



'''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
    a__						:			List[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 _lowercase   (  __A							,__A							,__A=None							,__A=None							,__A=None							,__A=None							,__A=None							,__A=None							,):



 '''simple docstring'''



 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		,	lowercase		,	lowercase=1_3		,	lowercase=7		,	lowercase=True		,	lowercase=False		,	lowercase=9_9		,	lowercase=1_6		,	lowercase=2		,	lowercase=4		,	lowercase=4		,	lowercase="gelu"		,	lowercase=0.1		,	lowercase=0.1		,	lowercase=3_2		,	lowercase=2		,	lowercase=1		,	lowercase=0		,	lowercase=0.02		,	)  ->							int:
  __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    __lowerCamelCase				(  self			)  ->							str:
  __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(lowercase		,	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=lowercase		,	)
  __UpperCamelCase				      =  prepare_marian_inputs_dict(lowercase		,	lowercase		,	lowercase			)
  return config, inputs_dict



 def    __lowerCamelCase				(  self			)  ->							Union[str, Any]:
  __UpperCamelCase      ,		__UpperCamelCase				      =  self.prepare_config_and_inputs()
  return config, inputs_dict



 def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase			)  ->							Optional[Any]:
  __UpperCamelCase				      =  2_0
  __UpperCamelCase				      =  model_class_name(lowercase			)

  __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]		,	lowercase		,	lowercase			)
  __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]		,	lowercase		,	decoder_attention_mask=lowercase		,	past_key_values=lowercase		,	decoder_position_ids=lowercase		,	)

  __UpperCamelCase				      =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,	dtype="""i4"""			)
  __UpperCamelCase				      =  model.decode(
      decoder_input_ids[:, -1:]		,	lowercase		,	decoder_attention_mask=lowercase		,	past_key_values=outputs_cache.past_key_values		,	decoder_position_ids=lowercase		,	)

  __UpperCamelCase				      =  model.decode(lowercase		,	lowercase			)

  __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    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase			)  ->							Dict:
  __UpperCamelCase				      =  2_0
  __UpperCamelCase				      =  model_class_name(lowercase			)

  __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]		,	lowercase		,	lowercase			)
  __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]		,	lowercase		,	decoder_attention_mask=lowercase		,	past_key_values=lowercase		,	decoder_position_ids=lowercase		,	)
  __UpperCamelCase				      =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,	dtype="""i4"""			)
  __UpperCamelCase				      =  model.decode(
      decoder_input_ids[:, -1:]		,	lowercase		,	past_key_values=outputs_cache.past_key_values		,	decoder_attention_mask=lowercase		,	decoder_position_ids=lowercase		,	)

  __UpperCamelCase				      =  model.decode(lowercase		,	lowercase		,	decoder_attention_mask=lowercase			)

  __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__  (    UpperCAmelCase_							,			unittest.TestCase							,			UpperCAmelCase_):
 __SCREAMING_SNAKE_CASE					      =				True
 __SCREAMING_SNAKE_CASE					      =				(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
 __SCREAMING_SNAKE_CASE					      =				(FlaxMarianMTModel,) if is_flax_available() else ()



 def    __lowerCamelCase				(  self			)  ->							List[Any]:
  __UpperCamelCase				      =  FlaxMarianModelTester(self			)



 def    __lowerCamelCase				(  self			)  ->							List[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(lowercase		,	lowercase		,	lowercase			)



 def    __lowerCamelCase				(  self			)  ->							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(lowercase		,	lowercase		,	lowercase			)



 def    __lowerCamelCase				(  self			)  ->							Optional[int]:
  __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(lowercase		,	lowercase			)
    __UpperCamelCase				      =  model_class(lowercase			)

    @jax.jit
    def encode_jitted(lowercase		,	lowercase=None		,	**lowercase			):
     return model.encode(input_ids=lowercase		,	attention_mask=lowercase			)

    with self.subTest("""JIT Enabled"""			):
     __UpperCamelCase				      =  encode_jitted(**lowercase			).to_tuple()

    with self.subTest("""JIT Disabled"""			):
     with jax.disable_jit():
      __UpperCamelCase				      =  encode_jitted(**lowercase			).to_tuple()

    self.assertEqual(len(lowercase			)		,	len(lowercase			)			)
    for jitted_output, output in zip(lowercase		,	lowercase			):
     self.assertEqual(jitted_output.shape		,	output.shape			)



 def    __lowerCamelCase				(  self			)  ->							str:
  __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(lowercase			)
    __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(lowercase		,	lowercase		,	lowercase			):
     return model.decode(
         decoder_input_ids=lowercase		,	decoder_attention_mask=lowercase		,	encoder_outputs=lowercase		,	)

    with self.subTest("""JIT Enabled"""			):
     __UpperCamelCase				      =  decode_jitted(**lowercase			).to_tuple()

    with self.subTest("""JIT Disabled"""			):
     with jax.disable_jit():
      __UpperCamelCase				      =  decode_jitted(**lowercase			).to_tuple()

    self.assertEqual(len(lowercase			)		,	len(lowercase			)			)
    for jitted_output, output in zip(lowercase		,	lowercase			):
     self.assertEqual(jitted_output.shape		,	output.shape			)




 @slow
 def    __lowerCamelCase				(  self			)  ->							Optional[Any]:
  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(lowercase			)
   self.assertIsNotNone(lowercase			)


@require_flax
@require_sentencepiece
@require_tokenizers
class        UpperCAmelCase__  (    unittest.TestCase):
 __SCREAMING_SNAKE_CASE					      =				None
 __SCREAMING_SNAKE_CASE					      =				None



 @classmethod
 def    __lowerCamelCase				(  cls			)  ->							None:
  __UpperCamelCase				      =  f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
  return cls



 @cached_property
 def    __lowerCamelCase				(  self			)  ->							List[Any]:
  return MarianTokenizer.from_pretrained(self.model_name			)



 @property
 def    __lowerCamelCase				(  self			)  ->							int:
  return self.tokenizer.eos_token_id



 @cached_property
 def    __lowerCamelCase				(  self			)  ->							Dict:
  __UpperCamelCase				      =  FlaxMarianMTModel.from_pretrained(self.model_name			)
  self.assertEqual(model.config.decoder_start_token_id		,	model.config.pad_token_id			)
  return model



 def    __lowerCamelCase				(  self		,	**lowercase			)  ->							List[str]:
  __UpperCamelCase				      =  self.translate_src_text(**lowercase			)
  self.assertListEqual(self.expected_text		,	lowercase			)




 def    __lowerCamelCase				(  self		,	**lowercase			)  ->							Optional[int]:
  __UpperCamelCase				      =  self.tokenizer(self.src_text		,	padding=lowercase		,	return_tensors="""np"""		,	**lowercase			)
  __UpperCamelCase				      =  self.model.generate(
      model_inputs.input_ids		,	attention_mask=model_inputs.attention_mask		,	num_beams=2		,	max_length=1_2_8		,	).sequences
  __UpperCamelCase				      =  self.tokenizer.batch_decode(lowercase		,	skip_special_tokens=lowercase			)
  return generated_words


@require_flax
@require_sentencepiece
@require_tokenizers
class        UpperCAmelCase__  (    UpperCAmelCase_):
 __SCREAMING_SNAKE_CASE					      =				'''en'''
 __SCREAMING_SNAKE_CASE					      =				'''fr'''
 __SCREAMING_SNAKE_CASE					      =				[
     '''I am a small frog.''',
     '''Now I can forget the 100 words of german that I know.''',
 ]
 __SCREAMING_SNAKE_CASE					      =				[
     '''Je suis une petite grenouille.''',
     '''Maintenant, je peux oublier les 100 mots d\'allemand que je connais.''',
 ]



 @slow
 def    __lowerCamelCase				(  self			)  ->							List[str]:
  self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class        UpperCAmelCase__  (    UpperCAmelCase_):
 __SCREAMING_SNAKE_CASE					      =				'''fr'''
 __SCREAMING_SNAKE_CASE					      =				'''en'''
 __SCREAMING_SNAKE_CASE					      =				[
     '''Donnez moi le micro.''',
     '''Tom et Mary étaient assis à une table.''',  # Accents
 ]
 __SCREAMING_SNAKE_CASE					      =				[
     '''Give me the microphone.''',
     '''Tom and Mary were sitting at a table.''',
 ]



 @slow
 def    __lowerCamelCase				(  self			)  ->							List[Any]:
  self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class        UpperCAmelCase__  (    UpperCAmelCase_):
 __SCREAMING_SNAKE_CASE					      =				'''mt'''
 __SCREAMING_SNAKE_CASE					      =				'''en'''
 __SCREAMING_SNAKE_CASE					      =				['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
 __SCREAMING_SNAKE_CASE					      =				['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']



 @slow
 def    __lowerCamelCase				(  self			)  ->							Optional[Any]:
  self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class        UpperCAmelCase__  (    UpperCAmelCase_):
 __SCREAMING_SNAKE_CASE					      =				'''en'''
 __SCREAMING_SNAKE_CASE					      =				'''de'''
 __SCREAMING_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.''',
 ]
 __SCREAMING_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    __lowerCamelCase				(  self			)  ->							Optional[Any]:
  self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class        UpperCAmelCase__  (    UpperCAmelCase_):
 __SCREAMING_SNAKE_CASE					      =				'''en'''
 __SCREAMING_SNAKE_CASE					      =				'''zh'''
 __SCREAMING_SNAKE_CASE					      =				['''My name is Wolfgang and I live in Berlin''']
 __SCREAMING_SNAKE_CASE					      =				['''我叫沃尔夫冈 我住在柏林''']



 @slow
 def    __lowerCamelCase				(  self			)  ->							Any:
  self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class        UpperCAmelCase__  (    UpperCAmelCase_):
 __SCREAMING_SNAKE_CASE					      =				'''ru'''
 __SCREAMING_SNAKE_CASE					      =				'''fr'''
 __SCREAMING_SNAKE_CASE					      =				['''Он показал мне рукопись своей новой пьесы.''']
 __SCREAMING_SNAKE_CASE					      =				['''Il m\'a montré le manuscrit de sa nouvelle pièce.''']



 @slow
 def    __lowerCamelCase				(  self			)  ->							Union[str, Any]:
  self._assert_generated_batch_equal_expected()


@require_flax
@require_sentencepiece
@require_tokenizers
class        UpperCAmelCase__  (    UpperCAmelCase_):
 __SCREAMING_SNAKE_CASE					      =				'''en'''
 __SCREAMING_SNAKE_CASE					      =				'''ROMANCE'''
 __SCREAMING_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.''',
 ]
 __SCREAMING_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    __lowerCamelCase				(  self			)  ->							str:
  self._assert_generated_batch_equal_expected()










'''simple docstring'''



a__						:			Any										=			{
    '0000': '0',
    '0001': '1',
    '0010': '2',
    '0011': '3',
    '0100': '4',
    '0101': '5',
    '0110': '6',
    '0111': '7',
    '1000': '8',
    '1001': '9',
    '1010': 'a',
    '1011': 'b',
    '1100': 'c',
    '1101': 'd',
    '1110': 'e',
    '1111': 'f',
}







def _lowercase   (  __A       ):



 '''simple docstring'''



 __UpperCamelCase				      =  str(__A       ).strip()

 # Exceptions
 if not binary_str:
  raise ValueError("""Empty string was passed to the function"""       )
 __UpperCamelCase				      =  binary_str[0] == """-"""
 __UpperCamelCase				      =  binary_str[1:] if is_negative else binary_str
 if not all(char in """01""" for char in binary_str       ):
  raise ValueError("""Non-binary value was passed to the function"""       )

 __UpperCamelCase				      =  (
     """0""" * (4 * (divmod(len(__A       )							,4       )[0] + 1) - len(__A       )) + binary_str
 )

 __UpperCamelCase				      =  []
 for x in range(0							,len(__A       )							,4       ):
  hexadecimal.append(BITS_TO_HEX[binary_str[x : x + 4]]       )
 __UpperCamelCase				      =  """0x""" + """""".join(__A       )

 return "-" + hexadecimal_str if is_negative else hexadecimal_str


if __name__ == "__main__":
 from doctest import testmod

 testmod()





