code: 
  
  
  import os
  import unittest
  
  from transformers import MPNetTokenizerFast
  from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
  from transformers.testing_utils import require_tokenizers, slow
  
  from ...test_tokenization_common import TokenizerTesterMixin
  
  
  
  
  
  @require_tokenizers
  class        lowercase_				( UpperCamelCase__			,unittest.TestCase):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		MPNetTokenizer
  							snake_case_        =		MPNetTokenizerFast
  							snake_case_        =		True
  							snake_case_        =		True
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													super().setUp()
  
  													a_					=		[
  													    """[UNK]""",
  													    """[CLS]""",
  													    """[SEP]""",
  													    """[PAD]""",
  													    """[MASK]""",
  													    """want""",
  													    """##want""",
  													    """##ed""",
  													    """wa""",
  													    """un""",
  													    """runn""",
  													    """##ing""",
  													    """,""",
  													    """low""",
  													    """lowest""",
  													]
  													a_					=		os.path.join(self.tmpdirname			,    VOCAB_FILES_NAMES["""vocab_file"""]		)
  													with open(self.vocab_file			,    """w"""			,    encoding="""utf-8"""		) as vocab_writer:
  																			vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]		)		)
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		"""UNwant\u00E9d,running"""
  													a_					=		"""unwanted, running"""
  													return input_text, output_text
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.tokenizer_class(self.vocab_file		)
  
  													a_					=		tokenizer.tokenize("""UNwant\u00E9d,running"""		)
  													self.assertListEqual(_UpperCAmelCase			,    ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]		)
  													self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)			,    [9, 6, 7, 12, 10, 11]		)
  
  
  
  
  
  
  							@slow
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.tokenizer_class.from_pretrained("""microsoft/mpnet-base"""		)
  
  													a_					=		tokenizer.encode("""sequence builders"""			,    add_special_tokens=_UpperCAmelCase		)
  													a_					=		tokenizer.encode("""multi-sequence build"""			,    add_special_tokens=_UpperCAmelCase		)
  
  													a_					=		tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase		)
  													a_					=		tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  													assert encoded_sentence == [0] + text + [2]
  													assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]
style_context: 
  
  
  import unittest
  
  import numpy as np
  import timeout_decorator  # noqa
  
  from transformers import MBartConfig, is_flax_available
  from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
  from transformers.utils import cached_property
  
  from ...generation.test_flax_utils import FlaxGenerationTesterMixin
  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_    :					List[Any]             ="""platform"""
  
  	import jax
  	import jax.numpy as jnp
  
  	from transformers import AutoTokenizer
  	from transformers.models.mbart.modeling_flax_mbart import (
  	    FlaxMBartForConditionalGeneration,
  	    FlaxMBartForQuestionAnswering,
  	    FlaxMBartForSequenceClassification,
  	    FlaxMBartModel,
  	    shift_tokens_right,
  	)
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__=None       ,    UpperCAmelCase__=None       ,    UpperCAmelCase__=None       ,    UpperCAmelCase__=None       ,    UpperCAmelCase__=None       ,    UpperCAmelCase__=None       ,    ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						if attention_mask is None:
  												a_					=		np.where(input_ids != config.pad_token_id       ,    1       ,    0      )
  						if decoder_attention_mask is None:
  												a_					=		np.where(decoder_input_ids != config.pad_token_id       ,    1       ,    0      )
  						if head_mask is None:
  												a_					=		np.ones((config.encoder_layers, config.encoder_attention_heads)      )
  						if decoder_head_mask is None:
  												a_					=		np.ones((config.decoder_layers, config.decoder_attention_heads)      )
  						if cross_attn_head_mask is None:
  												a_					=		np.ones((config.decoder_layers, config.decoder_attention_heads)      )
  						return {
  						    "input_ids": input_ids,
  						    "decoder_input_ids": decoder_input_ids,
  						    "attention_mask": attention_mask,
  						    "decoder_attention_mask": decoder_attention_mask,
  						}
  
  
  
  
  
  class        lowercase_				( unittest.TestCase):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							def __init__(   self			,    _UpperCAmelCase			,    _UpperCAmelCase=13			,    _UpperCAmelCase=7			,    _UpperCAmelCase=True			,    _UpperCAmelCase=False			,    _UpperCAmelCase=99			,    _UpperCAmelCase=16			,    _UpperCAmelCase=2			,    _UpperCAmelCase=4			,    _UpperCAmelCase=4			,    _UpperCAmelCase="gelu"			,    _UpperCAmelCase=0.1			,    _UpperCAmelCase=0.1			,    _UpperCAmelCase=32			,    _UpperCAmelCase=2			,    _UpperCAmelCase=1			,    _UpperCAmelCase=0			,    _UpperCAmelCase=2			,    _UpperCAmelCase=0.0_2			,    ):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		parent
  													a_					=		batch_size
  													a_					=		seq_length
  													a_					=		is_training
  													a_					=		use_labels
  													a_					=		vocab_size
  													a_					=		hidden_size
  													a_					=		num_hidden_layers
  													a_					=		num_attention_heads
  													a_					=		intermediate_size
  													a_					=		hidden_act
  													a_					=		hidden_dropout_prob
  													a_					=		attention_probs_dropout_prob
  													a_					=		max_position_embeddings
  													a_					=		eos_token_id
  													a_					=		pad_token_id
  													a_					=		bos_token_id
  													a_					=		decoder_start_token_id
  													a_					=		initializer_range
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,    self.vocab_size		)			,    3			,    self.vocab_size		)
  													a_					=		np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,    dtype=np.intaa		))			,    -1		)
  
  													a_					=		shift_tokens_right(_UpperCAmelCase			,    1		)
  
  													a_					=		MBartConfig(
  													    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_id=self.eos_token_id			,    bos_token_id=self.bos_token_id			,    pad_token_id=self.pad_token_id			,    decoder_start_token_id=self.decoder_start_token_id			,    initializer_range=self.initializer_range			,    use_cache=_UpperCAmelCase			,    )
  													a_					=		prepare_mbart_inputs_dict(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)
  													return config, inputs_dict
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_			,   a_					=		self.prepare_config_and_inputs()
  													return config, inputs_dict
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		20
  													a_					=		model_class_name(_UpperCAmelCase		)
  
  													a_					=		model.encode(inputs_dict["""input_ids"""]		)
  
  													a_			,   a_					=		(
  													    inputs_dict["""decoder_input_ids"""],
  													    inputs_dict["""decoder_attention_mask"""],
  													)
  
  													a_					=		model.init_cache(decoder_input_ids.shape[0]			,    _UpperCAmelCase			,    _UpperCAmelCase		)
  													a_					=		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			,    dtype="""i4"""		)
  
  													a_					=		jnp.broadcast_to(
  													    jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]			,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,    )
  													a_					=		model.decode(
  													    decoder_input_ids[:, :-1]			,    _UpperCAmelCase			,    decoder_attention_mask=_UpperCAmelCase			,    past_key_values=_UpperCAmelCase			,    decoder_position_ids=_UpperCAmelCase			,    )
  
  													a_					=		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,    dtype="""i4"""		)
  													a_					=		model.decode(
  													    decoder_input_ids[:, -1:]			,    _UpperCAmelCase			,    decoder_attention_mask=_UpperCAmelCase			,    past_key_values=outputs_cache.past_key_values			,    decoder_position_ids=_UpperCAmelCase			,    )
  
  													a_					=		model.decode(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  													a_					=		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 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		20
  													a_					=		model_class_name(_UpperCAmelCase		)
  
  													a_					=		model.encode(inputs_dict["""input_ids"""]		)
  
  													a_			,   a_					=		(
  													    inputs_dict["""decoder_input_ids"""],
  													    inputs_dict["""decoder_attention_mask"""],
  													)
  
  													a_					=		jnp.concatenate(
  													    [
  													        decoder_attention_mask,
  													        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])		),
  													    ]			,    axis=-1			,    )
  
  													a_					=		model.init_cache(decoder_input_ids.shape[0]			,    _UpperCAmelCase			,    _UpperCAmelCase		)
  													a_					=		jnp.broadcast_to(
  													    jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]			,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,    )
  
  													a_					=		model.decode(
  													    decoder_input_ids[:, :-1]			,    _UpperCAmelCase			,    decoder_attention_mask=_UpperCAmelCase			,    past_key_values=_UpperCAmelCase			,    decoder_position_ids=_UpperCAmelCase			,    )
  													a_					=		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,    dtype="""i4"""		)
  													a_					=		model.decode(
  													    decoder_input_ids[:, -1:]			,    _UpperCAmelCase			,    past_key_values=outputs_cache.past_key_values			,    decoder_attention_mask=_UpperCAmelCase			,    decoder_position_ids=_UpperCAmelCase			,    )
  
  													a_					=		model.decode(_UpperCAmelCase			,    _UpperCAmelCase			,    decoder_attention_mask=_UpperCAmelCase		)
  
  													a_					=		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}"		)
  
  
  
  
  
  @require_flax
  class        lowercase_				( unittest.TestCase):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		99
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		np.array(
  													    [
  													        [71, 82, 18, 33, 46, 91, 2],
  													        [68, 34, 26, 58, 30, 82, 2],
  													        [5, 97, 17, 39, 94, 40, 2],
  													        [76, 83, 94, 25, 70, 78, 2],
  													        [87, 59, 41, 35, 48, 66, 2],
  													        [55, 13, 16, 58, 5, 2, 1],  # note padding
  													        [64, 27, 31, 51, 12, 75, 2],
  													        [52, 64, 86, 17, 83, 39, 2],
  													        [48, 61, 9, 24, 71, 82, 2],
  													        [26, 1, 60, 48, 22, 13, 2],
  													        [21, 5, 62, 28, 14, 76, 2],
  													        [45, 98, 37, 86, 59, 48, 2],
  													        [70, 70, 50, 9, 28, 0, 2],
  													    ]			,    dtype=np.intaa			,    )
  
  													a_					=		input_ids.shape[0]
  													a_					=		MBartConfig(
  													    vocab_size=self.vocab_size			,    d_model=24			,    encoder_layers=2			,    decoder_layers=2			,    encoder_attention_heads=2			,    decoder_attention_heads=2			,    encoder_ffn_dim=32			,    decoder_ffn_dim=32			,    max_position_embeddings=48			,    eos_token_id=2			,    pad_token_id=1			,    bos_token_id=0			,    )
  													return config, input_ids, batch_size
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_			,   a_			,   a_					=		self._get_config_and_data()
  													a_					=		FlaxMBartForSequenceClassification(_UpperCAmelCase		)
  													a_					=		model(input_ids=_UpperCAmelCase			,    decoder_input_ids=_UpperCAmelCase		)
  													a_					=		(batch_size, config.num_labels)
  													self.assertEqual(outputs["""logits"""].shape			,    _UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_			,   a_			,   a_					=		self._get_config_and_data()
  													a_					=		FlaxMBartForQuestionAnswering(_UpperCAmelCase		)
  													a_					=		model(input_ids=_UpperCAmelCase		)
  
  													self.assertEqual(outputs["""start_logits"""].shape			,    input_ids.shape		)
  													self.assertEqual(outputs["""end_logits"""].shape			,    input_ids.shape		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_			,   a_			,   a_					=		self._get_config_and_data()
  													a_					=		FlaxMBartForConditionalGeneration(_UpperCAmelCase		)
  													a_					=		lm_model(input_ids=_UpperCAmelCase		)
  													a_					=		(batch_size, input_ids.shape[1], config.vocab_size)
  													self.assertEqual(outputs["""logits"""].shape			,    _UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		MBartConfig(
  													    vocab_size=self.vocab_size			,    d_model=14			,    encoder_layers=2			,    decoder_layers=2			,    encoder_attention_heads=2			,    decoder_attention_heads=2			,    encoder_ffn_dim=8			,    decoder_ffn_dim=8			,    max_position_embeddings=48			,    )
  													a_					=		FlaxMBartForConditionalGeneration(_UpperCAmelCase		)
  													a_					=		np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]			,    dtype=np.intaa		)
  													a_					=		np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]			,    dtype=np.intaa		)
  													a_					=		lm_model(input_ids=_UpperCAmelCase			,    decoder_input_ids=_UpperCAmelCase		)
  													a_					=		(*summary.shape, config.vocab_size)
  													self.assertEqual(outputs["""logits"""].shape			,    _UpperCAmelCase		)
  
  
  
  
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]			,    dtype=np.intaa		)
  													a_					=		shift_tokens_right(_UpperCAmelCase			,    1		)
  													a_					=		np.equal(_UpperCAmelCase			,    1		).astype(np.floataa		).sum()
  													a_					=		np.equal(_UpperCAmelCase			,    1		).astype(np.floataa		).sum()
  													self.assertEqual(shifted.shape			,    input_ids.shape		)
  													self.assertEqual(_UpperCAmelCase			,    n_pad_before - 1		)
  													self.assertTrue(np.equal(shifted[:, 0]			,    2		).all()		)
  
  
  
  
  
  @require_flax
  class        lowercase_				( UpperCamelCase__			,unittest.TestCase			,UpperCamelCase__):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		True
  							snake_case_        =		(
  							    (
  							        FlaxMBartModel,
  							        FlaxMBartForConditionalGeneration,
  							        FlaxMBartForSequenceClassification,
  							        FlaxMBartForQuestionAnswering,
  							    )
  							    if is_flax_available()
  							    else ()
  							)
  							snake_case_        =		(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		FlaxMBartModelTester(self		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_			,   a_					=		self.model_tester.prepare_config_and_inputs()
  													for model_class in self.all_model_classes:
  																			self.model_tester.check_use_cache_forward(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_			,   a_					=		self.model_tester.prepare_config_and_inputs()
  													for model_class in self.all_model_classes:
  																			self.model_tester.check_use_cache_forward_with_attn_mask(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_			,   a_					=		self.model_tester.prepare_config_and_inputs_for_common()
  
  													for model_class in self.all_model_classes:
  																			with self.subTest(model_class.__name__		):
  																									a_					=		self._prepare_for_class(_UpperCAmelCase			,    _UpperCAmelCase		)
  																									a_					=		model_class(_UpperCAmelCase		)
  
  																									@jax.jit
  																									def encode_jitted(_UpperCAmelCase			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):
  																															return model.encode(input_ids=_UpperCAmelCase			,    attention_mask=_UpperCAmelCase		)
  
  																									with self.subTest("""JIT Enabled"""		):
  																															a_					=		encode_jitted(**_UpperCAmelCase		).to_tuple()
  
  																									with self.subTest("""JIT Disabled"""		):
  																															with jax.disable_jit():
  																																					a_					=		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 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_			,   a_					=		self.model_tester.prepare_config_and_inputs_for_common()
  
  													for model_class in self.all_model_classes:
  																			with self.subTest(model_class.__name__		):
  																									a_					=		model_class(_UpperCAmelCase		)
  																									a_					=		model.encode(inputs_dict["""input_ids"""]			,    inputs_dict["""attention_mask"""]		)
  
  																									a_					=		{
  																									    """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			,    _UpperCAmelCase			,    _UpperCAmelCase		):
  																															return model.decode(
  																															    decoder_input_ids=_UpperCAmelCase			,    decoder_attention_mask=_UpperCAmelCase			,    encoder_outputs=_UpperCAmelCase			,    )
  
  																									with self.subTest("""JIT Enabled"""		):
  																															a_					=		decode_jitted(**_UpperCAmelCase		).to_tuple()
  
  																									with self.subTest("""JIT Disabled"""		):
  																															with jax.disable_jit():
  																																					a_					=		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 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for model_class_name in self.all_model_classes:
  																			a_					=		model_class_name.from_pretrained("""facebook/mbart-large-cc25"""			,    from_pt=_UpperCAmelCase		)
  																			# FlaxMBartForSequenceClassification expects eos token in input_ids
  																			a_					=		np.ones((1, 1)		) * model.config.eos_token_id
  																			a_					=		model(_UpperCAmelCase		)
  																			self.assertIsNotNone(_UpperCAmelCase		)
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class        lowercase_				( unittest.TestCase):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		[
  							    ''' UN Chief Says There Is No Military Solution in Syria''',
  							]
  							snake_case_        =		[
  							    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
  							]
  							snake_case_        =		'''facebook/mbart-large-en-ro'''
  
  
  							@cached_property
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return AutoTokenizer.from_pretrained(self.model_name		)
  
  
  							@cached_property
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		FlaxMBartForConditionalGeneration.from_pretrained(self.model_name			,    from_pt=_UpperCAmelCase		)
  													return model
  
  
  							def 						lowercase__     (   self			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.translate_src_text(**_UpperCAmelCase		)
  													self.assertListEqual(self.expected_text			,    _UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.tokenizer(self.src_text			,    **_UpperCAmelCase			,    return_tensors="""np"""		)
  													a_					=		self.model.generate(
  													    model_inputs.input_ids			,    attention_mask=model_inputs.attention_mask			,    decoder_start_token_id=self.tokenizer.lang_code_to_id["""ro_RO"""]			,    early_stopping=_UpperCAmelCase			,    num_beams=2			,    ).sequences
  													a_					=		self.tokenizer.batch_decode(_UpperCAmelCase			,    skip_special_tokens=_UpperCAmelCase		)
  													return generated_words
  
  
  
  
  
  
  							@slow
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													self._assert_generated_batch_equal_expected()