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
   A_    :					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_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__=None       ,    UpperCAmelCase__=None       ,    UpperCAmelCase__=None       ,    UpperCAmelCase__=None       ,    UpperCAmelCase__=None       ,    UpperCAmelCase__=None       ,    ):
  
  
  
        """simple docstring"""
  
  
  
  
        if attention_mask is None:
              a_					=		np.where(input_ids != config.pad_token_id       ,    1       ,    0      )
        if decoder_attention_mask is None:
              a_					=		np.where(decoder_input_ids != config.pad_token_id       ,    1       ,    0      )
        if head_mask is None:
              a_					=		np.ones((config.encoder_layers, config.encoder_attention_heads)      )
        if decoder_head_mask is None:
              a_					=		np.ones((config.decoder_layers, config.decoder_attention_heads)      )
        if cross_attn_head_mask is None:
              a_					=		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        lowercase_				:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         def __init__(   self			,    _UpperCAmelCase			,    _UpperCAmelCase=13			,    _UpperCAmelCase=7			,    _UpperCAmelCase=True			,    _UpperCAmelCase=False			,    _UpperCAmelCase=99			,    _UpperCAmelCase=16			,    _UpperCAmelCase=2			,    _UpperCAmelCase=4			,    _UpperCAmelCase=4			,    _UpperCAmelCase="gelu"			,    _UpperCAmelCase=0.1			,    _UpperCAmelCase=0.1			,    _UpperCAmelCase=32			,    _UpperCAmelCase=2			,    _UpperCAmelCase=1			,    _UpperCAmelCase=0			,    _UpperCAmelCase=0.0_2			,    ):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_					=		parent
               a_					=		batch_size
               a_					=		seq_length
               a_					=		is_training
               a_					=		use_labels
               a_					=		vocab_size
               a_					=		hidden_size
               a_					=		num_hidden_layers
               a_					=		num_attention_heads
               a_					=		intermediate_size
               a_					=		hidden_act
               a_					=		hidden_dropout_prob
               a_					=		attention_probs_dropout_prob
               a_					=		max_position_embeddings
               a_					=		eos_token_id
               a_					=		pad_token_id
               a_					=		bos_token_id
               a_					=		initializer_range
  
  
         def 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_					=		np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,    self.vocab_size		)			,    3			,    self.vocab_size		)
               a_					=		np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,    dtype=np.intaa		))			,    -1		)
  
               a_					=		shift_tokens_right(__a			,    1			,    2		)
  
               a_					=		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_					=		prepare_marian_inputs_dict(__a			,    __a			,    __a		)
               return config, inputs_dict
  
  
         def 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_					=		self.prepare_config_and_inputs()
               return config, inputs_dict
  
  
         def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_					=		20
               a_					=		model_class_name(__a		)
  
               a_					=		model.encode(inputs_dict["""input_ids"""]		)
  
               a_					=		(
                   inputs_dict['decoder_input_ids'],
                   inputs_dict['decoder_attention_mask'],
               )
  
               a_					=		model.init_cache(decoder_input_ids.shape[0]			,    __a			,    __a		)
               a_					=		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			,    dtype="""i4"""		)
  
               a_					=		jnp.broadcast_to(
                   jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]			,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,    )
               a_					=		model.decode(
                   decoder_input_ids[:, :-1]			,    __a			,    decoder_attention_mask=__a			,    past_key_values=__a			,    decoder_position_ids=__a			,    )
  
               a_					=		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,    dtype="""i4"""		)
               a_					=		model.decode(
                   decoder_input_ids[:, -1:]			,    __a			,    decoder_attention_mask=__a			,    past_key_values=outputs_cache.past_key_values			,    decoder_position_ids=__a			,    )
  
               a_					=		model.decode(__a			,    __a		)
  
               a_					=		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 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_					=		20
               a_					=		model_class_name(__a		)
  
               a_					=		model.encode(inputs_dict["""input_ids"""]		)
  
               a_					=		(
                   inputs_dict['decoder_input_ids'],
                   inputs_dict['decoder_attention_mask'],
               )
  
               a_					=		jnp.concatenate(
                   [
                       decoder_attention_mask,
                       jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])		),
                   ]			,    axis=-1			,    )
  
               a_					=		model.init_cache(decoder_input_ids.shape[0]			,    __a			,    __a		)
               a_					=		jnp.broadcast_to(
                   jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]			,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,    )
  
               a_					=		model.decode(
                   decoder_input_ids[:, :-1]			,    __a			,    decoder_attention_mask=__a			,    past_key_values=__a			,    decoder_position_ids=__a			,    )
               a_					=		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,    dtype="""i4"""		)
               a_					=		model.decode(
                   decoder_input_ids[:, -1:]			,    __a			,    past_key_values=outputs_cache.past_key_values			,    decoder_attention_mask=__a			,    decoder_position_ids=__a			,    )
  
               a_					=		model.decode(__a			,    __a			,    decoder_attention_mask=__a		)
  
               a_					=		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        lowercase_				( lowerCAmelCase_			,unittest.TestCase			,lowerCAmelCase_):
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         snake_case_        =		True
         snake_case_        =		(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
         snake_case_        =		(FlaxMarianMTModel,) if is_flax_available() else ()
  
  
         def 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_					=		FlaxMarianModelTester(self		)
  
  
         def 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_					=		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 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_					=		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 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_					=		self.model_tester.prepare_config_and_inputs_for_common()
  
               for model_class in self.all_model_classes:
                     with self.subTest(model_class.__name__		):
                           a_					=		self._prepare_for_class(__a			,    __a		)
                           a_					=		model_class(__a		)
  
                           @jax.jit
                           def encode_jitted(_UpperCAmelCase			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):
                                 return model.encode(input_ids=__a			,    attention_mask=__a		)
  
                           with self.subTest("""JIT Enabled"""		):
                                 a_					=		encode_jitted(**__a		).to_tuple()
  
                           with self.subTest("""JIT Disabled"""		):
                                 with jax.disable_jit():
                                       a_					=		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 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_					=		self.model_tester.prepare_config_and_inputs_for_common()
  
               for model_class in self.all_model_classes:
                     with self.subTest(model_class.__name__		):
                           a_					=		model_class(__a		)
                           a_					=		model.encode(inputs_dict["""input_ids"""]			,    inputs_dict["""attention_mask"""]		)
  
                           a_					=		{
                               '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(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		):
                                 return model.decode(
                                     decoder_input_ids=__a			,    decoder_attention_mask=__a			,    encoder_outputs=__a			,    )
  
                           with self.subTest("""JIT Enabled"""		):
                                 a_					=		decode_jitted(**__a		).to_tuple()
  
                           with self.subTest("""JIT Disabled"""		):
                                 with jax.disable_jit():
                                       a_					=		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 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               for model_class_name in self.all_model_classes:
                     a_					=		model_class_name.from_pretrained("""Helsinki-NLP/opus-mt-en-de"""		)
                     # FlaxMarianForSequenceClassification expects eos token in input_ids
                     a_					=		np.ones((1, 1)		) * model.config.eos_token_id
                     a_					=		model(__a		)
                     self.assertIsNotNone(__a		)
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class        lowercase_				( unittest.TestCase):
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         snake_case_        =		None
         snake_case_        =		None
  
  
         @classmethod
         def 						lowercase__     (   cls		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_					=		f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
               return cls
  
  
         @cached_property
         def 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               return MarianTokenizer.from_pretrained(self.model_name		)
  
  
         @property
         def 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               return self.tokenizer.eos_token_id
  
  
         @cached_property
         def 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_					=		FlaxMarianMTModel.from_pretrained(self.model_name		)
               self.assertEqual(model.config.decoder_start_token_id			,    model.config.pad_token_id		)
               return model
  
  
         def 						lowercase__     (   self			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_					=		self.translate_src_text(**__a		)
               self.assertListEqual(self.expected_text			,    __a		)
  
  
  
  
  
  
         def 						lowercase__     (   self			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               a_					=		self.tokenizer(self.src_text			,    padding=__a			,    return_tensors="""np"""			,    **__a		)
               a_					=		self.model.generate(
                   model_inputs.input_ids			,    attention_mask=model_inputs.attention_mask			,    num_beams=2			,    max_length=128			,    ).sequences
               a_					=		self.tokenizer.batch_decode(__a			,    skip_special_tokens=__a		)
               return generated_words
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class        lowercase_				( lowerCAmelCase_):
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         snake_case_        =		'''en'''
         snake_case_        =		'''fr'''
         snake_case_        =		[
             '''I am a small frog.''',
             '''Now I can forget the 100 words of german that I know.''',
         ]
         snake_case_        =		[
             '''Je suis une petite grenouille.''',
             '''Maintenant, je peux oublier les 100 mots d\'allemand que je connais.''',
         ]
  
  
         @slow
         def 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               self._assert_generated_batch_equal_expected()
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class        lowercase_				( lowerCAmelCase_):
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         snake_case_        =		'''fr'''
         snake_case_        =		'''en'''
         snake_case_        =		[
             '''Donnez moi le micro.''',
             '''Tom et Mary étaient assis à une table.''',  # Accents
         ]
         snake_case_        =		[
             '''Give me the microphone.''',
             '''Tom and Mary were sitting at a table.''',
         ]
  
  
         @slow
         def 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               self._assert_generated_batch_equal_expected()
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class        lowercase_				( lowerCAmelCase_):
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         snake_case_        =		'''mt'''
         snake_case_        =		'''en'''
         snake_case_        =		['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
         snake_case_        =		['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']
  
  
         @slow
         def 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               self._assert_generated_batch_equal_expected()
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class        lowercase_				( lowerCAmelCase_):
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         snake_case_        =		'''en'''
         snake_case_        =		'''de'''
         snake_case_        =		[
             '''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.''',
         ]
         snake_case_        =		[
             '''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 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               self._assert_generated_batch_equal_expected()
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class        lowercase_				( lowerCAmelCase_):
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         snake_case_        =		'''en'''
         snake_case_        =		'''zh'''
         snake_case_        =		['''My name is Wolfgang and I live in Berlin''']
         snake_case_        =		['''我叫沃尔夫冈 我住在柏林''']
  
  
         @slow
         def 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               self._assert_generated_batch_equal_expected()
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class        lowercase_				( lowerCAmelCase_):
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         snake_case_        =		'''ru'''
         snake_case_        =		'''fr'''
         snake_case_        =		['''Он показал мне рукопись своей новой пьесы.''']
         snake_case_        =		['''Il m\'a montré le manuscrit de sa nouvelle pièce.''']
  
  
         @slow
         def 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               self._assert_generated_batch_equal_expected()
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class        lowercase_				( lowerCAmelCase_):
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         snake_case_        =		'''en'''
         snake_case_        =		'''ROMANCE'''
         snake_case_        =		[
             '''>>fr<< Don\'t spend so much time watching TV.''',
             '''>>pt<< Your message has been sent.''',
             '''>>es<< He\'s two years older than me.''',
         ]
         snake_case_        =		[
             '''Ne passez pas autant de temps à regarder la télé.''',
             '''A sua mensagem foi enviada.''',
             '''Es dos años más viejo que yo.''',
         ]
  
  
         @slow
         def 						lowercase__     (   self		):
  
  
  
  
  
  
  
               """simple docstring"""
  
  
               self._assert_generated_batch_equal_expected()
style_context: 
  
  
  from collections.abc import Callable
  
  A_    :					Any             =Callable[[float], float]  # type alias for a real -> real function
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__       ,    ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		float(UpperCAmelCase__      )
  						while True:
  												try:
  																		a_					=		prev_guess - function(UpperCAmelCase__      ) / derivative(UpperCAmelCase__      )
  												except ZeroDivisionError:
  																		raise ZeroDivisionError("""Could not find root"""      ) from None
  												if abs(prev_guess - next_guess      ) < 10**-5:
  																		return next_guess
  												a_					=		next_guess
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						return (x**3) - (2 * x) - 5
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						return 3 * (x**2) - 2
  
  
  if __name__ == "__main__":
  	print(newton(f, fa, 3))