





'''simple docstring'''




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
							__UpperCAmelCase         =   """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 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_=None	,	lowerCamelCase_=None	,	lowerCamelCase_=None	,	lowerCamelCase_=None	,	lowerCamelCase_=None	,	lowerCamelCase_=None	,	):


		"""simple docstring"""



		if attention_mask is None:
				SCREAMING_SNAKE_CASE				:							Any						=	np.where(input_ids != config.pad_token_id	,	1	,	0					)
		if decoder_attention_mask is None:
				SCREAMING_SNAKE_CASE				:							Dict						=	np.where(decoder_input_ids != config.pad_token_id	,	1	,	0					)
		if head_mask is None:
				SCREAMING_SNAKE_CASE				:							Optional[Any]						=	np.ones((config.encoder_layers, config.encoder_attention_heads)					)
		if decoder_head_mask is None:
				SCREAMING_SNAKE_CASE				:							Any						=	np.ones((config.decoder_layers, config.decoder_attention_heads)					)
		if cross_attn_head_mask is None:
				SCREAMING_SNAKE_CASE				:							Dict						=	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       UpperCamelCase__			(			unittest.TestCase	):




					"""simple docstring"""


					def __init__(		self							:	Tuple     ,		lowerCamelCase_							:	Union[str, Any]     ,		lowerCamelCase_							:	Dict=13     ,		lowerCamelCase_							:	int=7     ,		lowerCamelCase_							:	Optional[Any]=True     ,		lowerCamelCase_							:	Optional[Any]=False     ,		lowerCamelCase_							:	Optional[Any]=99     ,		lowerCamelCase_							:	Optional[Any]=16     ,		lowerCamelCase_							:	Union[str, Any]=2     ,		lowerCamelCase_							:	str=4     ,		lowerCamelCase_							:	Optional[int]=4     ,		lowerCamelCase_							:	str="gelu"     ,		lowerCamelCase_							:	Optional[int]=0.1     ,		lowerCamelCase_							:	Tuple=0.1     ,		lowerCamelCase_							:	str=32     ,		lowerCamelCase_							:	Optional[Any]=2     ,		lowerCamelCase_							:	Dict=1     ,		lowerCamelCase_							:	List[str]=0     ,		lowerCamelCase_							:	Optional[Any]=2     ,		lowerCamelCase_							:	Optional[Any]=0.02     ,		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[str]						=	parent
							SCREAMING_SNAKE_CASE				:							str						=	batch_size
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	seq_length
							SCREAMING_SNAKE_CASE				:							List[str]						=	is_training
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	use_labels
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	vocab_size
							SCREAMING_SNAKE_CASE				:							Tuple						=	hidden_size
							SCREAMING_SNAKE_CASE				:							int						=	num_hidden_layers
							SCREAMING_SNAKE_CASE				:							List[str]						=	num_attention_heads
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	intermediate_size
							SCREAMING_SNAKE_CASE				:							str						=	hidden_act
							SCREAMING_SNAKE_CASE				:							Tuple						=	hidden_dropout_prob
							SCREAMING_SNAKE_CASE				:							List[Any]						=	attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE				:							Any						=	max_position_embeddings
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	eos_token_id
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	pad_token_id
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	bos_token_id
							SCREAMING_SNAKE_CASE				:							List[str]						=	decoder_start_token_id
							SCREAMING_SNAKE_CASE				:							Dict						=	initializer_range


					def lowerCamelCase_	(		self							:	Any		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Dict						=	np.clip(ids_tensor([self.batch_size, self.seq_length - 1]     ,		self.vocab_size		)     ,		3     ,		self.vocab_size		)
							SCREAMING_SNAKE_CASE				:							Dict						=	np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)     ,		dtype=np.intaa		))     ,		-1		)

							SCREAMING_SNAKE_CASE				:							List[Any]						=	shift_tokens_right(lowerCamelCase_     ,		1		)

							SCREAMING_SNAKE_CASE				:							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_     ,		)
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	prepare_mbart_inputs_dict(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_		)
							return config, inputs_dict


					def lowerCamelCase_	(		self							:	List[str]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Tuple						=	self.prepare_config_and_inputs()
							return config, inputs_dict


					def lowerCamelCase_	(		self							:	Optional[int]     ,		lowerCamelCase_							:	Tuple     ,		lowerCamelCase_							:	Any     ,		lowerCamelCase_							:	str		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[Any]						=	20
							SCREAMING_SNAKE_CASE				:							str						=	model_class_name(lowerCamelCase_		)

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

							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Dict						=	(
							    inputs_dict["""decoder_input_ids"""],
							    inputs_dict["""decoder_attention_mask"""],
							)

							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	model.init_cache(decoder_input_ids.shape[0]     ,		lowerCamelCase_     ,		lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							int						=	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,		dtype="""i4"""		)

							SCREAMING_SNAKE_CASE				:							Optional[int]						=	jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]     ,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,		)
							SCREAMING_SNAKE_CASE				:							int						=	model.decode(
							    decoder_input_ids[:, :-1]     ,		lowerCamelCase_     ,		decoder_attention_mask=lowerCamelCase_     ,		past_key_values=lowerCamelCase_     ,		decoder_position_ids=lowerCamelCase_     ,		)

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

							SCREAMING_SNAKE_CASE				:							Any						=	model.decode(lowerCamelCase_     ,		lowerCamelCase_		)

							SCREAMING_SNAKE_CASE				:							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 lowerCamelCase_	(		self							:	Dict     ,		lowerCamelCase_							:	Optional[int]     ,		lowerCamelCase_							:	List[Any]     ,		lowerCamelCase_							:	Optional[int]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	20
							SCREAMING_SNAKE_CASE				:							str						=	model_class_name(lowerCamelCase_		)

							SCREAMING_SNAKE_CASE				:							Dict						=	model.encode(inputs_dict["""input_ids"""]		)

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

							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	jnp.concatenate(
							    [
							        decoder_attention_mask,
							        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])		),
							    ]     ,		axis=-1     ,		)

							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	model.init_cache(decoder_input_ids.shape[0]     ,		lowerCamelCase_     ,		lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							str						=	jnp.broadcast_to(
							    jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]     ,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,		)

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

							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	model.decode(lowerCamelCase_     ,		lowerCamelCase_     ,		decoder_attention_mask=lowerCamelCase_		)

							SCREAMING_SNAKE_CASE				:							int						=	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       UpperCamelCase__			(			unittest.TestCase	):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__						=					99


					def lowerCamelCase_	(		self							:	int		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Union[str, 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     ,		)

							SCREAMING_SNAKE_CASE				:							Any						=	input_ids.shape[0]
							SCREAMING_SNAKE_CASE				:							Union[str, 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							:	Optional[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							int						=	self._get_config_and_data()
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	FlaxMBartForSequenceClassification(lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	model(input_ids=lowerCamelCase_     ,		decoder_input_ids=lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							str						=	(batch_size, config.num_labels)
							self.assertEqual(outputs["""logits"""].shape     ,		lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	Optional[int]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	self._get_config_and_data()
							SCREAMING_SNAKE_CASE				:							str						=	FlaxMBartForQuestionAnswering(lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							Any						=	model(input_ids=lowerCamelCase_		)

							self.assertEqual(outputs["""start_logits"""].shape     ,		input_ids.shape		)
							self.assertEqual(outputs["""end_logits"""].shape     ,		input_ids.shape		)


					def lowerCamelCase_	(		self							:	Tuple		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							List[str]						=	self._get_config_and_data()
							SCREAMING_SNAKE_CASE				:							Any						=	FlaxMBartForConditionalGeneration(lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							Tuple						=	lm_model(input_ids=lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							int						=	(batch_size, input_ids.shape[1], config.vocab_size)
							self.assertEqual(outputs["""logits"""].shape     ,		lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	int		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							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     ,		)
							SCREAMING_SNAKE_CASE				:							Dict						=	FlaxMBartForConditionalGeneration(lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]     ,		dtype=np.intaa		)
							SCREAMING_SNAKE_CASE				:							Tuple						=	np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]     ,		dtype=np.intaa		)
							SCREAMING_SNAKE_CASE				:							Tuple						=	lm_model(input_ids=lowerCamelCase_     ,		decoder_input_ids=lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	(*summary.shape, config.vocab_size)
							self.assertEqual(outputs["""logits"""].shape     ,		lowerCamelCase_		)



					def lowerCamelCase_	(		self							:	List[str]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[str]						=	np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]     ,		dtype=np.intaa		)
							SCREAMING_SNAKE_CASE				:							Any						=	shift_tokens_right(lowerCamelCase_     ,		1		)
							SCREAMING_SNAKE_CASE				:							List[str]						=	np.equal(lowerCamelCase_     ,		1		).astype(np.floataa		).sum()
							SCREAMING_SNAKE_CASE				:							List[Any]						=	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       UpperCamelCase__			(			lowercase_		,			unittest.TestCase		,			lowercase_	):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__						=					True
					SCREAMING_SNAKE_CASE__						=					(
					    (
					        FlaxMBartModel,
					        FlaxMBartForConditionalGeneration,
					        FlaxMBartForSequenceClassification,
					        FlaxMBartForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)
					SCREAMING_SNAKE_CASE__						=					(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()


					def lowerCamelCase_	(		self							:	Any		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Any						=	FlaxMBartModelTester(self		)


					def lowerCamelCase_	(		self							:	Optional[Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Optional[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 lowerCamelCase_	(		self							:	Union[str, Any]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							int						=	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 lowerCamelCase_	(		self							:	Optional[int]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							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__		):
											SCREAMING_SNAKE_CASE				:							Optional[int]						=	self._prepare_for_class(lowerCamelCase_     ,		lowerCamelCase_		)
											SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	model_class(lowerCamelCase_		)

											@jax.jit
											def encode_jitted(lowerCamelCase_							:	Optional[int]     ,		lowerCamelCase_							:	Tuple=None     ,		**lowerCamelCase_							:	Tuple		):
													return model.encode(input_ids=lowerCamelCase_     ,		attention_mask=lowerCamelCase_		)

											with self.subTest("""JIT Enabled"""		):
													SCREAMING_SNAKE_CASE				:							int						=	encode_jitted(**lowerCamelCase_		).to_tuple()

											with self.subTest("""JIT Disabled"""		):
													with jax.disable_jit():
															SCREAMING_SNAKE_CASE				:							List[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 lowerCamelCase_	(		self							:	List[str]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							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__		):
											SCREAMING_SNAKE_CASE				:							Tuple						=	model_class(lowerCamelCase_		)
											SCREAMING_SNAKE_CASE				:							Dict						=	model.encode(inputs_dict["""input_ids"""]     ,		inputs_dict["""attention_mask"""]		)

											SCREAMING_SNAKE_CASE				:							Optional[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(lowerCamelCase_							:	List[str]     ,		lowerCamelCase_							:	List[Any]     ,		lowerCamelCase_							:	Any		):
													return model.decode(
													    decoder_input_ids=lowerCamelCase_     ,		decoder_attention_mask=lowerCamelCase_     ,		encoder_outputs=lowerCamelCase_     ,		)

											with self.subTest("""JIT Enabled"""		):
													SCREAMING_SNAKE_CASE				:							List[Any]						=	decode_jitted(**lowerCamelCase_		).to_tuple()

											with self.subTest("""JIT Disabled"""		):
													with jax.disable_jit():
															SCREAMING_SNAKE_CASE				:							Tuple						=	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 lowerCamelCase_	(		self							:	Optional[int]		):

							'''simple docstring'''



							for model_class_name in self.all_model_classes:
									SCREAMING_SNAKE_CASE				:							int						=	model_class_name.from_pretrained("""facebook/mbart-large-cc25"""     ,		from_pt=lowerCamelCase_		)
									# FlaxMBartForSequenceClassification expects eos token in input_ids
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	np.ones((1, 1)		) * model.config.eos_token_id
									SCREAMING_SNAKE_CASE				:							str						=	model(lowerCamelCase_		)
									self.assertIsNotNone(lowerCamelCase_		)







@require_flax
@require_sentencepiece
@require_tokenizers
class       UpperCamelCase__			(			unittest.TestCase	):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__						=					[
					    ''' UN Chief Says There Is No Military Solution in Syria''',
					]
					SCREAMING_SNAKE_CASE__						=					[
					    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
					]
					SCREAMING_SNAKE_CASE__						=					'''facebook/mbart-large-en-ro'''


					@cached_property
					def lowerCamelCase_	(		self							:	Tuple		):

							'''simple docstring'''



							return AutoTokenizer.from_pretrained(self.model_name		)


					@cached_property
					def lowerCamelCase_	(		self							:	Tuple		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							int						=	FlaxMBartForConditionalGeneration.from_pretrained(self.model_name     ,		from_pt=lowerCamelCase_		)
							return model


					def lowerCamelCase_	(		self							:	Tuple     ,		**lowerCamelCase_							:	List[str]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	self.translate_src_text(**lowerCamelCase_		)
							self.assertListEqual(self.expected_text     ,		lowerCamelCase_		)


					def lowerCamelCase_	(		self							:	Optional[int]     ,		**lowerCamelCase_							:	int		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							int						=	self.tokenizer(self.src_text     ,		**lowerCamelCase_     ,		return_tensors="""np"""		)
							SCREAMING_SNAKE_CASE				:							List[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
							SCREAMING_SNAKE_CASE				:							List[str]						=	self.tokenizer.batch_decode(lowerCamelCase_     ,		skip_special_tokens=lowerCamelCase_		)
							return generated_words



					@slow
					def lowerCamelCase_	(		self							:	Any		):

							'''simple docstring'''



							self._assert_generated_batch_equal_expected()










'''simple docstring'''




from __future__ import annotations

from collections import Counter



def 						__A						(       lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							int						=	[True] * n
		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	False
		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	False
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	True

		for i in range(3	,	int(n**0.5 + 1					)	,	2					):
				SCREAMING_SNAKE_CASE				:							List[Any]						=	i * 2
				while index < n:
						SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	False
						SCREAMING_SNAKE_CASE				:							Optional[Any]						=	index + i

		SCREAMING_SNAKE_CASE				:							Dict						=	[2]

		for i in range(3	,	lowerCamelCase_	,	2					):
				if is_prime[i]:
						primes.append(lowerCamelCase_					)

		return primes



def 						__A						(       lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							List[str]						=	str(lowerCamelCase_					)
		SCREAMING_SNAKE_CASE				:							List[str]						=	[]
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	["""0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9"""]

		for duplicate in Counter(lowerCamelCase_					) - Counter(set(lowerCamelCase_					)					):
				SCREAMING_SNAKE_CASE				:							Tuple						=	[int(number_str.replace(lowerCamelCase_	,	lowerCamelCase_					)					) for digit in digits]
				replacements.append(lowerCamelCase_					)

		return replacements



def 						__A						(       lowerCamelCase_ = 8					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Tuple						=	set()

		# Filter primes with less than 3 replaceable digits
		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	{
		    x for x in set(prime_sieve(1_00_00_00					)					) if len(str(lowerCamelCase_					)					) - len(set(str(lowerCamelCase_					)					)					) >= 3
		}

		for prime in primes:
				if prime in numbers_checked:
						continue

				SCREAMING_SNAKE_CASE				:							int						=	digit_replacements(lowerCamelCase_					)

				for family in replacements:
						numbers_checked.update(lowerCamelCase_					)
						SCREAMING_SNAKE_CASE				:							Dict						=	primes.intersection(lowerCamelCase_					)

						if len(lowerCamelCase_					) != family_length:
								continue

						return min(lowerCamelCase_					)

		return -1


if __name__ == "__main__":
							print(solution())



