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 	UpperCamelCase_     :
  					_a			:	Any							=				MarianConfig
  					_a			:	Optional[Any]							=				{}
  					_a			:	int							=				'gelu'
  
  
  
  					def __init__(		self    :    Optional[Any]					,			lowerCamelCase    :    Tuple					,			lowerCamelCase    :    List[Any]=13					,			lowerCamelCase    :    int=7					,			lowerCamelCase    :    Tuple=True					,			lowerCamelCase    :    List[str]=False					,			lowerCamelCase    :    Tuple=99					,			lowerCamelCase    :    int=32					,			lowerCamelCase    :    Union[str, Any]=2					,			lowerCamelCase    :    Optional[int]=4					,			lowerCamelCase    :    Dict=37					,			lowerCamelCase    :    Optional[int]=0.1					,			lowerCamelCase    :    Dict=0.1					,			lowerCamelCase    :    Optional[int]=20					,			lowerCamelCase    :    List[str]=2					,			lowerCamelCase    :    Dict=1					,			lowerCamelCase    :    int=0					,			):
  							lowerCamelCase_  :    int   	=			parent
  							lowerCamelCase_  :    int   	=			batch_size
  							lowerCamelCase_  :    str   	=			seq_length
  							lowerCamelCase_  :    Tuple   	=			is_training
  							lowerCamelCase_  :    List[Any]   	=			use_labels
  							lowerCamelCase_  :    Tuple   	=			vocab_size
  							lowerCamelCase_  :    int   	=			hidden_size
  							lowerCamelCase_  :    List[str]   	=			num_hidden_layers
  							lowerCamelCase_  :    Tuple   	=			num_attention_heads
  							lowerCamelCase_  :    Any   	=			intermediate_size
  
  							lowerCamelCase_  :    List[str]   	=			hidden_dropout_prob
  							lowerCamelCase_  :    Optional[int]   	=			attention_probs_dropout_prob
  							lowerCamelCase_  :    int   	=			max_position_embeddings
  							lowerCamelCase_  :    List[str]   	=			eos_token_id
  							lowerCamelCase_  :    Optional[int]   	=			pad_token_id
  							lowerCamelCase_  :    Any   	=			bos_token_id
  
  
  
  					def      __a    (		self    :    Dict	):
  							lowerCamelCase_  :    Optional[int]   	=			ids_tensor([self.batch_size, self.seq_length - 1]					,			self.vocab_size	)
  							lowerCamelCase_  :    List[Any]   	=			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size	)					,			1	)
  							lowerCamelCase_  :    Any   	=			tf.concat([input_ids, eos_tensor]					,			axis=1	)
  
  							lowerCamelCase_  :    Dict   	=			ids_tensor([self.batch_size, self.seq_length]					,			self.vocab_size	)
  
  							lowerCamelCase_  :    List[Any]   	=			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					,			)
  							lowerCamelCase_  :    Optional[Any]   	=			prepare_marian_inputs_dict(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase	)
  							return config, inputs_dict
  
  
  
  
  
  					def      __a    (		self    :    List[Any]					,			lowerCamelCase    :    List[Any]					,			lowerCamelCase    :    Dict	):
  							lowerCamelCase_  :    Optional[Any]   	=			TFMarianModel(config=lowerCamelCase	).get_decoder()
  							lowerCamelCase_  :    Any   	=			inputs_dict['input_ids']
  
  							lowerCamelCase_  :    Tuple   	=			input_ids[:1, :]
  							lowerCamelCase_  :    Any   	=			inputs_dict['attention_mask'][:1, :]
  							lowerCamelCase_  :    Optional[Any]   	=			inputs_dict['head_mask']
  							lowerCamelCase_  :    List[Any]   	=			1
  
  							# first forward pass
  							lowerCamelCase_  :    Optional[int]   	=			model(lowerCamelCase					,			attention_mask=lowerCamelCase					,			head_mask=lowerCamelCase					,			use_cache=lowerCamelCase	)
  
  							lowerCamelCase_	,			lowerCamelCase_  :    int   	=			outputs.to_tuple()
  
  							# create hypothetical next token and extent to next_input_ids
  							lowerCamelCase_  :    List[Any]   	=			ids_tensor((self.batch_size, 3)					,			config.vocab_size	)
  							lowerCamelCase_  :    Any   	=			tf.cast(ids_tensor((self.batch_size, 3)					,			2	)					,			tf.inta	)
  
  							# append to next input_ids and
  							lowerCamelCase_  :    str   	=			tf.concat([input_ids, next_tokens]					,			axis=-1	)
  							lowerCamelCase_  :    Union[str, Any]   	=			tf.concat([attention_mask, next_attn_mask]					,			axis=-1	)
  
  							lowerCamelCase_  :    Optional[Any]   	=			model(lowerCamelCase					,			attention_mask=lowerCamelCase	)[0]
  							lowerCamelCase_  :    Union[str, Any]   	=			model(lowerCamelCase					,			attention_mask=lowerCamelCase					,			past_key_values=lowerCamelCase	)[0]
  
  							self.parent.assertEqual(next_tokens.shape[1]					,			output_from_past.shape[1]	)
  
  							# select random slice
  							lowerCamelCase_  :    List[str]   	=			int(ids_tensor((1,)					,			output_from_past.shape[-1]	)	)
  							lowerCamelCase_  :    Any   	=			output_from_no_past[:, -3:, random_slice_idx]
  							lowerCamelCase_  :    Any   	=			output_from_past[:, :, random_slice_idx]
  
  							# test that outputs are equal for slice
  							tf.debugging.assert_near(lowerCamelCase					,			lowerCamelCase					,			rtol=1E-3	)
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__=None       ,lowerCAmelCase__=None       ,lowerCAmelCase__=None       ,lowerCAmelCase__=None       ,lowerCAmelCase__=None       ,):
  		if attention_mask is None:
  				lowerCamelCase_  :    Any   	=			tf.cast(tf.math.not_equal(lowerCAmelCase__       ,config.pad_token_id	)       ,tf.inta	)
  		if decoder_attention_mask is None:
  				lowerCamelCase_  :    Union[str, Any]   	=			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:
  				lowerCamelCase_  :    Optional[int]   	=			tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
  		if decoder_head_mask is None:
  				lowerCamelCase_  :    List[Any]   	=			tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
  		if cross_attn_head_mask is None:
  				lowerCamelCase_  :    List[str]   	=			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 	UpperCamelCase_     (     snake_case__     ,    snake_case__     ,    unittest.TestCase       ):
  					_a			:	Tuple							=				(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
  					_a			:	Optional[int]							=				(TFMarianMTModel,) if is_tf_available() else ()
  					_a			:	List[str]							=				(
  					    {
  					        'conversational': TFMarianMTModel,
  					        'feature-extraction': TFMarianModel,
  					        'summarization': TFMarianMTModel,
  					        'text2text-generation': TFMarianMTModel,
  					        'translation': TFMarianMTModel,
  					    }
  					    if is_tf_available()
  					    else {}
  					)
  					_a			:	str							=				True
  					_a			:	Any							=				False
  					_a			:	Optional[Any]							=				False
  
  
  
  					def      __a    (		self    :    Optional[Any]	):
  							lowerCamelCase_  :    str   	=			TFMarianModelTester(self	)
  							lowerCamelCase_  :    Tuple   	=			ConfigTester(self					,			config_class=lowerCamelCase	)
  
  
  
  					def      __a    (		self    :    Tuple	):
  							self.config_tester.run_common_tests()
  
  
  
  
  
  					def      __a    (		self    :    Any	):
  							lowerCamelCase_  :    int   	=			self.model_tester.prepare_config_and_inputs_for_common()
  							self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase	)
  
  
  
  
  
  
  @require_tf
  class 	UpperCamelCase_     (     unittest.TestCase       ):
  					_a			:	List[str]							=				1_0_0_0  # show more chars for failing integration tests
  
  
  
  					@classmethod
  					def      __a    (		cls    :    Dict	):
  							lowerCamelCase_  :    Optional[Any]   	=			F"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
  							return cls
  
  
  
  					@cached_property
  					def      __a    (		self    :    Union[str, Any]	):
  							return AutoTokenizer.from_pretrained(self.model_name	)
  
  
  
  					@property
  					def      __a    (		self    :    Optional[Any]	):
  							return self.tokenizer.eos_token_id
  
  
  
  					@cached_property
  					def      __a    (		self    :    Any	):
  							warnings.simplefilter('error'	)
  							lowerCamelCase_  :    TFMarianMTModel   	=			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name	)
  							assert isinstance(lowerCamelCase					,			lowerCamelCase	)
  							lowerCamelCase_  :    List[Any]   	=			model.config
  							self.assertListEqual(c.bad_words_ids					,			[[c.pad_token_id]]	)
  							self.assertEqual(c.max_length					,			5_12	)
  							self.assertEqual(c.decoder_start_token_id					,			c.pad_token_id	)
  							return model
  
  
  
  					def      __a    (		self    :    int					,			**lowerCamelCase    :    int	):
  							lowerCamelCase_  :    List[str]   	=			self.translate_src_text(**lowerCamelCase	)
  							self.assertListEqual(self.expected_text					,			lowerCamelCase	)
  
  
  
  
  
  					def      __a    (		self    :    Optional[int]					,			**lowerCamelCase    :    int	):
  							lowerCamelCase_  :    str   	=			self.tokenizer(self.src_text					,			**lowerCamelCase					,			padding=lowerCamelCase					,			return_tensors='tf'	)
  							lowerCamelCase_  :    Dict   	=			self.model.generate(
  							    model_inputs.input_ids					,			attention_mask=model_inputs.attention_mask					,			num_beams=2					,			max_length=1_28	)
  							lowerCamelCase_  :    Dict   	=			self.tokenizer.batch_decode(generated_ids.numpy()					,			skip_special_tokens=lowerCamelCase	)
  							return generated_words
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class 	UpperCamelCase_     (     snake_case__       ):
  					_a			:	Dict							=				'mt'
  					_a			:	Optional[Any]							=				'en'
  					_a			:	int							=				['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
  					_a			:	Optional[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      __a    (		self    :    Optional[Any]	):
  							self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class 	UpperCamelCase_     (     snake_case__       ):
  					_a			:	Optional[Any]							=				'en'
  					_a			:	Optional[Any]							=				'zh'
  					_a			:	Union[str, Any]							=				['My name is Wolfgang and I live in Berlin']
  					_a			:	Dict							=				['我叫沃尔夫冈 我住在柏林']
  
  
  
  					@unittest.skip('Skipping until #12647 is resolved.'	)
  					@slow
  					def      __a    (		self    :    Tuple	):
  							self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class 	UpperCamelCase_     (     snake_case__       ):
  					_a			:	str							=				'en'
  					_a			:	Optional[int]							=				'ROMANCE'
  					_a			:	Dict							=				[
  					    '>>fr<< Don\'t spend so much time watching TV.',
  					    '>>pt<< Your message has been sent.',
  					    '>>es<< He\'s two years older than me.',
  					]
  					_a			:	List[str]							=				[
  					    '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      __a    (		self    :    List[Any]	):
  							self._assert_generated_batch_equal_expected()
  
  
  
  
  
  					@unittest.skip('Skipping until #12647 is resolved.'	)
  					@slow
  					def      __a    (		self    :    Any	):
  							lowerCamelCase_  :    Union[str, Any]   	=			TranslationPipeline(self.model					,			self.tokenizer					,			framework='tf'	)
  							lowerCamelCase_  :    List[Any]   	=			pipeline(self.src_text	)
  							self.assertEqual(self.expected_text					,			[x['translation_text'] for x in output]	)
  
style_context: 
  
  
  
  
  
  from __future__ import annotations
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
  		lowerCamelCase_  :    int
  		lowerCamelCase_  :    list[str]   	=			list(lowerCAmelCase__	)
  		lowerCamelCase_	,			lowerCamelCase_  :    List[str]   	=			_vector[:-1], int(_vector[-1]	)
  		lowerCamelCase_  :    list[int]   	=			[int(lowerCAmelCase__	) for digit in __vector]
  
  		vector.reverse()
  		for i, digit in enumerate(lowerCAmelCase__	):
  				if i & 1 == 0:
  						lowerCamelCase_  :    int   	=			digit * 2
  						if doubled > 9:
  								doubled -= 9
  						check_digit += doubled
  				else:
  						check_digit += digit
  
  		return check_digit % 10 == 0
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  		assert is_luhn("""79927398713""")
  		assert not is_luhn("""79927398714""")
  