code: 
  
  
  '''simple docstring'''
  
  
  
  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
  	__lowercase			:		Dict =			'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 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Tuple		,   _SCREAMING_SNAKE_CASE   :     Any		,   _SCREAMING_SNAKE_CASE   :     int=None		,   _SCREAMING_SNAKE_CASE   :     int=None		,   _SCREAMING_SNAKE_CASE   :     Dict=None		,   _SCREAMING_SNAKE_CASE   :     int=None		,   _SCREAMING_SNAKE_CASE   :     Tuple=None		,   _SCREAMING_SNAKE_CASE   :     Dict=None		,   ):
  					if attention_mask is None:
  										__a    : str      					=						np.where(input_ids != config.pad_token_id		,   1		,   0     )
  					if decoder_attention_mask is None:
  										__a    : str      					=						np.where(decoder_input_ids != config.pad_token_id		,   1		,   0     )
  					if head_mask is None:
  										__a    : Optional[Any]      					=						np.ones((config.encoder_layers, config.encoder_attention_heads)     )
  					if decoder_head_mask is None:
  										__a    : List[str]      					=						np.ones((config.decoder_layers, config.decoder_attention_heads)     )
  					if cross_attn_head_mask is None:
  										__a    : Any      					=						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		,       __a		,       __a=13		,       __a=7		,       __a=True		,       __a=False		,       __a=99		,       __a=16		,       __a=2		,       __a=4		,       __a=4		,       __a="gelu"		,       __a=0.1		,       __a=0.1		,       __a=32		,       __a=2		,       __a=1		,       __a=0		,       __a=0.02		,       ):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : List[Any]      					=						parent
  						__a    : List[str]      					=						batch_size
  						__a    : List[Any]      					=						seq_length
  						__a    : List[str]      					=						is_training
  						__a    : Union[str, Any]      					=						use_labels
  						__a    : Optional[Any]      					=						vocab_size
  						__a    : str      					=						hidden_size
  						__a    : int      					=						num_hidden_layers
  						__a    : Any      					=						num_attention_heads
  						__a    : List[Any]      					=						intermediate_size
  						__a    : Optional[Any]      					=						hidden_act
  						__a    : int      					=						hidden_dropout_prob
  						__a    : Tuple      					=						attention_probs_dropout_prob
  						__a    : int      					=						max_position_embeddings
  						__a    : List[str]      					=						eos_token_id
  						__a    : Optional[int]      					=						pad_token_id
  						__a    : int      					=						bos_token_id
  						__a    : Dict      					=						initializer_range
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : str      					=						np.clip(ids_tensor([self.batch_size, self.seq_length - 1]		,       self.vocab_size							)		,       3		,       self.vocab_size							)
  						__a    : Tuple      					=						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)		,       dtype=np.intaa							))		,       -1							)
  
  						__a    : List[Any]      					=						shift_tokens_right(__a		,       1		,       2							)
  
  						__a    : List[str]      					=						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=__a		,       )
  						__a    : Union[str, Any]      					=						prepare_marian_inputs_dict(__a		,       __a		,       __a							)
  						return config, inputs_dict
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : List[Any]      					=						self.prepare_config_and_inputs()
  						return config, inputs_dict
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : List[str]      					=						20
  						__a    : Any      					=						model_class_name(__a							)
  
  						__a    : Any      					=						model.encode(inputs_dict['input_ids']							)
  
  						__a					,       __a    : Dict      					=						(
  						    inputs_dict['decoder_input_ids'],
  						    inputs_dict['decoder_attention_mask'],
  						)
  
  						__a    : List[str]      					=						model.init_cache(decoder_input_ids.shape[0]		,       __a		,       __a							)
  						__a    : Tuple      					=						jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,       dtype='i4'							)
  
  						__a    : Dict      					=						jnp.broadcast_to(
  						    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]		,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,       )
  						__a    : Optional[Any]      					=						model.decode(
  						    decoder_input_ids[:, :-1]		,       __a		,       decoder_attention_mask=__a		,       past_key_values=__a		,       decoder_position_ids=__a		,       )
  
  						__a    : Dict      					=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,       dtype='i4'							)
  						__a    : Union[str, Any]      					=						model.decode(
  						    decoder_input_ids[:, -1:]		,       __a		,       decoder_attention_mask=__a		,       past_key_values=outputs_cache.past_key_values		,       decoder_position_ids=__a		,       )
  
  						__a    : Tuple      					=						model.decode(__a		,       __a							)
  
  						__a    : List[Any]      					=						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		,       __a		,       __a		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : str      					=						20
  						__a    : Dict      					=						model_class_name(__a							)
  
  						__a    : int      					=						model.encode(inputs_dict['input_ids']							)
  
  						__a					,       __a    : Dict      					=						(
  						    inputs_dict['decoder_input_ids'],
  						    inputs_dict['decoder_attention_mask'],
  						)
  
  						__a    : Tuple      					=						jnp.concatenate(
  						    [
  						        decoder_attention_mask,
  						        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])							),
  						    ]		,       axis=-1		,       )
  
  						__a    : Tuple      					=						model.init_cache(decoder_input_ids.shape[0]		,       __a		,       __a							)
  						__a    : Dict      					=						jnp.broadcast_to(
  						    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]		,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,       )
  
  						__a    : List[Any]      					=						model.decode(
  						    decoder_input_ids[:, :-1]		,       __a		,       decoder_attention_mask=__a		,       past_key_values=__a		,       decoder_position_ids=__a		,       )
  						__a    : Optional[int]      					=						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,       dtype='i4'							)
  						__a    : Tuple      					=						model.decode(
  						    decoder_input_ids[:, -1:]		,       __a		,       past_key_values=outputs_cache.past_key_values		,       decoder_attention_mask=__a		,       decoder_position_ids=__a		,       )
  
  						__a    : Tuple      					=						model.decode(__a		,       __a		,       decoder_attention_mask=__a							)
  
  						__a    : List[str]      					=						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       (	lowerCAmelCase_	,   unittest.TestCase	,   lowerCAmelCase_      ):
  	A_			    =					True
  	A_			    =					(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
  	A_			    =					(FlaxMarianMTModel,) if is_flax_available() else ()
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Any      					=						FlaxMarianModelTester(self							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : Any      					=						self.model_tester.prepare_config_and_inputs()
  						for model_class in self.all_model_classes:
  											self.model_tester.check_use_cache_forward(__a		,       __a		,       __a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : Any      					=						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(__a		,       __a		,       __a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : str      					=						self.model_tester.prepare_config_and_inputs_for_common()
  
  						for model_class in self.all_model_classes:
  											with self.subTest(model_class.__name__							):
  																__a    : List[str]      					=						self._prepare_for_class(__a		,       __a							)
  																__a    : Optional[int]      					=						model_class(__a							)
  
  																@jax.jit
  																def encode_jitted(__a		,       __a=None		,       **__a							):
  																					return model.encode(input_ids=__a		,       attention_mask=__a							)
  
  																with self.subTest('JIT Enabled'							):
  																					__a    : List[Any]      					=						encode_jitted(**__a							).to_tuple()
  
  																with self.subTest('JIT Disabled'							):
  																					with jax.disable_jit():
  																										__a    : Any      					=						encode_jitted(**__a							).to_tuple()
  
  																self.assertEqual(len(__a							)		,       len(__a							)							)
  																for jitted_output, output in zip(__a		,       __a							):
  																					self.assertEqual(jitted_output.shape		,       output.shape							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a					,       __a    : Any      					=						self.model_tester.prepare_config_and_inputs_for_common()
  
  						for model_class in self.all_model_classes:
  											with self.subTest(model_class.__name__							):
  																__a    : str      					=						model_class(__a							)
  																__a    : Tuple      					=						model.encode(inputs_dict['input_ids']		,       inputs_dict['attention_mask']							)
  
  																__a    : Dict      					=						{
  																    '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(__a		,       __a		,       __a							):
  																					return model.decode(
  																					    decoder_input_ids=__a		,       decoder_attention_mask=__a		,       encoder_outputs=__a		,       )
  
  																with self.subTest('JIT Enabled'							):
  																					__a    : Optional[Any]      					=						decode_jitted(**__a							).to_tuple()
  
  																with self.subTest('JIT Disabled'							):
  																					with jax.disable_jit():
  																										__a    : Tuple      					=						decode_jitted(**__a							).to_tuple()
  
  																self.assertEqual(len(__a							)		,       len(__a							)							)
  																for jitted_output, output in zip(__a		,       __a							):
  																					self.assertEqual(jitted_output.shape		,       output.shape							)
  
  
  
  
  
  	@slow
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						for model_class_name in self.all_model_classes:
  											__a    : Any      					=						model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de'							)
  											# FlaxMarianForSequenceClassification expects eos token in input_ids
  											__a    : Optional[Any]      					=						np.ones((1, 1)							) * model.config.eos_token_id
  											__a    : int      					=						model(__a							)
  											self.assertIsNotNone(__a							)
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class 							__UpperCamelCase       (	unittest.TestCase      ):
  	A_			    =					None
  	A_			    =					None
  
  
  
  
  	@classmethod
  	def    __UpperCAmelCase							(     cls							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : List[Any]      					=						f"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
  						return cls
  
  
  
  
  	@cached_property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return MarianTokenizer.from_pretrained(self.model_name							)
  
  
  
  
  	@property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return self.tokenizer.eos_token_id
  
  
  
  
  	@cached_property
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : FlaxMarianMTModel      					=						FlaxMarianMTModel.from_pretrained(self.model_name							)
  						self.assertEqual(model.config.decoder_start_token_id		,       model.config.pad_token_id							)
  						return model
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       **__a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : int      					=						self.translate_src_text(**__a							)
  						self.assertListEqual(self.expected_text		,       __a							)
  
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       **__a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : List[str]      					=						self.tokenizer(self.src_text		,       padding=__a		,       return_tensors='np'		,       **__a							)
  						__a    : int      					=						self.model.generate(
  						    model_inputs.input_ids		,       attention_mask=model_inputs.attention_mask		,       num_beams=2		,       max_length=128		,       ).sequences
  						__a    : List[Any]      					=						self.tokenizer.batch_decode(__a		,       skip_special_tokens=__a							)
  						return generated_words
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  	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    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						self._assert_generated_batch_equal_expected()
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  	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    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						self._assert_generated_batch_equal_expected()
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  	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    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						self._assert_generated_batch_equal_expected()
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  	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    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						self._assert_generated_batch_equal_expected()
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  	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    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						self._assert_generated_batch_equal_expected()
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  	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    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						self._assert_generated_batch_equal_expected()
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  	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    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						self._assert_generated_batch_equal_expected()
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     int = 30     ):
  					__a    : Dict      					=						exponent + 2
  					__a    : List[Any]      					=						(1 + 5**0.5) / 2
  					__a    : Any      					=						(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5
  
  					return int(_SCREAMING_SNAKE_CASE     )
  
  
  if __name__ == "__main__":
  	print(f'''{solution() = }''')
  
  
  
  