






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_					(						lowercase_	,  lowercase_	,  lowercase_=None	,  lowercase_=None	,  lowercase_=None	,  lowercase_=None	,  lowercase_=None	,  lowercase_=None	,  )				->Tuple:







 """simple docstring"""

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



     """simple docstring"""



     def __init__(		self					:	str  ,  lowerCAmelCase__					:	Dict  ,  lowerCAmelCase__					:	Any=1_3  ,  lowerCAmelCase__					:	int=7  ,  lowerCAmelCase__					:	Dict=True  ,  lowerCAmelCase__					:	Union[str, Any]=False  ,  lowerCAmelCase__					:	Any=9_9  ,  lowerCAmelCase__					:	List[str]=1_6  ,  lowerCAmelCase__					:	str=2  ,  lowerCAmelCase__					:	Dict=4  ,  lowerCAmelCase__					:	List[str]=4  ,  lowerCAmelCase__					:	Dict="gelu"  ,  lowerCAmelCase__					:	int=0.1  ,  lowerCAmelCase__					:	str=0.1  ,  lowerCAmelCase__					:	str=3_2  ,  lowerCAmelCase__					:	str=2  ,  lowerCAmelCase__					:	str=1  ,  lowerCAmelCase__					:	Union[str, Any]=0  ,  lowerCAmelCase__					:	Optional[int]=2  ,  lowerCAmelCase__					:	Dict=0.02  ,  )							->	List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		parent
      SCREAMING_SNAKE_CASE				    =		batch_size
      SCREAMING_SNAKE_CASE				    =		seq_length
      SCREAMING_SNAKE_CASE				    =		is_training
      SCREAMING_SNAKE_CASE				    =		use_labels
      SCREAMING_SNAKE_CASE				    =		vocab_size
      SCREAMING_SNAKE_CASE				    =		hidden_size
      SCREAMING_SNAKE_CASE				    =		num_hidden_layers
      SCREAMING_SNAKE_CASE				    =		num_attention_heads
      SCREAMING_SNAKE_CASE				    =		intermediate_size
      SCREAMING_SNAKE_CASE				    =		hidden_act
      SCREAMING_SNAKE_CASE				    =		hidden_dropout_prob
      SCREAMING_SNAKE_CASE				    =		attention_probs_dropout_prob
      SCREAMING_SNAKE_CASE				    =		max_position_embeddings
      SCREAMING_SNAKE_CASE				    =		eos_token_id
      SCREAMING_SNAKE_CASE				    =		pad_token_id
      SCREAMING_SNAKE_CASE				    =		bos_token_id
      SCREAMING_SNAKE_CASE				    =		decoder_start_token_id
      SCREAMING_SNAKE_CASE				    =		initializer_range





     def     __UpperCamelCase						(		self					:	Dict)							->	Union[str, Any]:

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

      SCREAMING_SNAKE_CASE				    =		shift_tokens_right(lowerCAmelCase__  ,  1)

      SCREAMING_SNAKE_CASE				    =		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				    =		prepare_mbart_inputs_dict(lowerCAmelCase__  ,  lowerCAmelCase__  ,  lowerCAmelCase__)
      return config, inputs_dict





     def     __UpperCamelCase						(		self					:	Any)							->	Optional[int]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self.prepare_config_and_inputs()
      return config, inputs_dict





     def     __UpperCamelCase						(		self					:	Optional[int]  ,  lowerCAmelCase__					:	List[str]  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	str)							->	Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		2_0
      SCREAMING_SNAKE_CASE				    =		model_class_name(lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		model.encode(inputs_dict['input_ids'])

      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		(
          inputs_dict['decoder_input_ids'],
          inputs_dict['decoder_attention_mask'],
      )

      SCREAMING_SNAKE_CASE				    =		model.init_cache(decoder_input_ids.shape[0]  ,  lowerCAmelCase__  ,  lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)  ,  dtype='i4')

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

      SCREAMING_SNAKE_CASE				    =		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,  dtype='i4')
      SCREAMING_SNAKE_CASE				    =		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				    =		model.decode(lowerCAmelCase__  ,  lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		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     __UpperCamelCase						(		self					:	List[str]  ,  lowerCAmelCase__					:	List[str]  ,  lowerCAmelCase__					:	List[str]  ,  lowerCAmelCase__					:	Optional[Any])							->	List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		2_0
      SCREAMING_SNAKE_CASE				    =		model_class_name(lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		model.encode(inputs_dict['input_ids'])

      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		(
          inputs_dict['decoder_input_ids'],
          inputs_dict['decoder_attention_mask'],
      )

      SCREAMING_SNAKE_CASE				    =		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				    =		model.init_cache(decoder_input_ids.shape[0]  ,  lowerCAmelCase__  ,  lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		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				    =		model.decode(
          decoder_input_ids[:, :-1]  ,  lowerCAmelCase__  ,  decoder_attention_mask=lowerCAmelCase__  ,  past_key_values=lowerCAmelCase__  ,  decoder_position_ids=lowerCAmelCase__  ,  )
      SCREAMING_SNAKE_CASE				    =		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,  dtype='i4')
      SCREAMING_SNAKE_CASE				    =		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				    =		model.decode(lowerCAmelCase__  ,  lowerCAmelCase__  ,  decoder_attention_mask=lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		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 	a_(		unittest.TestCase	):



     """simple docstring"""



     __snake_case		:				Optional[Any]  					=99





     def     __UpperCamelCase						(		self					:	Tuple)							->	Optional[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		np.array(
          [
              [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
              [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
              [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
              [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
              [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
              [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
              [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
              [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
              [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
              [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
              [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
              [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
              [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
          ]  ,  dtype=np.intaa  ,  )

      SCREAMING_SNAKE_CASE				    =		input_ids.shape[0]
      SCREAMING_SNAKE_CASE				    =		MBartConfig(
          vocab_size=self.vocab_size  ,  d_model=2_4  ,  encoder_layers=2  ,  decoder_layers=2  ,  encoder_attention_heads=2  ,  decoder_attention_heads=2  ,  encoder_ffn_dim=3_2  ,  decoder_ffn_dim=3_2  ,  max_position_embeddings=4_8  ,  eos_token_id=2  ,  pad_token_id=1  ,  bos_token_id=0  ,  )
      return config, input_ids, batch_size





     def     __UpperCamelCase						(		self					:	str)							->	Optional[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self._get_config_and_data()
      SCREAMING_SNAKE_CASE				    =		FlaxMBartForSequenceClassification(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		model(input_ids=lowerCAmelCase__  ,  decoder_input_ids=lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		(batch_size, config.num_labels)
      self.assertEqual(outputs['logits'].shape  ,  lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	List[str])							->	int:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self._get_config_and_data()
      SCREAMING_SNAKE_CASE				    =		FlaxMBartForQuestionAnswering(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		model(input_ids=lowerCAmelCase__)

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





     def     __UpperCamelCase						(		self					:	Dict)							->	str:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		self._get_config_and_data()
      SCREAMING_SNAKE_CASE				    =		FlaxMBartForConditionalGeneration(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		lm_model(input_ids=lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		(batch_size, input_ids.shape[1], config.vocab_size)
      self.assertEqual(outputs['logits'].shape  ,  lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	Optional[Any])							->	str:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		MBartConfig(
          vocab_size=self.vocab_size  ,  d_model=1_4  ,  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=4_8  ,  )
      SCREAMING_SNAKE_CASE				    =		FlaxMBartForConditionalGeneration(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]  ,  dtype=np.intaa)
      SCREAMING_SNAKE_CASE				    =		np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]  ,  dtype=np.intaa)
      SCREAMING_SNAKE_CASE				    =		lm_model(input_ids=lowerCAmelCase__  ,  decoder_input_ids=lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		(*summary.shape, config.vocab_size)
      self.assertEqual(outputs['logits'].shape  ,  lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	List[str])							->	List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]  ,  dtype=np.intaa)
      SCREAMING_SNAKE_CASE				    =		shift_tokens_right(lowerCAmelCase__  ,  1)
      SCREAMING_SNAKE_CASE				    =		np.equal(lowerCAmelCase__  ,  1).astype(np.floataa).sum()
      SCREAMING_SNAKE_CASE				    =		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 	a_(		lowercase__			,							unittest.TestCase			,							lowercase__	):



     """simple docstring"""



     __snake_case		:				Any  					=True
     __snake_case		:				List[str]  					=(
         (
             FlaxMBartModel,
             FlaxMBartForConditionalGeneration,
             FlaxMBartForSequenceClassification,
             FlaxMBartForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )
     __snake_case		:				Optional[int]  					=(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()





     def     __UpperCamelCase						(		self					:	int)							->	Optional[int]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		FlaxMBartModelTester(self)





     def     __UpperCamelCase						(		self					:	Tuple)							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		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     __UpperCamelCase						(		self					:	Optional[Any])							->	Optional[int]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		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     __UpperCamelCase						(		self					:	Optional[int])							->	Union[str, Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		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				    =		self._prepare_for_class(lowerCAmelCase__  ,  lowerCAmelCase__)
        SCREAMING_SNAKE_CASE				    =		model_class(lowerCAmelCase__)

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

        with self.subTest('JIT Enabled'):
         SCREAMING_SNAKE_CASE				    =		encode_jitted(**lowerCAmelCase__).to_tuple()

        with self.subTest('JIT Disabled'):
         with jax.disable_jit():
          SCREAMING_SNAKE_CASE				    =		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     __UpperCamelCase						(		self					:	int)							->	List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE    ,   SCREAMING_SNAKE_CASE				    =		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				    =		model_class(lowerCAmelCase__)
        SCREAMING_SNAKE_CASE				    =		model.encode(inputs_dict['input_ids']  ,  inputs_dict['attention_mask'])

        SCREAMING_SNAKE_CASE				    =		{
            '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__					:	Union[str, Any]  ,  lowerCAmelCase__					:	Optional[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				    =		decode_jitted(**lowerCAmelCase__).to_tuple()

        with self.subTest('JIT Disabled'):
         with jax.disable_jit():
          SCREAMING_SNAKE_CASE				    =		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     __UpperCamelCase						(		self					:	List[Any])							->	Dict:

      """simple docstring"""
      for model_class_name in self.all_model_classes:
       SCREAMING_SNAKE_CASE				    =		model_class_name.from_pretrained('facebook/mbart-large-cc25'  ,  from_pt=lowerCAmelCase__)
       # FlaxMBartForSequenceClassification expects eos token in input_ids
       SCREAMING_SNAKE_CASE				    =		np.ones((1, 1)) * model.config.eos_token_id
       SCREAMING_SNAKE_CASE				    =		model(lowerCAmelCase__)
       self.assertIsNotNone(lowerCAmelCase__)

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



     """simple docstring"""



     __snake_case		:				int  					=[
         ''' UN Chief Says There Is No Military Solution in Syria''',
     ]
     __snake_case		:				List[str]  					=[
         '''Şeful ONU declară că nu există o soluţie militară în Siria''',
     ]
     __snake_case		:				Optional[Any]  					='''facebook/mbart-large-en-ro'''





     @cached_property
     def     __UpperCamelCase						(		self					:	Optional[Any])							->	str:

      """simple docstring"""
      return AutoTokenizer.from_pretrained(self.model_name)





     @cached_property
     def     __UpperCamelCase						(		self					:	str)							->	Any:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		FlaxMBartForConditionalGeneration.from_pretrained(self.model_name  ,  from_pt=lowerCAmelCase__)
      return model





     def     __UpperCamelCase						(		self					:	List[str]  ,  **lowerCAmelCase__					:	Dict)							->	Dict:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.translate_src_text(**lowerCAmelCase__)
      self.assertListEqual(self.expected_text  ,  lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	Union[str, Any]  ,  **lowerCAmelCase__					:	List[str])							->	int:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		self.tokenizer(self.src_text  ,  **lowerCAmelCase__  ,  return_tensors='np')
      SCREAMING_SNAKE_CASE				    =		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				    =		self.tokenizer.batch_decode(lowerCAmelCase__  ,  skip_special_tokens=lowerCAmelCase__)
      return generated_words





     @slow
     def     __UpperCamelCase						(		self					:	Union[str, Any])							->	Optional[int]:

      """simple docstring"""
      self._assert_generated_batch_equal_expected()






"""simple docstring"""


def        _lowerCamelCase(__UpperCamelCase = 20			)							->					int:

	try:
		_lowerCAmelCase								=int(__UpperCamelCase			)
	except (TypeError, ValueError):
		raise TypeError("""Parameter n must be int or castable to int."""			)
	if n <= 0:
		raise ValueError("""Parameter n must be greater than or equal to one."""			)
	_lowerCAmelCase								=0
	while 1:
		i += n * (n - 1)
		_lowerCAmelCase								=0
		for j in range(2       ,					__UpperCamelCase			):
			if i % j != 0:
				_lowerCAmelCase								=1
				break
		if nfound == 0:
			if i == 0:
				_lowerCAmelCase								=1
			return i
	return None


if __name__ == "__main__":
			print(F"""{solution() = }""")



