code: 
  
  
  
  
  
  import unittest
  
  import numpy as np
  import timeout_decorator  # noqa
  
  from transformers import MarianConfig, 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
      __lowerCAmelCase     =			"platform"
  
      import jax
      import jax.numpy as jnp
  
      from transformers import MarianTokenizer
      from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right
  
  def     a						(      _lowercase   :     Optional[int] ,						_lowercase   :     List[str] ,						_lowercase   :     Optional[Any]=None ,						_lowercase   :     str=None ,						_lowercase   :     Union[str, Any]=None ,						_lowercase   :     List[str]=None ,						_lowercase   :     Optional[int]=None ,						_lowercase   :     int=None ,						)     ->Optional[int]:
    if attention_mask is None:
      UpperCamelCase__         =	np.where(input_ids != config.pad_token_id ,						1 ,						0       )
    if decoder_attention_mask is None:
      UpperCamelCase__         =	np.where(decoder_input_ids != config.pad_token_id ,						1 ,						0       )
    if head_mask is None:
      UpperCamelCase__         =	np.ones((config.encoder_layers, config.encoder_attention_heads)       )
    if decoder_head_mask is None:
      UpperCamelCase__         =	np.ones((config.decoder_layers, config.decoder_attention_heads)       )
    if cross_attn_head_mask is None:
      UpperCamelCase__         =	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": attention_mask,
    }
  
  
  
  
  class   _UpperCAmelCase	:
  
  
  
  
       def __init__(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase=13    ,      __lowerCAmelCase=7    ,      __lowerCAmelCase=True    ,      __lowerCAmelCase=False    ,      __lowerCAmelCase=99    ,      __lowerCAmelCase=16    ,      __lowerCAmelCase=2    ,      __lowerCAmelCase=4    ,      __lowerCAmelCase=4    ,      __lowerCAmelCase="gelu"    ,      __lowerCAmelCase=0.1    ,      __lowerCAmelCase=0.1    ,      __lowerCAmelCase=32    ,      __lowerCAmelCase=2    ,      __lowerCAmelCase=1    ,      __lowerCAmelCase=0    ,      __lowerCAmelCase=0.0_2    ,      )						->  str:
         UpperCamelCase__         =	parent
         UpperCamelCase__         =	batch_size
         UpperCamelCase__         =	seq_length
         UpperCamelCase__         =	is_training
         UpperCamelCase__         =	use_labels
         UpperCamelCase__         =	vocab_size
         UpperCamelCase__         =	hidden_size
         UpperCamelCase__         =	num_hidden_layers
         UpperCamelCase__         =	num_attention_heads
         UpperCamelCase__         =	intermediate_size
         UpperCamelCase__         =	hidden_act
         UpperCamelCase__         =	hidden_dropout_prob
         UpperCamelCase__         =	attention_probs_dropout_prob
         UpperCamelCase__         =	max_position_embeddings
         UpperCamelCase__         =	eos_token_id
         UpperCamelCase__         =	pad_token_id
         UpperCamelCase__         =	bos_token_id
         UpperCamelCase__         =	initializer_range
  
  
  
  
       def 	snake_case_		(						self       )						->  List[Any]:
         UpperCamelCase__         =	np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,      self.vocab_size       )    ,      3    ,      self.vocab_size       )
         UpperCamelCase__         =	np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,      dtype=np.intaa       ))    ,      -1       )
  
         UpperCamelCase__         =	shift_tokens_right(__lowerCAmelCase    ,      1    ,      2       )
  
         UpperCamelCase__         =	MarianConfig(
             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    ,      initializer_range=self.initializer_range    ,      use_cache=__lowerCAmelCase    ,      )
         UpperCamelCase__         =	prepare_marian_inputs_dict(__lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )
         return config, inputs_dict
  
  
  
  
       def 	snake_case_		(						self       )						->  Dict:
         UpperCamelCase__  ,			UpperCamelCase__         =	self.prepare_config_and_inputs()
         return config, inputs_dict
  
  
  
  
       def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  Any:
         UpperCamelCase__         =	20
         UpperCamelCase__         =	model_class_name(__lowerCAmelCase       )
  
         UpperCamelCase__         =	model.encode(inputs_dict["""input_ids"""]       )
  
         UpperCamelCase__  ,			UpperCamelCase__         =	(
             inputs_dict["""decoder_input_ids"""],
             inputs_dict["""decoder_attention_mask"""],
         )
  
         UpperCamelCase__         =	model.init_cache(decoder_input_ids.shape[0]    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )
         UpperCamelCase__         =	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,      dtype="""i4"""       )
  
         UpperCamelCase__         =	jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :]    ,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,      )
         UpperCamelCase__         =	model.decode(
             decoder_input_ids[:, :-1]    ,      __lowerCAmelCase    ,      decoder_attention_mask=__lowerCAmelCase    ,      past_key_values=__lowerCAmelCase    ,      decoder_position_ids=__lowerCAmelCase    ,      )
  
         UpperCamelCase__         =	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,      dtype="""i4"""       )
         UpperCamelCase__         =	model.decode(
             decoder_input_ids[:, -1:]    ,      __lowerCAmelCase    ,      decoder_attention_mask=__lowerCAmelCase    ,      past_key_values=outputs_cache.past_key_values    ,      decoder_position_ids=__lowerCAmelCase    ,      )
  
         UpperCamelCase__         =	model.decode(__lowerCAmelCase    ,      __lowerCAmelCase       )
  
         UpperCamelCase__         =	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 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  Optional[Any]:
         UpperCamelCase__         =	20
         UpperCamelCase__         =	model_class_name(__lowerCAmelCase       )
  
         UpperCamelCase__         =	model.encode(inputs_dict["""input_ids"""]       )
  
         UpperCamelCase__  ,			UpperCamelCase__         =	(
             inputs_dict["""decoder_input_ids"""],
             inputs_dict["""decoder_attention_mask"""],
         )
  
         UpperCamelCase__         =	jnp.concatenate(
             [
                 decoder_attention_mask,
                 jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])       ),
             ]    ,      axis=-1    ,      )
  
         UpperCamelCase__         =	model.init_cache(decoder_input_ids.shape[0]    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )
         UpperCamelCase__         =	jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :]    ,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,      )
  
         UpperCamelCase__         =	model.decode(
             decoder_input_ids[:, :-1]    ,      __lowerCAmelCase    ,      decoder_attention_mask=__lowerCAmelCase    ,      past_key_values=__lowerCAmelCase    ,      decoder_position_ids=__lowerCAmelCase    ,      )
         UpperCamelCase__         =	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,      dtype="""i4"""       )
         UpperCamelCase__         =	model.decode(
             decoder_input_ids[:, -1:]    ,      __lowerCAmelCase    ,      past_key_values=outputs_cache.past_key_values    ,      decoder_attention_mask=__lowerCAmelCase    ,      decoder_position_ids=__lowerCAmelCase    ,      )
  
         UpperCamelCase__         =	model.decode(__lowerCAmelCase    ,      __lowerCAmelCase    ,      decoder_attention_mask=__lowerCAmelCase       )
  
         UpperCamelCase__         =	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	(   _lowercase ,       unittest.TestCase ,       _lowercase					):
       __A          =   True
       __A          =   (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
       __A          =   (FlaxMarianMTModel,) if is_flax_available() else ()
  
  
  
  
       def 	snake_case_		(						self       )						->  List[str]:
         UpperCamelCase__         =	FlaxMarianModelTester(self       )
  
  
  
  
       def 	snake_case_		(						self       )						->  Optional[int]:
         UpperCamelCase__  ,			UpperCamelCase__         =	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 	snake_case_		(						self       )						->  Union[str, Any]:
         UpperCamelCase__  ,			UpperCamelCase__         =	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 	snake_case_		(						self       )						->  Tuple:
         UpperCamelCase__  ,			UpperCamelCase__         =	self.model_tester.prepare_config_and_inputs_for_common()
  
         for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__       ):
             UpperCamelCase__         =	self._prepare_for_class(__lowerCAmelCase    ,      __lowerCAmelCase       )
             UpperCamelCase__         =	model_class(__lowerCAmelCase       )
  
             @jax.jit
             def encode_jitted(__lowerCAmelCase    ,      __lowerCAmelCase=None    ,      **__lowerCAmelCase       ):
               return model.encode(input_ids=__lowerCAmelCase    ,      attention_mask=__lowerCAmelCase       )
  
             with self.subTest("""JIT Enabled"""       ):
               UpperCamelCase__         =	encode_jitted(**__lowerCAmelCase       ).to_tuple()
  
             with self.subTest("""JIT Disabled"""       ):
               with jax.disable_jit():
                 UpperCamelCase__         =	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 	snake_case_		(						self       )						->  List[Any]:
         UpperCamelCase__  ,			UpperCamelCase__         =	self.model_tester.prepare_config_and_inputs_for_common()
  
         for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__       ):
             UpperCamelCase__         =	model_class(__lowerCAmelCase       )
             UpperCamelCase__         =	model.encode(inputs_dict["""input_ids"""]    ,      inputs_dict["""attention_mask"""]       )
  
             UpperCamelCase__         =	{
                 """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    ,      __lowerCAmelCase    ,      __lowerCAmelCase       ):
               return model.decode(
                   decoder_input_ids=__lowerCAmelCase    ,      decoder_attention_mask=__lowerCAmelCase    ,      encoder_outputs=__lowerCAmelCase    ,      )
  
             with self.subTest("""JIT Enabled"""       ):
               UpperCamelCase__         =	decode_jitted(**__lowerCAmelCase       ).to_tuple()
  
             with self.subTest("""JIT Disabled"""       ):
               with jax.disable_jit():
                 UpperCamelCase__         =	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 	snake_case_		(						self       )						->  str:
         for model_class_name in self.all_model_classes:
           UpperCamelCase__         =	model_class_name.from_pretrained("""Helsinki-NLP/opus-mt-en-de"""       )
           # FlaxMarianForSequenceClassification expects eos token in input_ids
           UpperCamelCase__         =	np.ones((1, 1)       ) * model.config.eos_token_id
           UpperCamelCase__         =	model(__lowerCAmelCase       )
           self.assertIsNotNone(__lowerCAmelCase       )
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class   _UpperCAmelCase	(   unittest.TestCase					):
       __A          =   None
       __A          =   None
  
  
  
  
       @classmethod
       def 	snake_case_		(						cls       )						->  None:
         UpperCamelCase__         =	F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
         return cls
  
  
  
  
       @cached_property
       def 	snake_case_		(						self       )						->  int:
         return MarianTokenizer.from_pretrained(self.model_name       )
  
  
  
  
       @property
       def 	snake_case_		(						self       )						->  int:
         return self.tokenizer.eos_token_id
  
  
  
  
       @cached_property
       def 	snake_case_		(						self       )						->  List[Any]:
         UpperCamelCase__         =	FlaxMarianMTModel.from_pretrained(self.model_name       )
         self.assertEqual(model.config.decoder_start_token_id    ,      model.config.pad_token_id       )
         return model
  
  
  
  
       def 	snake_case_		(						self    ,      **__lowerCAmelCase       )						->  Dict:
         UpperCamelCase__         =	self.translate_src_text(**__lowerCAmelCase       )
         self.assertListEqual(self.expected_text    ,      __lowerCAmelCase       )
  
  
  
  
       def 	snake_case_		(						self    ,      **__lowerCAmelCase       )						->  str:
         UpperCamelCase__         =	self.tokenizer(self.src_text    ,      padding=__lowerCAmelCase    ,      return_tensors="""np"""    ,      **__lowerCAmelCase       )
         UpperCamelCase__         =	self.model.generate(
             model_inputs.input_ids    ,      attention_mask=model_inputs.attention_mask    ,      num_beams=2    ,      max_length=128    ,      ).sequences
         UpperCamelCase__         =	self.tokenizer.batch_decode(__lowerCAmelCase    ,      skip_special_tokens=__lowerCAmelCase       )
         return generated_words
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class   _UpperCAmelCase	(   _lowercase					):
       __A          =   "en"
       __A          =   "fr"
       __A          =   [
           "I am a small frog.",
           "Now I can forget the 100 words of german that I know.",
       ]
       __A          =   [
           "Je suis une petite grenouille.",
           "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
       ]
  
  
  
  
       @slow
       def 	snake_case_		(						self       )						->  Union[str, Any]:
         self._assert_generated_batch_equal_expected()
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class   _UpperCAmelCase	(   _lowercase					):
       __A          =   "fr"
       __A          =   "en"
       __A          =   [
           "Donnez moi le micro.",
           "Tom et Mary étaient assis à une table.",  # Accents
       ]
       __A          =   [
           "Give me the microphone.",
           "Tom and Mary were sitting at a table.",
       ]
  
  
  
  
       @slow
       def 	snake_case_		(						self       )						->  str:
         self._assert_generated_batch_equal_expected()
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class   _UpperCAmelCase	(   _lowercase					):
       __A          =   "mt"
       __A          =   "en"
       __A          =   ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
       __A          =   ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]
  
  
  
  
       @slow
       def 	snake_case_		(						self       )						->  Any:
         self._assert_generated_batch_equal_expected()
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class   _UpperCAmelCase	(   _lowercase					):
       __A          =   "en"
       __A          =   "de"
       __A          =   [
           "I am a small frog.",
           "Now I can forget the 100 words of german that I know.",
           "Tom asked his teacher for advice.",
           "That's how I would do it.",
           "Tom really admired Mary's courage.",
           "Turn around and close your eyes.",
       ]
       __A          =   [
           "Ich bin ein kleiner Frosch.",
           "Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
           "Tom bat seinen Lehrer um Rat.",
           "So würde ich das machen.",
           "Tom bewunderte Marias Mut wirklich.",
           "Drehen Sie sich um und schließen Sie die Augen.",
       ]
  
  
  
  
       @slow
       def 	snake_case_		(						self       )						->  Union[str, Any]:
         self._assert_generated_batch_equal_expected()
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class   _UpperCAmelCase	(   _lowercase					):
       __A          =   "en"
       __A          =   "zh"
       __A          =   ["My name is Wolfgang and I live in Berlin"]
       __A          =   ["我叫沃尔夫冈 我住在柏林"]
  
  
  
  
       @slow
       def 	snake_case_		(						self       )						->  List[str]:
         self._assert_generated_batch_equal_expected()
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class   _UpperCAmelCase	(   _lowercase					):
       __A          =   "ru"
       __A          =   "fr"
       __A          =   ["Он показал мне рукопись своей новой пьесы."]
       __A          =   ["Il m'a montré le manuscrit de sa nouvelle pièce."]
  
  
  
  
       @slow
       def 	snake_case_		(						self       )						->  Optional[Any]:
         self._assert_generated_batch_equal_expected()
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class   _UpperCAmelCase	(   _lowercase					):
       __A          =   "en"
       __A          =   "ROMANCE"
       __A          =   [
           ">>fr<< Don't spend so much time watching TV.",
           ">>pt<< Your message has been sent.",
           ">>es<< He's two years older than me.",
       ]
       __A          =   [
           "Ne passez pas autant de temps à regarder la télé.",
           "A sua mensagem foi enviada.",
           "Es dos años más viejo que yo.",
       ]
  
  
  
  
       @slow
       def 	snake_case_		(						self       )						->  Optional[Any]:
         self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  __lowerCAmelCase     =			{
      "0": 1,
      "1": 1,
      "2": 2,
      "3": 6,
      "4": 24,
      "5": 120,
      "6": 720,
      "7": 5_040,
      "8": 40_320,
      "9": 362_880,
  }
  
  __lowerCAmelCase     =			{145: 145}
  
  __lowerCAmelCase     =			{
      145: 0,
      169: 3,
      36_301: 3,
      1_454: 3,
      871: 2,
      45_361: 2,
      872: 2,
  }
  
  def     a						(      _lowercase   :     int       )     ->int:
  		if n in CACHE_SUM_DIGIT_FACTORIALS:
  				return CACHE_SUM_DIGIT_FACTORIALS[n]
  		UpperCamelCase__         =	sum(DIGIT_FACTORIALS[let] for let in str(_lowercase       )       )
  		UpperCamelCase__         =	ret
  		return ret
  
  def     a						(      _lowercase   :     int ,						_lowercase   :     set | None = None       )     ->int:
  		UpperCamelCase__         =	previous or set()
  		if n in CHAIN_LENGTH_CACHE:
  				return CHAIN_LENGTH_CACHE[n]
  		UpperCamelCase__         =	sum_digit_factorials(_lowercase       )
  		if next_number in previous:
  				UpperCamelCase__         =	0
  				return 0
  		else:
  				previous.add(_lowercase       )
  				UpperCamelCase__         =	1 + chain_length(_lowercase ,						_lowercase       )
  				UpperCamelCase__         =	ret
  				return ret
  
  def     a						(      _lowercase   :     int = 6_0 ,						_lowercase   :     int = 1_0_0_0_0_0_0       )     ->int:
  		return sum(1 for i in range(1 ,						_lowercase       ) if chain_length(_lowercase       ) == num_terms       )
  
  
  if __name__ == "__main__":
  				print(f"""{solution() = }""")
  
  
  
  
  
  