code: 
  
  from __future__ import annotations
  
  import unittest
  import warnings
  
  from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
  from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
  from transformers.utils import cached_property
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
    import tensorflow as tf
  
    from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel
  
  
  @require_tf
  class    SCREAMING_SNAKE_CASE__      :
      __lowerCamelCase		: Union[str, Any]														=  MarianConfig
      __lowerCamelCase		: Dict														=  {}
      __lowerCamelCase		: int														=  'gelu'
  
  
  
  
  
      def __init__(   self		,     a		,     a=13		,     a=7		,     a=True		,     a=False		,     a=99		,     a=32		,     a=2		,     a=4		,     a=37		,     a=0.1		,     a=0.1		,     a=20		,     a=2		,     a=1		,     a=0		,     ):
           lowercase__					:     Tuple   = parent
           lowercase__					:     Any   = batch_size
           lowercase__					:     Optional[Any]   = seq_length
           lowercase__					:     int   = is_training
           lowercase__					:     str   = use_labels
           lowercase__					:     Union[str, Any]   = vocab_size
           lowercase__					:     List[Any]   = hidden_size
           lowercase__					:     Union[str, Any]   = num_hidden_layers
           lowercase__					:     Union[str, Any]   = num_attention_heads
           lowercase__					:     Any   = intermediate_size
  
           lowercase__					:     Union[str, Any]   = hidden_dropout_prob
           lowercase__					:     Union[str, Any]   = attention_probs_dropout_prob
           lowercase__					:     str   = max_position_embeddings
           lowercase__					:     int   = eos_token_id
           lowercase__					:     List[Any]   = pad_token_id
           lowercase__					:     Union[str, Any]   = bos_token_id
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__					:     Any   = ids_tensor([self.batch_size, self.seq_length - 1]		,     self.vocab_size)
           lowercase__					:     Union[str, Any]   = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)		,     1)
           lowercase__					:     str   = tf.concat([input_ids, eos_tensor]		,     axis=1)
  
           lowercase__					:     Union[str, Any]   = ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size)
  
           lowercase__					:     str   = self.config_cls(
               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_ids=[2]		,     bos_token_id=self.bos_token_id		,     pad_token_id=self.pad_token_id		,     decoder_start_token_id=self.pad_token_id		,     **self.config_updates		,     )
           lowercase__					:     str   = prepare_marian_inputs_dict(UpperCamelCase__		,     UpperCamelCase__		,     UpperCamelCase__)
           return config, inputs_dict
  
  
  
  
  
      def    snake_case_       (   self		,     a		,     a):
           lowercase__					:     Optional[int]   = TFMarianModel(config=UpperCamelCase__).get_decoder()
           lowercase__					:     str   = inputs_dict['input_ids']
  
           lowercase__					:     Union[str, Any]   = input_ids[:1, :]
           lowercase__					:     Tuple   = inputs_dict['attention_mask'][:1, :]
           lowercase__					:     Optional[Any]   = inputs_dict['head_mask']
           lowercase__					:     Dict   = 1
  
           # first forward pass
           lowercase__					:     List[str]   = model(UpperCamelCase__		,     attention_mask=UpperCamelCase__		,     head_mask=UpperCamelCase__		,     use_cache=UpperCamelCase__)
  
           lowercase__   ,				lowercase__					:     List[Any]   = outputs.to_tuple()
  
           # create hypothetical next token and extent to next_input_ids
           lowercase__					:     Tuple   = ids_tensor((self.batch_size, 3)		,     config.vocab_size)
           lowercase__					:     int   = tf.cast(ids_tensor((self.batch_size, 3)		,     2)		,     tf.inta)
  
           # append to next input_ids and
           lowercase__					:     Optional[Any]   = tf.concat([input_ids, next_tokens]		,     axis=-1)
           lowercase__					:     Optional[Any]   = tf.concat([attention_mask, next_attn_mask]		,     axis=-1)
  
           lowercase__					:     List[str]   = model(UpperCamelCase__		,     attention_mask=UpperCamelCase__)[0]
           lowercase__					:     Dict   = model(UpperCamelCase__		,     attention_mask=UpperCamelCase__		,     past_key_values=UpperCamelCase__)[0]
  
           self.parent.assertEqual(next_tokens.shape[1]		,     output_from_past.shape[1])
  
           # select random slice
           lowercase__					:     str   = int(ids_tensor((1,)		,     output_from_past.shape[-1]))
           lowercase__					:     str   = output_from_no_past[:, -3:, random_slice_idx]
           lowercase__					:     Tuple   = output_from_past[:, :, random_slice_idx]
  
           # test that outputs are equal for slice
           tf.debugging.assert_near(UpperCamelCase__		,     UpperCamelCase__		,     rtol=1e-3)
  
  
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					Any			,  SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					str=None			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]=None			,  SCREAMING_SNAKE_CASE_	:					int=None			,  SCREAMING_SNAKE_CASE_	:					Dict=None			,  SCREAMING_SNAKE_CASE_	:					List[str]=None			,  ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       if attention_mask is None:
            lowercase__					:     Any   = tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE_			,  config.pad_token_id    )			,  tf.inta    )
       if decoder_attention_mask is None:
            lowercase__					:     str   = tf.concat(
                [
                    tf.ones(decoder_input_ids[:, :1].shape			,  dtype=tf.inta    ),
                    tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]			,  config.pad_token_id    )			,  tf.inta    ),
                ]			,  axis=-1			,  )
       if head_mask is None:
            lowercase__					:     List[Any]   = tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
       if decoder_head_mask is None:
            lowercase__					:     int   = tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
       if cross_attn_head_mask is None:
            lowercase__					:     Optional[Any]   = tf.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,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
           "cross_attn_head_mask": cross_attn_head_mask,
       }
  
  
  @require_tf
  class    SCREAMING_SNAKE_CASE__      (_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE	, unittest.TestCase  ):
      __lowerCamelCase		: Any														=  (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
      __lowerCamelCase		: Tuple														=  (TFMarianMTModel,) if is_tf_available() else ()
      __lowerCamelCase		: Tuple														=  (
          {
              'conversational': TFMarianMTModel,
              'feature-extraction': TFMarianModel,
              'summarization': TFMarianMTModel,
              'text2text-generation': TFMarianMTModel,
              'translation': TFMarianMTModel,
          }
          if is_tf_available()
          else {}
      )
      __lowerCamelCase		: int														=  True
      __lowerCamelCase		: List[str]														=  False
      __lowerCamelCase		: Union[str, Any]														=  False
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__					:     List[Any]   = TFMarianModelTester(self)
           lowercase__					:     Tuple   = ConfigTester(self		,     config_class=UpperCamelCase__)
  
  
  
  
  
      def    snake_case_       (   self):
           self.config_tester.run_common_tests()
  
  
  
  
  
      def    snake_case_       (   self):
           lowercase__					:     Optional[Any]   = self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase__)
  
  
  
  @require_tf
  class    SCREAMING_SNAKE_CASE__      (unittest.TestCase  ):
      __lowerCamelCase		: List[Any]														=  1000  # show more chars for failing integration tests
  
  
  
  
  
      @classmethod
      def    snake_case_       (   cls):
           lowercase__					:     List[Any]   = f"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
           return cls
  
  
  
  
  
      @cached_property
      def    snake_case_       (   self):
           return AutoTokenizer.from_pretrained(self.model_name)
  
  
  
  
  
      @property
      def    snake_case_       (   self):
           return self.tokenizer.eos_token_id
  
  
  
  
  
      @cached_property
      def    snake_case_       (   self):
           warnings.simplefilter('error')
           lowercase__					:     Optional[int]   = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
           assert isinstance(UpperCamelCase__		,     UpperCamelCase__)
           lowercase__					:     Any   = model.config
           self.assertListEqual(c.bad_words_ids		,     [[c.pad_token_id]])
           self.assertEqual(c.max_length		,     512)
           self.assertEqual(c.decoder_start_token_id		,     c.pad_token_id)
           return model
  
  
  
  
  
      def    snake_case_       (   self		,     **a):
           lowercase__					:     List[Any]   = self.translate_src_text(**UpperCamelCase__)
           self.assertListEqual(self.expected_text		,     UpperCamelCase__)
  
  
  
  
  
      def    snake_case_       (   self		,     **a):
           lowercase__					:     List[str]   = self.tokenizer(self.src_text		,     **UpperCamelCase__		,     padding=UpperCamelCase__		,     return_tensors='tf')
           lowercase__					:     Optional[int]   = self.model.generate(
               model_inputs.input_ids		,     attention_mask=model_inputs.attention_mask		,     num_beams=2		,     max_length=128)
           lowercase__					:     List[Any]   = self.tokenizer.batch_decode(generated_ids.numpy()		,     skip_special_tokens=UpperCamelCase__)
           return generated_words
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class    SCREAMING_SNAKE_CASE__      (_SCREAMING_SNAKE_CASE  ):
      __lowerCamelCase		: str														=  'mt'
      __lowerCamelCase		: List[str]														=  'en'
      __lowerCamelCase		: List[str]														=  ['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
      __lowerCamelCase		: Any														=  ['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']
  
  
  
  
  
      @unittest.skip('Skipping until #12647 is resolved.')
      @slow
      def    snake_case_       (   self):
           self._assert_generated_batch_equal_expected()
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class    SCREAMING_SNAKE_CASE__      (_SCREAMING_SNAKE_CASE  ):
      __lowerCamelCase		: str														=  'en'
      __lowerCamelCase		: Optional[Any]														=  'zh'
      __lowerCamelCase		: List[str]														=  ['My name is Wolfgang and I live in Berlin']
      __lowerCamelCase		: str														=  ['我叫沃尔夫冈 我住在柏林']
  
  
  
  
  
      @unittest.skip('Skipping until #12647 is resolved.')
      @slow
      def    snake_case_       (   self):
           self._assert_generated_batch_equal_expected()
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class    SCREAMING_SNAKE_CASE__      (_SCREAMING_SNAKE_CASE  ):
      __lowerCamelCase		: Optional[Any]														=  'en'
      __lowerCamelCase		: str														=  'ROMANCE'
      __lowerCamelCase		: List[Any]														=  [
          '>>fr<< Don\'t spend so much time watching TV.',
          '>>pt<< Your message has been sent.',
          '>>es<< He\'s two years older than me.',
      ]
      __lowerCamelCase		: Tuple														=  [
          'Ne passez pas autant de temps à regarder la télé.',
          'A sua mensagem foi enviada.',
          'Es dos años más viejo que yo.',
      ]
  
  
  
  
  
      @unittest.skip('Skipping until #12647 is resolved.')
      @slow
      def    snake_case_       (   self):
           self._assert_generated_batch_equal_expected()
  
  
  
  
  
      @unittest.skip('Skipping until #12647 is resolved.')
      @slow
      def    snake_case_       (   self):
           lowercase__					:     Union[str, Any]   = TranslationPipeline(self.model		,     self.tokenizer		,     framework='tf')
           lowercase__					:     Optional[Any]   = pipeline(self.src_text)
           self.assertEqual(self.expected_text		,     [x['translation_text'] for x in output])
  
  
style_context: 
  
  from __future__ import annotations
  
  
  class    SCREAMING_SNAKE_CASE__      :
  
  
  
  
  
      def __init__(   self		,     a = 0):
           lowercase__					:     int   = value
           lowercase__					:     Node | None   = None
           lowercase__					:     Node | None   = None
  
  
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Node | None			,  SCREAMING_SNAKE_CASE_	:					Node | None    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       if treea is None:
            return treea
       if treea is None:
            return treea
  
       lowercase__					:     Optional[Any]   = treea.value + treea.value
       lowercase__					:     Union[str, Any]   = merge_two_binary_trees(treea.left			,  treea.left    )
       lowercase__					:     Optional[Any]   = merge_two_binary_trees(treea.right			,  treea.right    )
       return treea
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Node | None    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       if root:
            print(root.value    )
            print_preorder(root.left    )
            print_preorder(root.right    )
  
  
  if __name__ == "__main__":
         snake_case_    						=				Node(1)
         snake_case_    						=				Node(2)
         snake_case_    						=				Node(3)
         snake_case_    						=				Node(4)
  
         snake_case_    						=				Node(2)
         snake_case_    						=				Node(4)
         snake_case_    						=				Node(6)
         snake_case_    						=				Node(9)
         snake_case_    						=				Node(5)
  
         print('''Tree1 is: ''')
         print_preorder(treea)
         print('''Tree2 is: ''')
         print_preorder(treea)
         snake_case_    						=				merge_two_binary_trees(treea, treea)
         print('''Merged Tree is: ''')
         print_preorder(merged_tree)
  
  