code: 
  
  
  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   				=      '''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 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase=None,    _UpperCamelCase=None,    _UpperCamelCase=None,    _UpperCamelCase=None,    _UpperCamelCase=None,    _UpperCamelCase=None,    )    ->Optional[int]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        if attention_mask is None:
              lowercase			:   str					   =		np.where(input_ids != config.pad_token_id,    1,    0     )
        if decoder_attention_mask is None:
              lowercase			:   Dict					   =		np.where(decoder_input_ids != config.pad_token_id,    1,    0     )
        if head_mask is None:
              lowercase			:   Union[str, Any]					   =		np.ones((config.encoder_layers, config.encoder_attention_heads)     )
        if decoder_head_mask is None:
              lowercase			:   Optional[Any]					   =		np.ones((config.decoder_layers, config.decoder_attention_heads)     )
        if cross_attn_head_mask is None:
              lowercase			:   Optional[int]					   =		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 				__SCREAMING_SNAKE_CASE   (			unittest.TestCase   ):
  
  
        def __init__(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__=13							,						SCREAMING_SNAKE_CASE__=7							,						SCREAMING_SNAKE_CASE__=True							,						SCREAMING_SNAKE_CASE__=False							,						SCREAMING_SNAKE_CASE__=99							,						SCREAMING_SNAKE_CASE__=16							,						SCREAMING_SNAKE_CASE__=2							,						SCREAMING_SNAKE_CASE__=4							,						SCREAMING_SNAKE_CASE__=4							,						SCREAMING_SNAKE_CASE__="gelu"							,						SCREAMING_SNAKE_CASE__=0.1							,						SCREAMING_SNAKE_CASE__=0.1							,						SCREAMING_SNAKE_CASE__=32							,						SCREAMING_SNAKE_CASE__=2							,						SCREAMING_SNAKE_CASE__=1							,						SCREAMING_SNAKE_CASE__=0							,						SCREAMING_SNAKE_CASE__=2							,						SCREAMING_SNAKE_CASE__=0.02							,						):
              lowercase			:   Optional[int]					   =		parent
              lowercase			:   List[str]					   =		batch_size
              lowercase			:   List[str]					   =		seq_length
              lowercase			:   Tuple					   =		is_training
              lowercase			:   str					   =		use_labels
              lowercase			:   str					   =		vocab_size
              lowercase			:   List[Any]					   =		hidden_size
              lowercase			:   int					   =		num_hidden_layers
              lowercase			:   List[Any]					   =		num_attention_heads
              lowercase			:   Union[str, Any]					   =		intermediate_size
              lowercase			:   str					   =		hidden_act
              lowercase			:   str					   =		hidden_dropout_prob
              lowercase			:   Optional[Any]					   =		attention_probs_dropout_prob
              lowercase			:   List[str]					   =		max_position_embeddings
              lowercase			:   List[str]					   =		eos_token_id
              lowercase			:   Dict					   =		pad_token_id
              lowercase			:   str					   =		bos_token_id
              lowercase			:   List[str]					   =		decoder_start_token_id
              lowercase			:   Dict					   =		initializer_range
  
  
        def        __lowerCamelCase			(							self		):
              lowercase			:   str					   =		np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,						self.vocab_size		)							,						3							,						self.vocab_size		)
              lowercase			:   Any					   =		np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,						dtype=np.intaa		))							,						-1		)
  
              lowercase			:   Optional[Any]					   =		shift_tokens_right(SCREAMING_SNAKE_CASE__							,						1		)
  
              lowercase			:   Optional[Any]					   =		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=SCREAMING_SNAKE_CASE__							,						)
              lowercase			:   str					   =		prepare_mbart_inputs_dict(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		)
              return config, inputs_dict
  
  
        def        __lowerCamelCase			(							self		):
              lowercase   ,			lowercase			:   Tuple					   =		self.prepare_config_and_inputs()
              return config, inputs_dict
  
  
        def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
              lowercase			:   Union[str, Any]					   =		20
              lowercase			:   Optional[int]					   =		model_class_name(SCREAMING_SNAKE_CASE__		)
  
              lowercase			:   Tuple					   =		model.encode(inputs_dict['''input_ids''']		)
  
              lowercase   ,			lowercase			:   Tuple					   =		(
                  inputs_dict['''decoder_input_ids'''],
                  inputs_dict['''decoder_attention_mask'''],
              )
  
              lowercase			:   str					   =		model.init_cache(decoder_input_ids.shape[0]							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		)
              lowercase			:   Union[str, Any]					   =		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,						dtype='''i4'''		)
  
              lowercase			:   Dict					   =		jnp.broadcast_to(
                  jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]							,						(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,						)
              lowercase			:   Any					   =		model.decode(
                  decoder_input_ids[:, :-1]							,						SCREAMING_SNAKE_CASE__							,						decoder_attention_mask=SCREAMING_SNAKE_CASE__							,						past_key_values=SCREAMING_SNAKE_CASE__							,						decoder_position_ids=SCREAMING_SNAKE_CASE__							,						)
  
              lowercase			:   List[str]					   =		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,						dtype='''i4'''		)
              lowercase			:   Optional[int]					   =		model.decode(
                  decoder_input_ids[:, -1:]							,						SCREAMING_SNAKE_CASE__							,						decoder_attention_mask=SCREAMING_SNAKE_CASE__							,						past_key_values=outputs_cache.past_key_values							,						decoder_position_ids=SCREAMING_SNAKE_CASE__							,						)
  
              lowercase			:   int					   =		model.decode(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		)
  
              lowercase			:   Optional[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							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
              lowercase			:   Any					   =		20
              lowercase			:   Optional[int]					   =		model_class_name(SCREAMING_SNAKE_CASE__		)
  
              lowercase			:   Any					   =		model.encode(inputs_dict['''input_ids''']		)
  
              lowercase   ,			lowercase			:   Tuple					   =		(
                  inputs_dict['''decoder_input_ids'''],
                  inputs_dict['''decoder_attention_mask'''],
              )
  
              lowercase			:   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							,						)
  
              lowercase			:   str					   =		model.init_cache(decoder_input_ids.shape[0]							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		)
              lowercase			:   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)							,						)
  
              lowercase			:   Any					   =		model.decode(
                  decoder_input_ids[:, :-1]							,						SCREAMING_SNAKE_CASE__							,						decoder_attention_mask=SCREAMING_SNAKE_CASE__							,						past_key_values=SCREAMING_SNAKE_CASE__							,						decoder_position_ids=SCREAMING_SNAKE_CASE__							,						)
              lowercase			:   int					   =		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,						dtype='''i4'''		)
              lowercase			:   Union[str, Any]					   =		model.decode(
                  decoder_input_ids[:, -1:]							,						SCREAMING_SNAKE_CASE__							,						past_key_values=outputs_cache.past_key_values							,						decoder_attention_mask=SCREAMING_SNAKE_CASE__							,						decoder_position_ids=SCREAMING_SNAKE_CASE__							,						)
  
              lowercase			:   Union[str, Any]					   =		model.decode(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						decoder_attention_mask=SCREAMING_SNAKE_CASE__		)
  
              lowercase			:   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}"""		)
  
  
  
  
  
  
  
  @require_flax
  class 				__SCREAMING_SNAKE_CASE   (			unittest.TestCase   ):
        A				:							List[str]    							=     99
  
  
        def        __lowerCamelCase			(							self		):
              lowercase			:   int					   =		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							,						)
  
              lowercase			:   str					   =		input_ids.shape[0]
              lowercase			:   List[Any]					   =		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        __lowerCamelCase			(							self		):
              lowercase   ,			lowercase   ,			lowercase			:   Optional[int]					   =		self._get_config_and_data()
              lowercase			:   str					   =		FlaxMBartForSequenceClassification(SCREAMING_SNAKE_CASE__		)
              lowercase			:   Any					   =		model(input_ids=SCREAMING_SNAKE_CASE__							,						decoder_input_ids=SCREAMING_SNAKE_CASE__		)
              lowercase			:   Dict					   =		(batch_size, config.num_labels)
              self.assertEqual(outputs['''logits'''].shape							,						SCREAMING_SNAKE_CASE__		)
  
  
        def        __lowerCamelCase			(							self		):
              lowercase   ,			lowercase   ,			lowercase			:   int					   =		self._get_config_and_data()
              lowercase			:   Optional[Any]					   =		FlaxMBartForQuestionAnswering(SCREAMING_SNAKE_CASE__		)
              lowercase			:   Dict					   =		model(input_ids=SCREAMING_SNAKE_CASE__		)
  
              self.assertEqual(outputs['''start_logits'''].shape							,						input_ids.shape		)
              self.assertEqual(outputs['''end_logits'''].shape							,						input_ids.shape		)
  
  
        def        __lowerCamelCase			(							self		):
              lowercase   ,			lowercase   ,			lowercase			:   Optional[Any]					   =		self._get_config_and_data()
              lowercase			:   List[str]					   =		FlaxMBartForConditionalGeneration(SCREAMING_SNAKE_CASE__		)
              lowercase			:   str					   =		lm_model(input_ids=SCREAMING_SNAKE_CASE__		)
              lowercase			:   Union[str, Any]					   =		(batch_size, input_ids.shape[1], config.vocab_size)
              self.assertEqual(outputs['''logits'''].shape							,						SCREAMING_SNAKE_CASE__		)
  
  
        def        __lowerCamelCase			(							self		):
              lowercase			:   int					   =		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							,						)
              lowercase			:   Dict					   =		FlaxMBartForConditionalGeneration(SCREAMING_SNAKE_CASE__		)
              lowercase			:   List[Any]					   =		np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]							,						dtype=np.intaa		)
              lowercase			:   Dict					   =		np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]							,						dtype=np.intaa		)
              lowercase			:   Optional[Any]					   =		lm_model(input_ids=SCREAMING_SNAKE_CASE__							,						decoder_input_ids=SCREAMING_SNAKE_CASE__		)
              lowercase			:   Any					   =		(*summary.shape, config.vocab_size)
              self.assertEqual(outputs['''logits'''].shape							,						SCREAMING_SNAKE_CASE__		)
  
  
  
  
  
  
        def        __lowerCamelCase			(							self		):
              lowercase			:   str					   =		np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]							,						dtype=np.intaa		)
              lowercase			:   Union[str, Any]					   =		shift_tokens_right(SCREAMING_SNAKE_CASE__							,						1		)
              lowercase			:   List[str]					   =		np.equal(SCREAMING_SNAKE_CASE__							,						1		).astype(np.floataa		).sum()
              lowercase			:   List[str]					   =		np.equal(SCREAMING_SNAKE_CASE__							,						1		).astype(np.floataa		).sum()
              self.assertEqual(shifted.shape							,						input_ids.shape		)
              self.assertEqual(SCREAMING_SNAKE_CASE__							,						n_pad_before - 1		)
              self.assertTrue(np.equal(shifted[:, 0]							,						2		).all()		)
  
  
  
  
  
  
  
  @require_flax
  class 				__SCREAMING_SNAKE_CASE   (			A__  ,						unittest.TestCase  ,						A__   ):
        A				:							Tuple    							=     True
        A				:							Optional[Any]    							=     (
            (
                FlaxMBartModel,
                FlaxMBartForConditionalGeneration,
                FlaxMBartForSequenceClassification,
                FlaxMBartForQuestionAnswering,
            )
            if is_flax_available()
            else ()
        )
        A				:							Optional[Any]    							=     (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()
  
  
        def        __lowerCamelCase			(							self		):
              lowercase			:   Optional[int]					   =		FlaxMBartModelTester(self		)
  
  
        def        __lowerCamelCase			(							self		):
              lowercase   ,			lowercase			:   Dict					   =		self.model_tester.prepare_config_and_inputs()
              for model_class in self.all_model_classes:
                    self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		)
  
  
        def        __lowerCamelCase			(							self		):
              lowercase   ,			lowercase			:   Dict					   =		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(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		)
  
  
        def        __lowerCamelCase			(							self		):
              lowercase   ,			lowercase			:   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__		):
                          lowercase			:   str					   =		self._prepare_for_class(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		)
                          lowercase			:   Tuple					   =		model_class(SCREAMING_SNAKE_CASE__		)
  
                          @jax.jit
                          def encode_jitted(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__=None							,						**SCREAMING_SNAKE_CASE__		):
                                return model.encode(input_ids=SCREAMING_SNAKE_CASE__							,						attention_mask=SCREAMING_SNAKE_CASE__		)
  
                          with self.subTest('''JIT Enabled'''		):
                                lowercase			:   Dict					   =		encode_jitted(**SCREAMING_SNAKE_CASE__		).to_tuple()
  
                          with self.subTest('''JIT Disabled'''		):
                                with jax.disable_jit():
                                      lowercase			:   Union[str, Any]					   =		encode_jitted(**SCREAMING_SNAKE_CASE__		).to_tuple()
  
                          self.assertEqual(len(SCREAMING_SNAKE_CASE__		)							,						len(SCREAMING_SNAKE_CASE__		)		)
                          for jitted_output, output in zip(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
                                self.assertEqual(jitted_output.shape							,						output.shape		)
  
  
        def        __lowerCamelCase			(							self		):
              lowercase   ,			lowercase			:   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__		):
                          lowercase			:   Optional[int]					   =		model_class(SCREAMING_SNAKE_CASE__		)
                          lowercase			:   Any					   =		model.encode(inputs_dict['''input_ids''']							,						inputs_dict['''attention_mask''']		)
  
                          lowercase			:   int					   =		{
                              '''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(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
                                return model.decode(
                                    decoder_input_ids=SCREAMING_SNAKE_CASE__							,						decoder_attention_mask=SCREAMING_SNAKE_CASE__							,						encoder_outputs=SCREAMING_SNAKE_CASE__							,						)
  
                          with self.subTest('''JIT Enabled'''		):
                                lowercase			:   str					   =		decode_jitted(**SCREAMING_SNAKE_CASE__		).to_tuple()
  
                          with self.subTest('''JIT Disabled'''		):
                                with jax.disable_jit():
                                      lowercase			:   List[str]					   =		decode_jitted(**SCREAMING_SNAKE_CASE__		).to_tuple()
  
                          self.assertEqual(len(SCREAMING_SNAKE_CASE__		)							,						len(SCREAMING_SNAKE_CASE__		)		)
                          for jitted_output, output in zip(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
                                self.assertEqual(jitted_output.shape							,						output.shape		)
  
  
  
  
  
  
        @slow
        def        __lowerCamelCase			(							self		):
              for model_class_name in self.all_model_classes:
                    lowercase			:   Optional[int]					   =		model_class_name.from_pretrained('''facebook/mbart-large-cc25'''							,						from_pt=SCREAMING_SNAKE_CASE__		)
                    # FlaxMBartForSequenceClassification expects eos token in input_ids
                    lowercase			:   int					   =		np.ones((1, 1)		) * model.config.eos_token_id
                    lowercase			:   List[Any]					   =		model(SCREAMING_SNAKE_CASE__		)
                    self.assertIsNotNone(SCREAMING_SNAKE_CASE__		)
  
  
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class 				__SCREAMING_SNAKE_CASE   (			unittest.TestCase   ):
        A				:							str    							=     [
            ' UN Chief Says There Is No Military Solution in Syria',
        ]
        A				:							str    							=     [
            'Şeful ONU declară că nu există o soluţie militară în Siria',
        ]
        A				:							List[str]    							=     'facebook/mbart-large-en-ro'
  
  
        @cached_property
        def        __lowerCamelCase			(							self		):
              return AutoTokenizer.from_pretrained(self.model_name		)
  
  
        @cached_property
        def        __lowerCamelCase			(							self		):
              lowercase			:   Tuple					   =		FlaxMBartForConditionalGeneration.from_pretrained(self.model_name							,						from_pt=SCREAMING_SNAKE_CASE__		)
              return model
  
  
        def        __lowerCamelCase			(							self							,						**SCREAMING_SNAKE_CASE__		):
              lowercase			:   int					   =		self.translate_src_text(**SCREAMING_SNAKE_CASE__		)
              self.assertListEqual(self.expected_text							,						SCREAMING_SNAKE_CASE__		)
  
  
        def        __lowerCamelCase			(							self							,						**SCREAMING_SNAKE_CASE__		):
              lowercase			:   Optional[Any]					   =		self.tokenizer(self.src_text							,						**SCREAMING_SNAKE_CASE__							,						return_tensors='''np'''		)
              lowercase			:   Optional[int]					   =		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=SCREAMING_SNAKE_CASE__							,						num_beams=2							,						).sequences
              lowercase			:   List[str]					   =		self.tokenizer.batch_decode(SCREAMING_SNAKE_CASE__							,						skip_special_tokens=SCREAMING_SNAKE_CASE__		)
              return generated_words
  
  
  
  
  
  
        @slow
        def        __lowerCamelCase			(							self		):
              self._assert_generated_batch_equal_expected()
  
  
style_context: 
  
  
  import numpy as np
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->Dict:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Optional[Any]					   =		np.array(_UpperCamelCase     )
        lowercase			:   Dict					   =		np.array(_UpperCamelCase     )
  
        lowercase			:   List[Any]					   =		abs(predict - actual     )
        lowercase			:   int					   =		difference.mean()
  
        return score
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Tuple					   =		np.array(_UpperCamelCase     )
        lowercase			:   str					   =		np.array(_UpperCamelCase     )
  
        lowercase			:   List[str]					   =		predict - actual
        lowercase			:   Tuple					   =		np.square(_UpperCamelCase     )
  
        lowercase			:   Union[str, Any]					   =		square_diff.mean()
        return score
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->str:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Union[str, Any]					   =		np.array(_UpperCamelCase     )
        lowercase			:   Optional[Any]					   =		np.array(_UpperCamelCase     )
  
        lowercase			:   Tuple					   =		predict - actual
        lowercase			:   Dict					   =		np.square(_UpperCamelCase     )
        lowercase			:   Union[str, Any]					   =		square_diff.mean()
        lowercase			:   Union[str, Any]					   =		np.sqrt(_UpperCamelCase     )
        return score
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   List[str]					   =		np.array(_UpperCamelCase     )
        lowercase			:   List[Any]					   =		np.array(_UpperCamelCase     )
  
        lowercase			:   Dict					   =		np.log(predict + 1     )
        lowercase			:   List[str]					   =		np.log(actual + 1     )
  
        lowercase			:   int					   =		log_predict - log_actual
        lowercase			:   Tuple					   =		np.square(_UpperCamelCase     )
        lowercase			:   List[Any]					   =		square_diff.mean()
  
        lowercase			:   int					   =		np.sqrt(_UpperCamelCase     )
  
        return score
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[str]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   List[Any]					   =		np.array(_UpperCamelCase     )
        lowercase			:   Optional[int]					   =		np.array(_UpperCamelCase     )
  
        lowercase			:   Optional[int]					   =		predict - actual
        lowercase			:   Optional[int]					   =		np.sum(_UpperCamelCase     ) / len(_UpperCamelCase     )
        lowercase			:   Dict					   =		np.sum(_UpperCamelCase     ) / len(_UpperCamelCase     )
        # print(numerator, denumerator)
        lowercase			:   int					   =		float(_UpperCamelCase     ) / denumerator * 100
  
        return score
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        return np.mean(np.array(_UpperCamelCase     ) == np.array(_UpperCamelCase     )     )
  
  