






import matplotlib.pyplot as plt
import numpy as np

def 					UpperCamelCase		( _A,					_A,					_A				):






						"""simple docstring"""







						__magic_name__    :     Union[str, Any]     		=      len(_A				)  # Number of training samples
						__magic_name__    :     Dict     		=      np.eye(_A				)  # Initialize weights as identity matrix
						for j in range(_A				):
												__magic_name__    :     Optional[Any]     		=      point - x_train[j]
												__magic_name__    :     Dict     		=      np.exp(diff @ diff.T / (-2.0 * tau**2)				)

						return weights

def 					UpperCamelCase		( _A,					_A,					_A,					_A				):






						"""simple docstring"""







						__magic_name__    :     str     		=      weight_matrix(_A,					_A,					_A				)
						__magic_name__    :     str     		=      np.linalg.inv(x_train.T @ weight_mat @ x_train				) @ (
						    x_train.T @ weight_mat @ y_train.T
						)

						return weight

def 					UpperCamelCase		( _A,					_A,					_A				):






						"""simple docstring"""







						__magic_name__    :     List[Any]     		=      np.zeros(len(_A				)				)  # Initialize array of predictions
						for i, item in enumerate(_A				):
												__magic_name__    :     Optional[Any]     		=      item @ local_weight(_A,					_A,					_A,					_A				)

						return y_pred

def 					UpperCamelCase		( _A,					_A,					_A				):






						"""simple docstring"""







						import seaborn as sns

						__magic_name__    :     Any     		=      sns.load_dataset(_A				)
						__magic_name__    :     int     		=      np.array(data[x_name]				)
						__magic_name__    :     str     		=      np.array(data[y_name]				)

						__magic_name__    :     str     		=      np.ones(len(_A				)				)

						# pairing elements of one and x_data
						__magic_name__    :     int     		=      np.column_stack((one, x_data)				)

						return x_train, x_data, y_data

def 					UpperCamelCase		( _A,					_A,					_A,					_A,					_A,					_A,					):






						"""simple docstring"""







						__magic_name__    :     Union[str, Any]     		=      np.sort(_A,					axis=0				)
						plt.scatter(_A,					_A,					color="""blue"""				)
						plt.plot(
						    x_train_sorted[:, 1],					preds[x_train[:, 1].argsort(0				)],					color="""yellow""",					linewidth=5,					)
						plt.title("""Local Weighted Regression"""				)
						plt.xlabel(_A				)
						plt.ylabel(_A				)
						plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		# Demo with a dataset from the seaborn module
		__magic_name__:  List[Any]						=			load_data("tips", "total_bill", "tip")
		__magic_name__:  List[Any]						=			local_weight_regression(training_data_x, tip, 5)
		plot_preds(training_data_x, predictions, total_bill, tip, "total_bill", "tip")








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
		__magic_name__:  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 					UpperCamelCase		( _A,					_A,					_A=None,					_A=None,					_A=None,					_A=None,					_A=None,					_A=None,					):






						"""simple docstring"""







						if attention_mask is None:
												__magic_name__    :     List[Any]     		=      np.where(input_ids != config.pad_token_id,					1,					0				)
						if decoder_attention_mask is None:
												__magic_name__    :     int     		=      np.where(decoder_input_ids != config.pad_token_id,					1,					0				)
						if head_mask is None:
												__magic_name__    :     Tuple     		=      np.ones((config.encoder_layers, config.encoder_attention_heads)				)
						if decoder_head_mask is None:
												__magic_name__    :     Union[str, Any]     		=      np.ones((config.decoder_layers, config.decoder_attention_heads)				)
						if cross_attn_head_mask is None:
												__magic_name__    :     str     		=      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  snake_case__      (							unittest.TestCase   ):
	def __init__(       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__=13    ,     lowerCAmelCase__=7    ,     lowerCAmelCase__=True    ,     lowerCAmelCase__=False    ,     lowerCAmelCase__=99    ,     lowerCAmelCase__=16    ,     lowerCAmelCase__=2    ,     lowerCAmelCase__=4    ,     lowerCAmelCase__=4    ,     lowerCAmelCase__="gelu"    ,     lowerCAmelCase__=0.1    ,     lowerCAmelCase__=0.1    ,     lowerCAmelCase__=32    ,     lowerCAmelCase__=2    ,     lowerCAmelCase__=1    ,     lowerCAmelCase__=0    ,     lowerCAmelCase__=2    ,     lowerCAmelCase__=0.0_2    ,     )							->   Any:
							__magic_name__    :     Dict     		=      parent
							__magic_name__    :     int     		=      batch_size
							__magic_name__    :     Union[str, Any]     		=      seq_length
							__magic_name__    :     int     		=      is_training
							__magic_name__    :     Optional[Any]     		=      use_labels
							__magic_name__    :     Tuple     		=      vocab_size
							__magic_name__    :     Union[str, Any]     		=      hidden_size
							__magic_name__    :     Tuple     		=      num_hidden_layers
							__magic_name__    :     Optional[Any]     		=      num_attention_heads
							__magic_name__    :     int     		=      intermediate_size
							__magic_name__    :     Optional[Any]     		=      hidden_act
							__magic_name__    :     List[str]     		=      hidden_dropout_prob
							__magic_name__    :     Union[str, Any]     		=      attention_probs_dropout_prob
							__magic_name__    :     List[Any]     		=      max_position_embeddings
							__magic_name__    :     Tuple     		=      eos_token_id
							__magic_name__    :     str     		=      pad_token_id
							__magic_name__    :     str     		=      bos_token_id
							__magic_name__    :     Optional[int]     		=      decoder_start_token_id
							__magic_name__    :     Tuple     		=      initializer_range
	def     __magic_name__       (       self				)							->   Union[str, Any]:
							__magic_name__    :     Dict     		=      np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,     self.vocab_size				)    ,     3    ,     self.vocab_size				)
							__magic_name__    :     List[Any]     		=      np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,     dtype=np.intaa				))    ,     -1				)

							__magic_name__    :     List[str]     		=      shift_tokens_right(lowerCAmelCase__    ,     1				)

							__magic_name__    :     str     		=      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=lowerCAmelCase__    ,     )
							__magic_name__    :     Dict     		=      prepare_mbart_inputs_dict(lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__				)
							return config, inputs_dict
	def     __magic_name__       (       self				)							->   List[str]:
							__magic_name__		,__magic_name__    :     List[str]     		=      self.prepare_config_and_inputs()
							return config, inputs_dict
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__				)							->   Optional[Any]:
							__magic_name__    :     Any     		=      20
							__magic_name__    :     Any     		=      model_class_name(lowerCAmelCase__				)

							__magic_name__    :     Union[str, Any]     		=      model.encode(inputs_dict["""input_ids"""]				)

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

							__magic_name__    :     str     		=      model.init_cache(decoder_input_ids.shape[0]    ,     lowerCAmelCase__    ,     lowerCAmelCase__				)
							__magic_name__    :     Optional[int]     		=      jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,     dtype="""i4"""				)

							__magic_name__    :     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)    ,     )
							__magic_name__    :     List[Any]     		=      model.decode(
							    decoder_input_ids[:, :-1]    ,     lowerCAmelCase__    ,     decoder_attention_mask=lowerCAmelCase__    ,     past_key_values=lowerCAmelCase__    ,     decoder_position_ids=lowerCAmelCase__    ,     )

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

							__magic_name__    :     Optional[Any]     		=      model.decode(lowerCAmelCase__    ,     lowerCAmelCase__				)

							__magic_name__    :     List[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     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__				)							->   str:
							__magic_name__    :     int     		=      20
							__magic_name__    :     List[str]     		=      model_class_name(lowerCAmelCase__				)

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

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

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

							__magic_name__    :     Tuple     		=      model.init_cache(decoder_input_ids.shape[0]    ,     lowerCAmelCase__    ,     lowerCAmelCase__				)
							__magic_name__    :     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)    ,     )

							__magic_name__    :     Any     		=      model.decode(
							    decoder_input_ids[:, :-1]    ,     lowerCAmelCase__    ,     decoder_attention_mask=lowerCAmelCase__    ,     past_key_values=lowerCAmelCase__    ,     decoder_position_ids=lowerCAmelCase__    ,     )
							__magic_name__    :     List[str]     		=      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,     dtype="""i4"""				)
							__magic_name__    :     Dict     		=      model.decode(
							    decoder_input_ids[:, -1:]    ,     lowerCAmelCase__    ,     past_key_values=outputs_cache.past_key_values    ,     decoder_attention_mask=lowerCAmelCase__    ,     decoder_position_ids=lowerCAmelCase__    ,     )

							__magic_name__    :     Dict     		=      model.decode(lowerCAmelCase__    ,     lowerCAmelCase__    ,     decoder_attention_mask=lowerCAmelCase__				)

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





@require_flax
class  snake_case__      (							unittest.TestCase   ):
	lowercase__		:  List[Any]							   =					99
	def     __magic_name__       (       self				)							->   List[str]:
							__magic_name__    :     Any     		=      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    ,     )

							__magic_name__    :     int     		=      input_ids.shape[0]
							__magic_name__    :     Dict     		=      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     __magic_name__       (       self				)							->   List[Any]:
							__magic_name__		,__magic_name__		,__magic_name__    :     Any     		=      self._get_config_and_data()
							__magic_name__    :     Union[str, Any]     		=      FlaxMBartForSequenceClassification(lowerCAmelCase__				)
							__magic_name__    :     Union[str, Any]     		=      model(input_ids=lowerCAmelCase__    ,     decoder_input_ids=lowerCAmelCase__				)
							__magic_name__    :     Any     		=      (batch_size, config.num_labels)
							self.assertEqual(outputs["""logits"""].shape    ,     lowerCAmelCase__				)
	def     __magic_name__       (       self				)							->   Optional[int]:
							__magic_name__		,__magic_name__		,__magic_name__    :     Tuple     		=      self._get_config_and_data()
							__magic_name__    :     Optional[int]     		=      FlaxMBartForQuestionAnswering(lowerCAmelCase__				)
							__magic_name__    :     str     		=      model(input_ids=lowerCAmelCase__				)

							self.assertEqual(outputs["""start_logits"""].shape    ,     input_ids.shape				)
							self.assertEqual(outputs["""end_logits"""].shape    ,     input_ids.shape				)
	def     __magic_name__       (       self				)							->   int:
							__magic_name__		,__magic_name__		,__magic_name__    :     str     		=      self._get_config_and_data()
							__magic_name__    :     Union[str, Any]     		=      FlaxMBartForConditionalGeneration(lowerCAmelCase__				)
							__magic_name__    :     int     		=      lm_model(input_ids=lowerCAmelCase__				)
							__magic_name__    :     List[str]     		=      (batch_size, input_ids.shape[1], config.vocab_size)
							self.assertEqual(outputs["""logits"""].shape    ,     lowerCAmelCase__				)
	def     __magic_name__       (       self				)							->   Tuple:
							__magic_name__    :     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    ,     )
							__magic_name__    :     List[str]     		=      FlaxMBartForConditionalGeneration(lowerCAmelCase__				)
							__magic_name__    :     Dict     		=      np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    ,     dtype=np.intaa				)
							__magic_name__    :     Dict     		=      np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    ,     dtype=np.intaa				)
							__magic_name__    :     Union[str, Any]     		=      lm_model(input_ids=lowerCAmelCase__    ,     decoder_input_ids=lowerCAmelCase__				)
							__magic_name__    :     str     		=      (*summary.shape, config.vocab_size)
							self.assertEqual(outputs["""logits"""].shape    ,     lowerCAmelCase__				)







	def     __magic_name__       (       self				)							->   Tuple:
							__magic_name__    :     int     		=      np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]    ,     dtype=np.intaa				)
							__magic_name__    :     Tuple     		=      shift_tokens_right(lowerCAmelCase__    ,     1				)
							__magic_name__    :     Any     		=      np.equal(lowerCAmelCase__    ,     1				).astype(np.floataa				).sum()
							__magic_name__    :     Tuple     		=      np.equal(lowerCAmelCase__    ,     1				).astype(np.floataa				).sum()
							self.assertEqual(shifted.shape    ,     input_ids.shape				)
							self.assertEqual(lowerCAmelCase__    ,     n_pad_before - 1				)
							self.assertTrue(np.equal(shifted[:, 0]    ,     2				).all()				)





@require_flax
class  snake_case__      (							_lowerCAmelCase		,						unittest.TestCase		,						_lowerCAmelCase   ):
	lowercase__		:  Dict							   =					True
	lowercase__		:  Any							   =					(
	    (
	        FlaxMBartModel,
	        FlaxMBartForConditionalGeneration,
	        FlaxMBartForSequenceClassification,
	        FlaxMBartForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)
	lowercase__		:  str							   =					(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()
	def     __magic_name__       (       self				)							->   List[str]:
							__magic_name__    :     Any     		=      FlaxMBartModelTester(self				)
	def     __magic_name__       (       self				)							->   List[str]:
							__magic_name__		,__magic_name__    :     Union[str, Any]     		=      self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward(lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__				)
	def     __magic_name__       (       self				)							->   int:
							__magic_name__		,__magic_name__    :     List[Any]     		=      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(lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__				)
	def     __magic_name__       (       self				)							->   Union[str, Any]:
							__magic_name__		,__magic_name__    :     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__				):
																			__magic_name__    :     Tuple     		=      self._prepare_for_class(lowerCAmelCase__    ,     lowerCAmelCase__				)
																			__magic_name__    :     Optional[int]     		=      model_class(lowerCAmelCase__				)

																			@jax.jit
																			def encode_jitted(lowerCAmelCase__    ,     lowerCAmelCase__=None    ,     **lowerCAmelCase__				):
																									return model.encode(input_ids=lowerCAmelCase__    ,     attention_mask=lowerCAmelCase__				)

																			with self.subTest("""JIT Enabled"""				):
																									__magic_name__    :     Any     		=      encode_jitted(**lowerCAmelCase__				).to_tuple()

																			with self.subTest("""JIT Disabled"""				):
																									with jax.disable_jit():
																															__magic_name__    :     Optional[Any]     		=      encode_jitted(**lowerCAmelCase__				).to_tuple()

																			self.assertEqual(len(lowerCAmelCase__				)    ,     len(lowerCAmelCase__				)				)
																			for jitted_output, output in zip(lowerCAmelCase__    ,     lowerCAmelCase__				):
																									self.assertEqual(jitted_output.shape    ,     output.shape				)
	def     __magic_name__       (       self				)							->   List[str]:
							__magic_name__		,__magic_name__    :     Dict     		=      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__				):
																			__magic_name__    :     Optional[int]     		=      model_class(lowerCAmelCase__				)
																			__magic_name__    :     Union[str, Any]     		=      model.encode(inputs_dict["""input_ids"""]    ,     inputs_dict["""attention_mask"""]				)

																			__magic_name__    :     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(lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__				):
																									return model.decode(
																									    decoder_input_ids=lowerCAmelCase__    ,     decoder_attention_mask=lowerCAmelCase__    ,     encoder_outputs=lowerCAmelCase__    ,     )

																			with self.subTest("""JIT Enabled"""				):
																									__magic_name__    :     Optional[int]     		=      decode_jitted(**lowerCAmelCase__				).to_tuple()

																			with self.subTest("""JIT Disabled"""				):
																									with jax.disable_jit():
																															__magic_name__    :     Any     		=      decode_jitted(**lowerCAmelCase__				).to_tuple()

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







	@slow
	def     __magic_name__       (       self				)							->   List[Any]:
							for model_class_name in self.all_model_classes:
													__magic_name__    :     Any     		=      model_class_name.from_pretrained("""facebook/mbart-large-cc25"""    ,     from_pt=lowerCAmelCase__				)
													# FlaxMBartForSequenceClassification expects eos token in input_ids
													__magic_name__    :     List[Any]     		=      np.ones((1, 1)				) * model.config.eos_token_id
													__magic_name__    :     str     		=      model(lowerCAmelCase__				)
													self.assertIsNotNone(lowerCAmelCase__				)





@require_flax
@require_sentencepiece
@require_tokenizers
class  snake_case__      (							unittest.TestCase   ):
	lowercase__		:  Any							   =					[
	    ''' UN Chief Says There Is No Military Solution in Syria''',
	]
	lowercase__		:  Optional[Any]							   =					[
	    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
	]
	lowercase__		:  Optional[Any]							   =					'''facebook/mbart-large-en-ro'''
	@cached_property
	def     __magic_name__       (       self				)							->   Tuple:
							return AutoTokenizer.from_pretrained(self.model_name				)
	@cached_property
	def     __magic_name__       (       self				)							->   int:
							__magic_name__    :     Tuple     		=      FlaxMBartForConditionalGeneration.from_pretrained(self.model_name    ,     from_pt=lowerCAmelCase__				)
							return model
	def     __magic_name__       (       self    ,     **lowerCAmelCase__				)							->   Tuple:
							__magic_name__    :     Optional[Any]     		=      self.translate_src_text(**lowerCAmelCase__				)
							self.assertListEqual(self.expected_text    ,     lowerCAmelCase__				)
	def     __magic_name__       (       self    ,     **lowerCAmelCase__				)							->   Dict:
							__magic_name__    :     Dict     		=      self.tokenizer(self.src_text    ,     **lowerCAmelCase__    ,     return_tensors="""np"""				)
							__magic_name__    :     str     		=      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=lowerCAmelCase__    ,     num_beams=2    ,     ).sequences
							__magic_name__    :     str     		=      self.tokenizer.batch_decode(lowerCAmelCase__    ,     skip_special_tokens=lowerCAmelCase__				)
							return generated_words







	@slow
	def     __magic_name__       (       self				)							->   List[str]:
							self._assert_generated_batch_equal_expected()
