,      _A=None					,      _A=None					,      )    ->  int:





    """simple docstring"""

    if attention_mask is None:
        _UpperCAmelCase          =   np.not_equal(_A					,      config.pad_token_id      ).astype(np.inta      )
    if decoder_attention_mask is None:
        _UpperCAmelCase          =   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 		a_						( _UpperCAmelCase     ,						unittest.TestCase	):
    a      :				List[str]     =				(
        (
            FlaxPegasusForConditionalGeneration,
            FlaxPegasusModel,
        )
        if is_flax_available()
        else ()
    )
    a      :				Any     =				(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
    a      :				Any     =				True
    a      :				int     =				False
    a      :				Union[str, Any]     =				False
    a      :				Optional[int]     =				False







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






        '''simple docstring'''






        _UpperCAmelCase          =   FlaxPegasusModelTester(self     )
        _UpperCAmelCase          =   ConfigTester(self     ,					config_class=__UpperCamelCase     )







    def      _snake_case					(			self			:       Optional[int]     )		->Union[str, Any]:






        '''simple docstring'''






        self.config_tester.run_common_tests()







    def      _snake_case					(			self			:       Optional[int]     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   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      _snake_case					(			self			:       Dict     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   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      _snake_case					(			self			:       Dict     )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__     ):
                _UpperCAmelCase          =   self._prepare_for_class(__UpperCamelCase     ,					__UpperCamelCase     )
                _UpperCAmelCase          =   model_class(__UpperCamelCase     )

                @jax.jit
                def encode_jitted(__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       str=None     ,					**__UpperCamelCase			:       int     ):
                    return model.encode(input_ids=__UpperCamelCase     ,					attention_mask=__UpperCamelCase     )

                with self.subTest("""JIT Enabled"""     ):
                    _UpperCAmelCase          =   encode_jitted(**__UpperCamelCase     ).to_tuple()

                with self.subTest("""JIT Disabled"""     ):
                    with jax.disable_jit():
                        _UpperCAmelCase          =   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      _snake_case					(			self			:       List[Any]     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__     ):
                _UpperCAmelCase          =   model_class(__UpperCamelCase     )
                _UpperCAmelCase          =   model.encode(inputs_dict["""input_ids"""]     ,					inputs_dict["""attention_mask"""]     )

                _UpperCAmelCase          =   {
                    """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			:       Union[str, Any]     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Tuple     ):
                    return model.decode(
                        decoder_input_ids=__UpperCamelCase     ,					decoder_attention_mask=__UpperCamelCase     ,					encoder_outputs=__UpperCamelCase     ,					)

                with self.subTest("""JIT Enabled"""     ):
                    _UpperCAmelCase          =   decode_jitted(**__UpperCamelCase     ).to_tuple()

                with self.subTest("""JIT Disabled"""     ):
                    with jax.disable_jit():
                        _UpperCAmelCase          =   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      _snake_case					(			self			:       int     )		->int:






        '''simple docstring'''






        for model_class_name in self.all_model_classes:
            _UpperCAmelCase          =   model_class_name.from_pretrained("""google/pegasus-large"""     ,					from_pt=__UpperCamelCase     )
            _UpperCAmelCase          =   np.ones((1, 1)     )
            _UpperCAmelCase          =   model(__UpperCamelCase     )
            self.assertIsNotNone(__UpperCamelCase     )







    @slow
    def      _snake_case					(			self			:       Dict     )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum"""     )
        _UpperCAmelCase          =   PegasusTokenizer.from_pretrained("""google/pegasus-xsum"""     )

        _UpperCAmelCase          =   [
            """ 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!\" """,
        ]

        _UpperCAmelCase          =   [
            """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.""",
        ]

        _UpperCAmelCase          =   tokenizer(__UpperCamelCase     ,					return_tensors="""np"""     ,					truncation=__UpperCamelCase     ,					max_length=5_12     ,					padding=__UpperCamelCase     )
        _UpperCAmelCase          =   model.generate(**__UpperCamelCase     ,					num_beams=2     ).sequences
        _UpperCAmelCase          =   tokenizer.batch_decode(__UpperCamelCase     ,					skip_special_tokens=__UpperCamelCase     )
        assert tgt_text == decoded