code: 
  
  
  '''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
      A__	:      Dict				=						'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__      (			UpperCAmelCase_					:       Optional[int]   ,					UpperCAmelCase_					:       Union[str, Any]   ,					UpperCAmelCase_					:       Optional[Any]=None   ,					UpperCAmelCase_					:       Any=None   ,					UpperCAmelCase_					:       Optional[int]=None   ,					UpperCAmelCase_					:       Optional[Any]=None   ,					UpperCAmelCase_					:       List[Any]=None   ,					UpperCAmelCase_					:       str=None   ,					)  ->	Union[str, Any]:
  
  
  
       if attention_mask is None:
            __lowerCamelCase     :			Dict              =	np.where(input_ids != config.pad_token_id   ,					1   ,					0							)
       if decoder_attention_mask is None:
            __lowerCamelCase     :			str              =	np.where(decoder_input_ids != config.pad_token_id   ,					1   ,					0							)
       if head_mask is None:
            __lowerCamelCase     :			Dict              =	np.ones((config.encoder_layers, config.encoder_attention_heads)							)
       if decoder_head_mask is None:
            __lowerCamelCase     :			Union[str, Any]              =	np.ones((config.decoder_layers, config.decoder_attention_heads)							)
       if cross_attn_head_mask is None:
            __lowerCamelCase     :			Optional[Any]              =	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    ,  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_=0.0_2    ,  )       ->  Optional[Any]:
           __lowerCamelCase     :			int              =	parent
           __lowerCamelCase     :			Tuple              =	batch_size
           __lowerCamelCase     :			int              =	seq_length
           __lowerCamelCase     :			Optional[int]              =	is_training
           __lowerCamelCase     :			Optional[Any]              =	use_labels
           __lowerCamelCase     :			List[str]              =	vocab_size
           __lowerCamelCase     :			Any              =	hidden_size
           __lowerCamelCase     :			Any              =	num_hidden_layers
           __lowerCamelCase     :			Union[str, Any]              =	num_attention_heads
           __lowerCamelCase     :			str              =	intermediate_size
           __lowerCamelCase     :			str              =	hidden_act
           __lowerCamelCase     :			Optional[Any]              =	hidden_dropout_prob
           __lowerCamelCase     :			int              =	attention_probs_dropout_prob
           __lowerCamelCase     :			List[str]              =	max_position_embeddings
           __lowerCamelCase     :			Optional[Any]              =	eos_token_id
           __lowerCamelCase     :			Dict              =	pad_token_id
           __lowerCamelCase     :			List[Any]              =	bos_token_id
           __lowerCamelCase     :			Union[str, Any]              =	decoder_start_token_id
           __lowerCamelCase     :			str              =	initializer_range
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[int]:
           __lowerCamelCase     :			Union[str, Any]              =	np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,  self.vocab_size			)    ,  3    ,  self.vocab_size			)
           __lowerCamelCase     :			List[str]              =	np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,  dtype=np.intaa			))    ,  -1			)
  
           __lowerCamelCase     :			Optional[int]              =	shift_tokens_right(SCREAMING_SNAKE_CASE_    ,  1			)
  
           __lowerCamelCase     :			Dict              =	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_    ,  )
           __lowerCamelCase     :			Dict              =	prepare_mbart_inputs_dict(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
           return config, inputs_dict
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Any:
           __lowerCamelCase       , __lowerCamelCase     :			Tuple              =	self.prepare_config_and_inputs()
           return config, inputs_dict
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  List[str]:
           __lowerCamelCase     :			int              =	20
           __lowerCamelCase     :			Dict              =	model_class_name(SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			int              =	model.encode(inputs_dict['input_ids']			)
  
           __lowerCamelCase       , __lowerCamelCase     :			Union[str, Any]              =	(
               inputs_dict['decoder_input_ids'],
               inputs_dict['decoder_attention_mask'],
           )
  
           __lowerCamelCase     :			Union[str, Any]              =	model.init_cache(decoder_input_ids.shape[0]    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			int              =	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,  dtype='i4'			)
  
           __lowerCamelCase     :			Union[str, Any]              =	jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]    ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,  )
           __lowerCamelCase     :			Tuple              =	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_    ,  )
  
           __lowerCamelCase     :			Optional[Any]              =	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,  dtype='i4'			)
           __lowerCamelCase     :			List[Any]              =	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_    ,  )
  
           __lowerCamelCase     :			Tuple              =	model.decode(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			str              =	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    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)       ->  List[str]:
           __lowerCamelCase     :			Any              =	20
           __lowerCamelCase     :			Any              =	model_class_name(SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			str              =	model.encode(inputs_dict['input_ids']			)
  
           __lowerCamelCase       , __lowerCamelCase     :			Tuple              =	(
               inputs_dict['decoder_input_ids'],
               inputs_dict['decoder_attention_mask'],
           )
  
           __lowerCamelCase     :			Any              =	jnp.concatenate(
               [
                   decoder_attention_mask,
                   jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])			),
               ]    ,  axis=-1    ,  )
  
           __lowerCamelCase     :			str              =	model.init_cache(decoder_input_ids.shape[0]    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			int              =	jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]    ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,  )
  
           __lowerCamelCase     :			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_    ,  )
           __lowerCamelCase     :			Optional[int]              =	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,  dtype='i4'			)
           __lowerCamelCase     :			Tuple              =	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_    ,  )
  
           __lowerCamelCase     :			Optional[Any]              =	model.decode(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  decoder_attention_mask=SCREAMING_SNAKE_CASE_			)
  
           __lowerCamelCase     :			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      UpperCAmelCase_						(unittest.TestCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      List[str]							  =    9_9
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Tuple:
           __lowerCamelCase     :			Optional[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    ,  )
  
           __lowerCamelCase     :			List[str]              =	input_ids.shape[0]
           __lowerCamelCase     :			int              =	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			)       ->  List[str]:
           __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase     :			Optional[Any]              =	self._get_config_and_data()
           __lowerCamelCase     :			Dict              =	FlaxMBartForSequenceClassification(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Tuple              =	model(input_ids=SCREAMING_SNAKE_CASE_    ,  decoder_input_ids=SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Tuple              =	(batch_size, config.num_labels)
           self.assertEqual(outputs['logits'].shape    ,  SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[int]:
           __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase     :			int              =	self._get_config_and_data()
           __lowerCamelCase     :			str              =	FlaxMBartForQuestionAnswering(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			str              =	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    lowercase_       (							self			)       ->  Dict:
           __lowerCamelCase       , __lowerCamelCase       , __lowerCamelCase     :			str              =	self._get_config_and_data()
           __lowerCamelCase     :			str              =	FlaxMBartForConditionalGeneration(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Tuple              =	lm_model(input_ids=SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			int              =	(batch_size, input_ids.shape[1], config.vocab_size)
           self.assertEqual(outputs['logits'].shape    ,  SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           __lowerCamelCase     :			Union[str, Any]              =	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    ,  )
           __lowerCamelCase     :			List[str]              =	FlaxMBartForConditionalGeneration(SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			Union[str, Any]              =	np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    ,  dtype=np.intaa			)
           __lowerCamelCase     :			int              =	np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    ,  dtype=np.intaa			)
           __lowerCamelCase     :			Union[str, Any]              =	lm_model(input_ids=SCREAMING_SNAKE_CASE_    ,  decoder_input_ids=SCREAMING_SNAKE_CASE_			)
           __lowerCamelCase     :			int              =	(*summary.shape, config.vocab_size)
           self.assertEqual(outputs['logits'].shape    ,  SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[str]:
           __lowerCamelCase     :			Dict              =	np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]    ,  dtype=np.intaa			)
           __lowerCamelCase     :			Optional[int]              =	shift_tokens_right(SCREAMING_SNAKE_CASE_    ,  1			)
           __lowerCamelCase     :			Dict              =	np.equal(SCREAMING_SNAKE_CASE_    ,  1			).astype(np.floataa			).sum()
           __lowerCamelCase     :			Union[str, Any]              =	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      UpperCAmelCase_						(UpperCAmelCase_      ,	unittest.TestCase      ,	UpperCAmelCase_						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Union[str, Any]							  =    True
      lowerCamelCase						:      Optional[int]							  =    (
          (
              FlaxMBartModel,
              FlaxMBartForConditionalGeneration,
              FlaxMBartForSequenceClassification,
              FlaxMBartForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )
      lowerCamelCase						:      str							  =    (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Optional[int]:
           __lowerCamelCase     :			int              =	FlaxMBartModelTester(self			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  Dict:
           __lowerCamelCase       , __lowerCamelCase     :			Optional[Any]              =	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    lowercase_       (							self			)       ->  Optional[Any]:
           __lowerCamelCase       , __lowerCamelCase     :			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(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self			)       ->  List[Any]:
           __lowerCamelCase       , __lowerCamelCase     :			Dict              =	self.model_tester.prepare_config_and_inputs_for_common()
  
           for model_class in self.all_model_classes:
                with self.subTest(model_class.__name__			):
                     __lowerCamelCase     :			Optional[Any]              =	self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)
                     __lowerCamelCase     :			Dict              =	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'			):
                          __lowerCamelCase     :			str              =	encode_jitted(**SCREAMING_SNAKE_CASE_			).to_tuple()
  
                     with self.subTest('JIT Disabled'			):
                          with jax.disable_jit():
                               __lowerCamelCase     :			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    lowercase_       (							self			)       ->  int:
           __lowerCamelCase       , __lowerCamelCase     :			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__			):
                     __lowerCamelCase     :			Tuple              =	model_class(SCREAMING_SNAKE_CASE_			)
                     __lowerCamelCase     :			List[Any]              =	model.encode(inputs_dict['input_ids']    ,  inputs_dict['attention_mask']			)
  
                     __lowerCamelCase     :			List[Any]              =	{
                         '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'			):
                          __lowerCamelCase     :			Union[str, Any]              =	decode_jitted(**SCREAMING_SNAKE_CASE_			).to_tuple()
  
                     with self.subTest('JIT Disabled'			):
                          with jax.disable_jit():
                               __lowerCamelCase     :			Union[str, Any]              =	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    lowercase_       (							self			)       ->  str:
           for model_class_name in self.all_model_classes:
                __lowerCamelCase     :			Tuple              =	model_class_name.from_pretrained('facebook/mbart-large-cc25'    ,  from_pt=SCREAMING_SNAKE_CASE_			)
                # FlaxMBartForSequenceClassification expects eos token in input_ids
                __lowerCamelCase     :			List[str]              =	np.ones((1, 1)			) * model.config.eos_token_id
                __lowerCamelCase     :			Tuple              =	model(SCREAMING_SNAKE_CASE_			)
                self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class      UpperCAmelCase_						(unittest.TestCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Union[str, Any]							  =    [
          ' UN Chief Says There Is No Military Solution in Syria',
      ]
      lowerCamelCase						:      str							  =    [
          'Şeful ONU declară că nu există o soluţie militară în Siria',
      ]
      lowerCamelCase						:      Tuple							  =    'facebook/mbart-large-en-ro'
  
  
  
  
  
  
  
      @cached_property
      def    lowercase_       (							self			)       ->  int:
           return AutoTokenizer.from_pretrained(self.model_name			)
  
  
  
  
  
  
  
      @cached_property
      def    lowercase_       (							self			)       ->  Dict:
           __lowerCamelCase     :			Union[str, Any]              =	FlaxMBartForConditionalGeneration.from_pretrained(self.model_name    ,  from_pt=SCREAMING_SNAKE_CASE_			)
           return model
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  **SCREAMING_SNAKE_CASE_			)       ->  str:
           __lowerCamelCase     :			Optional[int]              =	self.translate_src_text(**SCREAMING_SNAKE_CASE_			)
           self.assertListEqual(self.expected_text    ,  SCREAMING_SNAKE_CASE_			)
  
  
  
  
  
  
  
      def    lowercase_       (							self    ,  **SCREAMING_SNAKE_CASE_			)       ->  Any:
           __lowerCamelCase     :			Optional[int]              =	self.tokenizer(self.src_text    ,  **SCREAMING_SNAKE_CASE_    ,  return_tensors='np'			)
           __lowerCamelCase     :			Union[str, Any]              =	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
           __lowerCamelCase     :			int              =	self.tokenizer.batch_decode(SCREAMING_SNAKE_CASE_    ,  skip_special_tokens=SCREAMING_SNAKE_CASE_			)
           return generated_words
  
  
  
  
  
  
  
      @slow
      def    lowercase_       (							self			)       ->  List[str]:
           self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       str							)  ->	int:
       __lowerCamelCase     :			Tuple              =	str(UpperCAmelCase_							).strip()
       if not bin_string:
            raise ValueError('Empty string was passed to the function'							)
       __lowerCamelCase     :			Optional[int]              =	bin_string[0] == '-'
       if is_negative:
            __lowerCamelCase     :			List[Any]              =	bin_string[1:]
       if not all(char in '01' for char in bin_string							):
            raise ValueError('Non-binary value was passed to the function'							)
       __lowerCamelCase     :			Optional[int]              =	0
       for char in bin_string:
            __lowerCamelCase     :			str              =	2 * decimal_number + int(UpperCAmelCase_							)
       return -decimal_number if is_negative else decimal_number
  
  
  if __name__ == "__main__":
        from doctest import testmod
  
        testmod()
  
  
  
  
  
  
  