code: 
  '''simple docstring'''
  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   snake_case__					:
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	MarianConfig
  		lowerCamelCase           =	{}
  		lowerCamelCase           =	"""gelu"""
  
  
  
  		def __init__(			self							: Optional[Any]     ,  UpperCamelCase__							: Optional[Any]     ,  UpperCamelCase__							: Any=13     ,  UpperCamelCase__							: Optional[Any]=7     ,  UpperCamelCase__							: Union[str, Any]=True     ,  UpperCamelCase__							: Tuple=False     ,  UpperCamelCase__							: Union[str, Any]=99     ,  UpperCamelCase__							: Union[str, Any]=32     ,  UpperCamelCase__							: Any=2     ,  UpperCamelCase__							: List[Any]=4     ,  UpperCamelCase__							: Any=37     ,  UpperCamelCase__							: str=0.1     ,  UpperCamelCase__							: Any=0.1     ,  UpperCamelCase__							: str=20     ,  UpperCamelCase__							: Any=2     ,  UpperCamelCase__							: Optional[int]=1     ,  UpperCamelCase__							: Optional[int]=0     ,  )	-> Union[str, Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Dict     =	parent
  				snake_case     :					Tuple     =	batch_size
  				snake_case     :					List[str]     =	seq_length
  				snake_case     :					Tuple     =	is_training
  				snake_case     :					Optional[Any]     =	use_labels
  				snake_case     :					Optional[int]     =	vocab_size
  				snake_case     :					Any     =	hidden_size
  				snake_case     :					int     =	num_hidden_layers
  				snake_case     :					List[str]     =	num_attention_heads
  				snake_case     :					Tuple     =	intermediate_size
  
  				snake_case     :					int     =	hidden_dropout_prob
  				snake_case     :					Union[str, Any]     =	attention_probs_dropout_prob
  				snake_case     :					Union[str, Any]     =	max_position_embeddings
  				snake_case     :					Optional[int]     =	eos_token_id
  				snake_case     :					List[Any]     =	pad_token_id
  				snake_case     :					Optional[Any]     =	bos_token_id
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict			)	-> Dict:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Any     =	ids_tensor([self.batch_size, self.seq_length - 1]     ,  self.vocab_size			)
  				snake_case     :					Tuple     =	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size			)     ,  1			)
  				snake_case     :					List[Any]     =	tf.concat([input_ids, eos_tensor]     ,  axis=1			)
  
  				snake_case     :					str     =	ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size			)
  
  				snake_case     :					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     ,  )
  				snake_case     :					Dict     =	prepare_marian_inputs_dict(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__			)
  				return config, inputs_dict
  
  
  
  		def 			lowerCAmelCase							(			self							: List[str]     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: str			)	-> Optional[int]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					int     =	TFMarianModel(config=UpperCamelCase__			).get_decoder()
  				snake_case     :					int     =	inputs_dict['''input_ids''']
  
  				snake_case     :					int     =	input_ids[:1, :]
  				snake_case     :					Dict     =	inputs_dict['''attention_mask'''][:1, :]
  				snake_case     :					List[Any]     =	inputs_dict['''head_mask''']
  				snake_case     :					str     =	1
  
  				# first forward pass
  				snake_case     :					str     =	model(UpperCamelCase__     ,  attention_mask=UpperCamelCase__     ,  head_mask=UpperCamelCase__     ,  use_cache=UpperCamelCase__			)
  
  				snake_case     :					List[str]     =	outputs.to_tuple()
  
  				# create hypothetical next token and extent to next_input_ids
  				snake_case     :					List[str]     =	ids_tensor((self.batch_size, 3)     ,  config.vocab_size			)
  				snake_case     :					str     =	tf.cast(ids_tensor((self.batch_size, 3)     ,  2			)     ,  tf.inta			)
  
  				# append to next input_ids and
  				snake_case     :					Tuple     =	tf.concat([input_ids, next_tokens]     ,  axis=-1			)
  				snake_case     :					Any     =	tf.concat([attention_mask, next_attn_mask]     ,  axis=-1			)
  
  				snake_case     :					Dict     =	model(UpperCamelCase__     ,  attention_mask=UpperCamelCase__			)[0]
  				snake_case     :					Optional[int]     =	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
  				snake_case     :					List[str]     =	int(ids_tensor((1,)     ,  output_from_past.shape[-1]			)			)
  				snake_case     :					Optional[Any]     =	output_from_no_past[:, -3:, random_slice_idx]
  				snake_case     :					str     =	output_from_past[:, :, random_slice_idx]
  
  				# test that outputs are equal for slice
  				tf.debugging.assert_near(UpperCamelCase__     ,  UpperCamelCase__     ,  rtol=1e-3			)
  
  
  
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__=None   ,					SCREAMING_SNAKE_CASE__=None   ,					SCREAMING_SNAKE_CASE__=None   ,					SCREAMING_SNAKE_CASE__=None   ,					SCREAMING_SNAKE_CASE__=None   ,					)     ->       Optional[Any]:
  
  
  		'''simple docstring'''
  
  
  		if attention_mask is None:
  				snake_case     :					Dict     =	tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE__   ,					config.pad_token_id )   ,					tf.inta )
  		if decoder_attention_mask is None:
  				snake_case     :					Optional[int]     =	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:
  				snake_case     :					Tuple     =	tf.ones((config.encoder_layers, config.encoder_attention_heads) )
  		if decoder_head_mask is None:
  				snake_case     :					Optional[Any]     =	tf.ones((config.decoder_layers, config.decoder_attention_heads) )
  		if cross_attn_head_mask is None:
  				snake_case     :					Union[str, 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   snake_case__					(			__SCREAMING_SNAKE_CASE						,				__SCREAMING_SNAKE_CASE						,				unittest.TestCase				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
  		lowerCamelCase           =	(TFMarianMTModel,) if is_tf_available() else ()
  		lowerCamelCase           =	(
  		    {
  		        """conversational""": TFMarianMTModel,
  		        """feature-extraction""": TFMarianModel,
  		        """summarization""": TFMarianMTModel,
  		        """text2text-generation""": TFMarianMTModel,
  		        """translation""": TFMarianMTModel,
  		    }
  		    if is_tf_available()
  		    else {}
  		)
  		lowerCamelCase           =	True
  		lowerCamelCase           =	False
  		lowerCamelCase           =	False
  
  
  
  		def 			lowerCAmelCase							(			self							: List[str]			)	-> List[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					int     =	TFMarianModelTester(self			)
  				snake_case     :					int     =	ConfigTester(self     ,  config_class=UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict			)	-> List[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				self.config_tester.run_common_tests()
  
  
  
  		def 			lowerCAmelCase							(			self							: Any			)	-> Optional[int]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Dict     =	self.model_tester.prepare_config_and_inputs_for_common()
  				self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase__			)
  
  
  
  
  
  
  
  @require_tf
  class   snake_case__					(			unittest.TestCase				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	1000  # show more chars for failing integration tests
  
  
  
  		@classmethod
  		def 			lowerCAmelCase							(			cls							: int			)	-> None:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					List[str]     =	f'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
  				return cls
  
  
  
  		@cached_property
  		def 			lowerCAmelCase							(			self							: Tuple			)	-> MarianTokenizer:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				return AutoTokenizer.from_pretrained(self.model_name			)
  
  
  
  		@property
  		def 			lowerCAmelCase							(			self							: Optional[int]			)	-> int:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				return self.tokenizer.eos_token_id
  
  
  
  		@cached_property
  		def 			lowerCAmelCase							(			self							: Dict			)	-> Tuple:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				warnings.simplefilter('''error'''			)
  				snake_case     :					TFMarianMTModel     =	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name			)
  				assert isinstance(UpperCamelCase__     ,  UpperCamelCase__			)
  				snake_case     :					Optional[int]     =	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 			lowerCAmelCase							(			self							: Union[str, Any]     ,  **UpperCamelCase__							: int			)	-> int:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Optional[Any]     =	self.translate_src_text(**UpperCamelCase__			)
  				self.assertListEqual(self.expected_text     ,  UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict     ,  **UpperCamelCase__							: Tuple			)	-> Optional[int]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Any     =	self.tokenizer(self.src_text     ,  **UpperCamelCase__     ,  padding=UpperCamelCase__     ,  return_tensors='''tf'''			)
  				snake_case     :					Optional[int]     =	self.model.generate(
  				    model_inputs.input_ids     ,  attention_mask=model_inputs.attention_mask     ,  num_beams=2     ,  max_length=128			)
  				snake_case     :					Tuple     =	self.tokenizer.batch_decode(generated_ids.numpy()     ,  skip_special_tokens=UpperCamelCase__			)
  				return generated_words
  
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	"""mt"""
  		lowerCamelCase           =	"""en"""
  		lowerCamelCase           =	["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
  		lowerCamelCase           =	["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]
  
  
  
  		@unittest.skip('''Skipping until #12647 is resolved.'''			)
  		@slow
  		def 			lowerCAmelCase							(			self							: str			)	-> List[str]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	"""en"""
  		lowerCamelCase           =	"""zh"""
  		lowerCamelCase           =	["""My name is Wolfgang and I live in Berlin"""]
  		lowerCamelCase           =	["""我叫沃尔夫冈 我住在柏林"""]
  
  
  
  		@unittest.skip('''Skipping until #12647 is resolved.'''			)
  		@slow
  		def 			lowerCAmelCase							(			self							: int			)	-> List[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	"""en"""
  		lowerCamelCase           =	"""ROMANCE"""
  		lowerCamelCase           =	[
  		    """>>fr<< Don't spend so much time watching TV.""",
  		    """>>pt<< Your message has been sent.""",
  		    """>>es<< He's two years older than me.""",
  		]
  		lowerCamelCase           =	[
  		    """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 			lowerCAmelCase							(			self							: Tuple			)	-> int:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				self._assert_generated_batch_equal_expected()
  
  
  
  		@unittest.skip('''Skipping until #12647 is resolved.'''			)
  		@slow
  		def 			lowerCAmelCase							(			self							: Dict			)	-> Any:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Optional[Any]     =	TranslationPipeline(self.model     ,  self.tokenizer     ,  framework='''tf'''			)
  				snake_case     :					int     =	pipeline(self.src_text			)
  				self.assertEqual(self.expected_text     ,  [x['''translation_text'''] for x in output]			)
  
  
  
style_context: 
  '''simple docstring'''
  def       _UpperCamelCase	(					)     ->       int:
  
  
  		'''simple docstring'''
  
  
  		for a in range(300 ):
  				for b in range(a + 1   ,					400 ):
  						for c in range(b + 1   ,					500 ):
  								if (a + b + c) == 1000 and (a**2) + (b**2) == (c**2):
  										return a * b * c
  
  		return -1
  
  
  
  
  def       _UpperCamelCase	(					)     ->       int:
  
  
  		'''simple docstring'''
  
  
  		for a in range(300 ):
  				for b in range(400 ):
  						snake_case     :					Dict     =	1000 - a - b
  						if a < b < c and (a**2) + (b**2) == (c**2):
  								return a * b * c
  
  		return -1
  
  
  
  
  def       _UpperCamelCase	(					)     ->       None:
  
  
  		'''simple docstring'''
  
  
  		import timeit
  
  		print(
  		    timeit.timeit('''solution()'''   ,					setup='''from __main__ import solution'''   ,					number=1000 ) )
  		print(
  		    timeit.timeit(
  		        '''solution_fast()'''   ,					setup='''from __main__ import solution_fast'''   ,					number=1000 ) )
  
  
  if __name__ == "__main__":
  						print(f"{solution() = }")
  
  
  