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			:							Any       ='''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 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase=None      ,					__lowerCAmelCase=None      ,					__lowerCAmelCase=None      ,					__lowerCAmelCase=None      ,					__lowerCAmelCase=None      ,					__lowerCAmelCase=None      ,					)				->			Tuple:
       if attention_mask is None:
            UpperCamelCase__    :     List[Any]        =						np.where(input_ids != config.pad_token_id      ,					1      ,					0    )
       if decoder_attention_mask is None:
            UpperCamelCase__    :     List[Any]        =						np.where(decoder_input_ids != config.pad_token_id      ,					1      ,					0    )
       if head_mask is None:
            UpperCamelCase__    :     Tuple        =						np.ones((config.encoder_layers, config.encoder_attention_heads)    )
       if decoder_head_mask is None:
            UpperCamelCase__    :     str        =						np.ones((config.decoder_layers, config.decoder_attention_heads)    )
       if cross_attn_head_mask is None:
            UpperCamelCase__    :     List[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     __a						:
  
  
  
  
  
  
  
   def __init__(	self      :     int							,     SCREAMING_SNAKE_CASE      :     Optional[Any]							,     SCREAMING_SNAKE_CASE      :     Tuple=13							,     SCREAMING_SNAKE_CASE      :     Tuple=7							,     SCREAMING_SNAKE_CASE      :     Union[str, Any]=True							,     SCREAMING_SNAKE_CASE      :     List[Any]=False							,     SCREAMING_SNAKE_CASE      :     Dict=99							,     SCREAMING_SNAKE_CASE      :     Union[str, Any]=16							,     SCREAMING_SNAKE_CASE      :     str=2							,     SCREAMING_SNAKE_CASE      :     Optional[Any]=4							,     SCREAMING_SNAKE_CASE      :     str=4							,     SCREAMING_SNAKE_CASE      :     Optional[int]="gelu"							,     SCREAMING_SNAKE_CASE      :     Optional[Any]=0.1							,     SCREAMING_SNAKE_CASE      :     Union[str, Any]=0.1							,     SCREAMING_SNAKE_CASE      :     Dict=32							,     SCREAMING_SNAKE_CASE      :     Any=2							,     SCREAMING_SNAKE_CASE      :     Union[str, Any]=1							,     SCREAMING_SNAKE_CASE      :     Optional[Any]=0							,     SCREAMING_SNAKE_CASE      :     Tuple=0.0_2							,     ):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     str        =						parent
        UpperCamelCase__    :     str        =						batch_size
        UpperCamelCase__    :     Any        =						seq_length
        UpperCamelCase__    :     int        =						is_training
        UpperCamelCase__    :     Union[str, Any]        =						use_labels
        UpperCamelCase__    :     Optional[int]        =						vocab_size
        UpperCamelCase__    :     Optional[Any]        =						hidden_size
        UpperCamelCase__    :     Tuple        =						num_hidden_layers
        UpperCamelCase__    :     Optional[int]        =						num_attention_heads
        UpperCamelCase__    :     Optional[Any]        =						intermediate_size
        UpperCamelCase__    :     int        =						hidden_act
        UpperCamelCase__    :     int        =						hidden_dropout_prob
        UpperCamelCase__    :     List[Any]        =						attention_probs_dropout_prob
        UpperCamelCase__    :     Union[str, Any]        =						max_position_embeddings
        UpperCamelCase__    :     Optional[Any]        =						eos_token_id
        UpperCamelCase__    :     Optional[int]        =						pad_token_id
        UpperCamelCase__    :     Dict        =						bos_token_id
        UpperCamelCase__    :     List[Any]        =						initializer_range
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     List[str]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Tuple        =						np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,     self.vocab_size		)							,     3							,     self.vocab_size		)
        UpperCamelCase__    :     List[Any]        =						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,     dtype=np.intaa		))							,     -1		)
  
        UpperCamelCase__    :     List[str]        =						shift_tokens_right(SCREAMING_SNAKE_CASE							,     1							,     2		)
  
        UpperCamelCase__    :     Optional[Any]        =						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=SCREAMING_SNAKE_CASE							,     )
        UpperCamelCase__    :     Tuple        =						prepare_marian_inputs_dict(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)
        return config, inputs_dict
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Any		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__       ,  UpperCamelCase__    :     List[Any]        =						self.prepare_config_and_inputs()
        return config, inputs_dict
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     int							,     SCREAMING_SNAKE_CASE      :     List[str]							,     SCREAMING_SNAKE_CASE      :     Union[str, Any]							,     SCREAMING_SNAKE_CASE      :     List[Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     List[str]        =						20
        UpperCamelCase__    :     str        =						model_class_name(SCREAMING_SNAKE_CASE		)
  
        UpperCamelCase__    :     int        =						model.encode(inputs_dict["input_ids"]		)
  
        UpperCamelCase__       ,  UpperCamelCase__    :     Tuple        =						(
            inputs_dict["decoder_input_ids"],
            inputs_dict["decoder_attention_mask"],
        )
  
        UpperCamelCase__    :     Optional[Any]        =						model.init_cache(decoder_input_ids.shape[0]							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     Optional[int]        =						jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,     dtype="i4"		)
  
        UpperCamelCase__    :     List[str]        =						jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]							,     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,     )
        UpperCamelCase__    :     int        =						model.decode(
            decoder_input_ids[:, :-1]							,     SCREAMING_SNAKE_CASE							,     decoder_attention_mask=SCREAMING_SNAKE_CASE							,     past_key_values=SCREAMING_SNAKE_CASE							,     decoder_position_ids=SCREAMING_SNAKE_CASE							,     )
  
        UpperCamelCase__    :     List[Any]        =						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,     dtype="i4"		)
        UpperCamelCase__    :     Tuple        =						model.decode(
            decoder_input_ids[:, -1:]							,     SCREAMING_SNAKE_CASE							,     decoder_attention_mask=SCREAMING_SNAKE_CASE							,     past_key_values=outputs_cache.past_key_values							,     decoder_position_ids=SCREAMING_SNAKE_CASE							,     )
  
        UpperCamelCase__    :     Dict        =						model.decode(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)
  
        UpperCamelCase__    :     Optional[int]        =						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      :     Dict							,     SCREAMING_SNAKE_CASE      :     List[str]							,     SCREAMING_SNAKE_CASE      :     Optional[Any]							,     SCREAMING_SNAKE_CASE      :     Tuple		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Tuple        =						20
        UpperCamelCase__    :     List[Any]        =						model_class_name(SCREAMING_SNAKE_CASE		)
  
        UpperCamelCase__    :     str        =						model.encode(inputs_dict["input_ids"]		)
  
        UpperCamelCase__       ,  UpperCamelCase__    :     Optional[Any]        =						(
            inputs_dict["decoder_input_ids"],
            inputs_dict["decoder_attention_mask"],
        )
  
        UpperCamelCase__    :     str        =						jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])		),
            ]							,     axis=-1							,     )
  
        UpperCamelCase__    :     Optional[Any]        =						model.init_cache(decoder_input_ids.shape[0]							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     Optional[int]        =						jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]							,     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,     )
  
        UpperCamelCase__    :     Union[str, Any]        =						model.decode(
            decoder_input_ids[:, :-1]							,     SCREAMING_SNAKE_CASE							,     decoder_attention_mask=SCREAMING_SNAKE_CASE							,     past_key_values=SCREAMING_SNAKE_CASE							,     decoder_position_ids=SCREAMING_SNAKE_CASE							,     )
        UpperCamelCase__    :     List[Any]        =						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,     dtype="i4"		)
        UpperCamelCase__    :     Any        =						model.decode(
            decoder_input_ids[:, -1:]							,     SCREAMING_SNAKE_CASE							,     past_key_values=outputs_cache.past_key_values							,     decoder_attention_mask=SCREAMING_SNAKE_CASE							,     decoder_position_ids=SCREAMING_SNAKE_CASE							,     )
  
        UpperCamelCase__    :     List[Any]        =						model.decode(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     decoder_attention_mask=SCREAMING_SNAKE_CASE		)
  
        UpperCamelCase__    :     Tuple        =						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     __a						(					A__ ,     unittest.TestCase ,     A__ ):
   _lowerCAmelCase : Union[str, Any] 				=				True
   _lowerCAmelCase : int 				=				(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
   _lowerCAmelCase : List[str] 				=				(FlaxMarianMTModel,) if is_flax_available() else ()
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Optional[Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     List[str]        =						FlaxMarianModelTester(self		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Dict		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__       ,  UpperCamelCase__    :     List[Any]        =						self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Optional[int]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__       ,  UpperCamelCase__    :     Dict        =						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(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Any		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__       ,  UpperCamelCase__    :     Union[str, Any]        =						self.model_tester.prepare_config_and_inputs_for_common()
  
        for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__		):
                  UpperCamelCase__    :     Optional[int]        =						self._prepare_for_class(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)
                  UpperCamelCase__    :     Tuple        =						model_class(SCREAMING_SNAKE_CASE		)
  
                  @jax.jit
                  def encode_jitted(SCREAMING_SNAKE_CASE      :     Tuple							,     SCREAMING_SNAKE_CASE      :     List[Any]=None							,     **SCREAMING_SNAKE_CASE      :     Optional[int]		):
                       return model.encode(input_ids=SCREAMING_SNAKE_CASE							,     attention_mask=SCREAMING_SNAKE_CASE		)
  
                  with self.subTest("JIT Enabled"		):
                       UpperCamelCase__    :     str        =						encode_jitted(**SCREAMING_SNAKE_CASE		).to_tuple()
  
                  with self.subTest("JIT Disabled"		):
                       with jax.disable_jit():
                            UpperCamelCase__    :     List[str]        =						encode_jitted(**SCREAMING_SNAKE_CASE		).to_tuple()
  
                  self.assertEqual(len(SCREAMING_SNAKE_CASE		)							,     len(SCREAMING_SNAKE_CASE		)		)
                  for jitted_output, output in zip(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		):
                       self.assertEqual(jitted_output.shape							,     output.shape		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     List[str]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__       ,  UpperCamelCase__    :     Dict        =						self.model_tester.prepare_config_and_inputs_for_common()
  
        for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__		):
                  UpperCamelCase__    :     List[Any]        =						model_class(SCREAMING_SNAKE_CASE		)
                  UpperCamelCase__    :     Any        =						model.encode(inputs_dict["input_ids"]							,     inputs_dict["attention_mask"]		)
  
                  UpperCamelCase__    :     Any        =						{
                      "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(SCREAMING_SNAKE_CASE      :     Dict							,     SCREAMING_SNAKE_CASE      :     int							,     SCREAMING_SNAKE_CASE      :     List[str]		):
                       return model.decode(
                           decoder_input_ids=SCREAMING_SNAKE_CASE							,     decoder_attention_mask=SCREAMING_SNAKE_CASE							,     encoder_outputs=SCREAMING_SNAKE_CASE							,     )
  
                  with self.subTest("JIT Enabled"		):
                       UpperCamelCase__    :     Union[str, Any]        =						decode_jitted(**SCREAMING_SNAKE_CASE		).to_tuple()
  
                  with self.subTest("JIT Disabled"		):
                       with jax.disable_jit():
                            UpperCamelCase__    :     Optional[int]        =						decode_jitted(**SCREAMING_SNAKE_CASE		).to_tuple()
  
                  self.assertEqual(len(SCREAMING_SNAKE_CASE		)							,     len(SCREAMING_SNAKE_CASE		)		)
                  for jitted_output, output in zip(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		):
                       self.assertEqual(jitted_output.shape							,     output.shape		)
  
  
  
  
  
  
  
   @slow
   def       __lowercase      (	self      :     Optional[Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        for model_class_name in self.all_model_classes:
             UpperCamelCase__    :     Union[str, Any]        =						model_class_name.from_pretrained("Helsinki-NLP/opus-mt-en-de"		)
             # FlaxMarianForSequenceClassification expects eos token in input_ids
             UpperCamelCase__    :     Optional[int]        =						np.ones((1, 1)		) * model.config.eos_token_id
             UpperCamelCase__    :     Optional[int]        =						model(SCREAMING_SNAKE_CASE		)
             self.assertIsNotNone(SCREAMING_SNAKE_CASE		)
  
  
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class     __a						(					unittest.TestCase ):
   _lowerCAmelCase : Any 				=				None
   _lowerCAmelCase : Tuple 				=				None
  
  
  
  
  
  
  
   @classmethod
   def       __lowercase      (	cls      :     Dict		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     List[Any]        =						F'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
        return cls
  
  
  
  
  
  
  
   @cached_property
   def       __lowercase      (	self      :     Any		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        return MarianTokenizer.from_pretrained(self.model_name		)
  
  
  
  
  
  
  
   @property
   def       __lowercase      (	self      :     List[Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        return self.tokenizer.eos_token_id
  
  
  
  
  
  
  
   @cached_property
   def       __lowercase      (	self      :     Union[str, Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     FlaxMarianMTModel        =						FlaxMarianMTModel.from_pretrained(self.model_name		)
        self.assertEqual(model.config.decoder_start_token_id							,     model.config.pad_token_id		)
        return model
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Any							,     **SCREAMING_SNAKE_CASE      :     List[Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     List[Any]        =						self.translate_src_text(**SCREAMING_SNAKE_CASE		)
        self.assertListEqual(self.expected_text							,     SCREAMING_SNAKE_CASE		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Union[str, Any]							,     **SCREAMING_SNAKE_CASE      :     str		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     List[Any]        =						self.tokenizer(self.src_text							,     padding=SCREAMING_SNAKE_CASE							,     return_tensors="np"							,     **SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     Optional[int]        =						self.model.generate(
            model_inputs.input_ids							,     attention_mask=model_inputs.attention_mask							,     num_beams=2							,     max_length=1_28							,     ).sequences
        UpperCamelCase__    :     List[Any]        =						self.tokenizer.batch_decode(SCREAMING_SNAKE_CASE							,     skip_special_tokens=SCREAMING_SNAKE_CASE		)
        return generated_words
  
  
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class     __a						(					A__ ):
   _lowerCAmelCase : int 				=				'''en'''
   _lowerCAmelCase : List[Any] 				=				'''fr'''
   _lowerCAmelCase : int 				=				[
       '''I am a small frog.''',
       '''Now I can forget the 100 words of german that I know.''',
   ]
   _lowerCAmelCase : List[str] 				=				[
       '''Je suis une petite grenouille.''',
       '''Maintenant, je peux oublier les 100 mots d\'allemand que je connais.''',
   ]
  
  
  
  
  
  
  
   @slow
   def       __lowercase      (	self      :     int		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class     __a						(					A__ ):
   _lowerCAmelCase : int 				=				'''fr'''
   _lowerCAmelCase : List[Any] 				=				'''en'''
   _lowerCAmelCase : Optional[Any] 				=				[
       '''Donnez moi le micro.''',
       '''Tom et Mary étaient assis à une table.''',  # Accents
   ]
   _lowerCAmelCase : str 				=				[
       '''Give me the microphone.''',
       '''Tom and Mary were sitting at a table.''',
   ]
  
  
  
  
  
  
  
   @slow
   def       __lowercase      (	self      :     Optional[Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class     __a						(					A__ ):
   _lowerCAmelCase : Optional[int] 				=				'''mt'''
   _lowerCAmelCase : Tuple 				=				'''en'''
   _lowerCAmelCase : Optional[int] 				=				['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
   _lowerCAmelCase : int 				=				['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']
  
  
  
  
  
  
  
   @slow
   def       __lowercase      (	self      :     Union[str, Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class     __a						(					A__ ):
   _lowerCAmelCase : List[str] 				=				'''en'''
   _lowerCAmelCase : Any 				=				'''de'''
   _lowerCAmelCase : str 				=				[
       '''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.''',
   ]
   _lowerCAmelCase : Dict 				=				[
       '''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      :     Dict		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class     __a						(					A__ ):
   _lowerCAmelCase : List[Any] 				=				'''en'''
   _lowerCAmelCase : List[Any] 				=				'''zh'''
   _lowerCAmelCase : Optional[int] 				=				['''My name is Wolfgang and I live in Berlin''']
   _lowerCAmelCase : int 				=				['''我叫沃尔夫冈 我住在柏林''']
  
  
  
  
  
  
  
   @slow
   def       __lowercase      (	self      :     int		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class     __a						(					A__ ):
   _lowerCAmelCase : int 				=				'''ru'''
   _lowerCAmelCase : List[Any] 				=				'''fr'''
   _lowerCAmelCase : List[Any] 				=				['''Он показал мне рукопись своей новой пьесы.''']
   _lowerCAmelCase : List[str] 				=				['''Il m\'a montré le manuscrit de sa nouvelle pièce.''']
  
  
  
  
  
  
  
   @slow
   def       __lowercase      (	self      :     Tuple		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class     __a						(					A__ ):
   _lowerCAmelCase : str 				=				'''en'''
   _lowerCAmelCase : List[str] 				=				'''ROMANCE'''
   _lowerCAmelCase : Optional[Any] 				=				[
       '''>>fr<< Don\'t spend so much time watching TV.''',
       '''>>pt<< Your message has been sent.''',
       '''>>es<< He\'s two years older than me.''',
   ]
   _lowerCAmelCase : Any 				=				[
       '''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      :     Union[str, Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        self._assert_generated_batch_equal_expected()
style_context: 
  
  
  
  
  
  
  import os
  from dataclasses import replace
  
  import jax
  import wandb
  from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
  from datasets import load_dataset
  from flax import jax_utils
  
  from transformers import BigBirdTokenizerFast
  
  
  if __name__ == "__main__":
   print('''#################### AVAILABLE DEVICES ####################''')
   print(jax.devices())
   print('''###########################################################''')
  
   # setup for wandb sweep
   lowerCamelCase			:							str       =Args()
   lowerCamelCase			:							str       =wandb.init(project='''bigbird-natural-questions''', config=args.__dict__)
   lowerCamelCase			:							Any       =dict(logger.config)
   del wandb_args["batch_size"]
   lowerCamelCase			:							str       =replace(args, **wandb_args)
   lowerCamelCase			:							List[str]       =args.base_dir + '''-''' + wandb.run.id
   lowerCamelCase			:							str       =replace(args, base_dir=base_dir)
   print(args)
  
   lowerCamelCase			:							List[Any]       =load_dataset('''json''', data_files=args.tr_data_path)['''train''']
   lowerCamelCase			:							str       =load_dataset('''json''', data_files=args.val_data_path)['''train''']
  
   # drop extra batch for now
   lowerCamelCase			:							List[Any]       =range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
   lowerCamelCase			:							Union[str, Any]       =tr_dataset.shuffle().select(indices)
   lowerCamelCase			:							List[str]       =range(len(val_dataset) - len(val_dataset) % args.batch_size)
   lowerCamelCase			:							Dict       =val_dataset.shuffle().select(indices)
  
   if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
    lowerCamelCase			:							Optional[int]       =tr_dataset.shuffle().select(range(80000))
    lowerCamelCase			:							List[Any]       =val_dataset.shuffle().select(range(8000))
  
   print(tr_dataset)
   print(val_dataset)
  
   lowerCamelCase			:							int       =FlaxBigBirdForNaturalQuestions.from_pretrained(
       args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
   )
   lowerCamelCase			:							Union[str, Any]       =BigBirdTokenizerFast.from_pretrained(args.model_id)
   lowerCamelCase			:							Dict       =DataCollator(pad_id=tokenizer.pad_token_id, max_length=4096)
  
   lowerCamelCase			:							List[str]       ={
       '''lr''': args.lr,
       '''init_lr''': args.init_lr,
       '''warmup_steps''': args.warmup_steps,
       '''num_train_steps''': args.max_epochs * (len(tr_dataset) // args.batch_size),
       '''weight_decay''': args.weight_decay,
   }
   lowerCamelCase       ,    lowerCamelCase			:							Tuple       =build_tx(**tx_args)
  
   lowerCamelCase			:							Optional[Any]       =Trainer(
       args=args,
       data_collator=data_collator,
       model_save_fn=model.save_pretrained,
       train_step_fn=train_step,
       val_step_fn=val_step,
       logger=logger,
       scheduler_fn=lr,
   )
  
   lowerCamelCase			:							Tuple       =None
   lowerCamelCase			:							str       =trainer.create_state(model, tx, num_train_steps=tx_args['''num_train_steps'''], ckpt_dir=ckpt_dir)
   try:
    trainer.train(state, tr_dataset, val_dataset)
   except KeyboardInterrupt:
    print('''Oooops; TRAINING STOPPED UNFORTUNATELY''')
  
   print('''SAVING WEIGHTS IN `final-weights`''')
   lowerCamelCase			:							str       =jax_utils.unreplicate(state.params)
   model.save_pretrained(os.path.join(args.base_dir, '''final-weights'''), params=params)