

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
   __UpperCAmelCase      = """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 	snake_case_      (__A     :						Optional[int]					,   __A     :						List[str]					,   __A     :						List[Any]=None					,   __A     :						int=None					,   __A     :						Optional[Any]=None					,   __A     :						Dict=None					,   __A     :						List[str]=None					,   __A     :						str=None					,   )			->	Tuple:
   if attention_mask is None:
      __lowerCAmelCase  :			Dict					    =						np.where(input_ids != config.pad_token_id					,   1					,   0     )
   if decoder_attention_mask is None:
      __lowerCAmelCase  :			Union[str, Any]					    =						np.where(decoder_input_ids != config.pad_token_id					,   1					,   0     )
   if head_mask is None:
      __lowerCAmelCase  :			Optional[Any]					    =						np.ones((config.encoder_layers, config.encoder_attention_heads)     )
   if decoder_head_mask is None:
      __lowerCAmelCase  :			str					    =						np.ones((config.decoder_layers, config.decoder_attention_heads)     )
   if cross_attn_head_mask is None:
      __lowerCAmelCase  :			Optional[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 SCREAMING_SNAKE_CASE	:
  """simple docstring"""






  def __init__(			self :    Optional[int]       ,							lowerCAmelCase :    Union[str, Any]       ,							lowerCAmelCase :    Tuple=13       ,							lowerCAmelCase :    Any=7       ,							lowerCAmelCase :    Union[str, Any]=True       ,							lowerCAmelCase :    List[str]=False       ,							lowerCAmelCase :    List[Any]=99       ,							lowerCAmelCase :    Dict=16       ,							lowerCAmelCase :    Optional[int]=2       ,							lowerCAmelCase :    Optional[Any]=4       ,							lowerCAmelCase :    List[Any]=4       ,							lowerCAmelCase :    List[str]="gelu"       ,							lowerCAmelCase :    int=0.1       ,							lowerCAmelCase :    int=0.1       ,							lowerCAmelCase :    str=32       ,							lowerCAmelCase :    int=2       ,							lowerCAmelCase :    int=1       ,							lowerCAmelCase :    Dict=0       ,							lowerCAmelCase :    Optional[int]=0.02       ,							)							->	int:






     """simple docstring"""







     __lowerCAmelCase  :			Optional[int]					    =						parent
     __lowerCAmelCase  :			Tuple					    =						batch_size
     __lowerCAmelCase  :			Any					    =						seq_length
     __lowerCAmelCase  :			Union[str, Any]					    =						is_training
     __lowerCAmelCase  :			List[Any]					    =						use_labels
     __lowerCAmelCase  :			Optional[int]					    =						vocab_size
     __lowerCAmelCase  :			Tuple					    =						hidden_size
     __lowerCAmelCase  :			Optional[int]					    =						num_hidden_layers
     __lowerCAmelCase  :			Dict					    =						num_attention_heads
     __lowerCAmelCase  :			Optional[Any]					    =						intermediate_size
     __lowerCAmelCase  :			List[str]					    =						hidden_act
     __lowerCAmelCase  :			int					    =						hidden_dropout_prob
     __lowerCAmelCase  :			List[str]					    =						attention_probs_dropout_prob
     __lowerCAmelCase  :			Optional[int]					    =						max_position_embeddings
     __lowerCAmelCase  :			Union[str, Any]					    =						eos_token_id
     __lowerCAmelCase  :			List[str]					    =						pad_token_id
     __lowerCAmelCase  :			List[str]					    =						bos_token_id
     __lowerCAmelCase  :			str					    =						initializer_range







  def        SCREAMING_SNAKE_CASE    (			self :    Optional[Any]    )							->	Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :			str					    =						np.clip(ids_tensor([self.batch_size, self.seq_length - 1]       ,							self.vocab_size    )       ,							3       ,							self.vocab_size    )
     __lowerCAmelCase  :			Optional[int]					    =						np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)       ,							dtype=np.intaa    ))       ,							-1    )

     __lowerCAmelCase  :			int					    =						shift_tokens_right(lowerCAmelCase       ,							1       ,							2    )

     __lowerCAmelCase  :			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=lowerCAmelCase       ,							)
     __lowerCAmelCase  :			Optional[Any]					    =						prepare_marian_inputs_dict(lowerCAmelCase       ,							lowerCAmelCase       ,							lowerCAmelCase    )
     return config, inputs_dict







  def        SCREAMING_SNAKE_CASE    (			self :    List[Any]    )							->	Tuple:






     """simple docstring"""







     __lowerCAmelCase    ,__lowerCAmelCase  :			List[str]					    =						self.prepare_config_and_inputs()
     return config, inputs_dict







  def        SCREAMING_SNAKE_CASE    (			self :    str       ,							lowerCAmelCase :    Dict       ,							lowerCAmelCase :    Dict       ,							lowerCAmelCase :    List[str]    )							->	Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :			List[str]					    =						20
     __lowerCAmelCase  :			int					    =						model_class_name(lowerCAmelCase    )

     __lowerCAmelCase  :			Union[str, Any]					    =						model.encode(inputs_dict["""input_ids"""]    )

     __lowerCAmelCase    ,__lowerCAmelCase  :			Tuple					    =						(
         inputs_dict["""decoder_input_ids"""],
         inputs_dict["""decoder_attention_mask"""],
     )

     __lowerCAmelCase  :			Optional[int]					    =						model.init_cache(decoder_input_ids.shape[0]       ,							lowerCAmelCase       ,							lowerCAmelCase    )
     __lowerCAmelCase  :			Optional[Any]					    =						jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       ,							dtype="""i4"""    )

     __lowerCAmelCase  :			Any					    =						jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1    )[None, :]       ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,							)
     __lowerCAmelCase  :			Any					    =						model.decode(
         decoder_input_ids[:, :-1]       ,							lowerCAmelCase       ,							decoder_attention_mask=lowerCAmelCase       ,							past_key_values=lowerCAmelCase       ,							decoder_position_ids=lowerCAmelCase       ,							)

     __lowerCAmelCase  :			Union[str, Any]					    =						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,							dtype="""i4"""    )
     __lowerCAmelCase  :			Any					    =						model.decode(
         decoder_input_ids[:, -1:]       ,							lowerCAmelCase       ,							decoder_attention_mask=lowerCAmelCase       ,							past_key_values=outputs_cache.past_key_values       ,							decoder_position_ids=lowerCAmelCase       ,							)

     __lowerCAmelCase  :			List[str]					    =						model.decode(lowerCAmelCase       ,							lowerCAmelCase    )

     __lowerCAmelCase  :			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}'''    )







  def        SCREAMING_SNAKE_CASE    (			self :    List[Any]       ,							lowerCAmelCase :    Optional[Any]       ,							lowerCAmelCase :    int       ,							lowerCAmelCase :    Any    )							->	Any:






     """simple docstring"""







     __lowerCAmelCase  :			Union[str, Any]					    =						20
     __lowerCAmelCase  :			str					    =						model_class_name(lowerCAmelCase    )

     __lowerCAmelCase  :			str					    =						model.encode(inputs_dict["""input_ids"""]    )

     __lowerCAmelCase    ,__lowerCAmelCase  :			str					    =						(
         inputs_dict["""decoder_input_ids"""],
         inputs_dict["""decoder_attention_mask"""],
     )

     __lowerCAmelCase  :			List[str]					    =						jnp.concatenate(
         [
             decoder_attention_mask,
             jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])    ),
         ]       ,							axis=-1       ,							)

     __lowerCAmelCase  :			Any					    =						model.init_cache(decoder_input_ids.shape[0]       ,							lowerCAmelCase       ,							lowerCAmelCase    )
     __lowerCAmelCase  :			Union[str, Any]					    =						jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1    )[None, :]       ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,							)

     __lowerCAmelCase  :			Tuple					    =						model.decode(
         decoder_input_ids[:, :-1]       ,							lowerCAmelCase       ,							decoder_attention_mask=lowerCAmelCase       ,							past_key_values=lowerCAmelCase       ,							decoder_position_ids=lowerCAmelCase       ,							)
     __lowerCAmelCase  :			Any					    =						jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,							dtype="""i4"""    )
     __lowerCAmelCase  :			str					    =						model.decode(
         decoder_input_ids[:, -1:]       ,							lowerCAmelCase       ,							past_key_values=outputs_cache.past_key_values       ,							decoder_attention_mask=lowerCAmelCase       ,							decoder_position_ids=lowerCAmelCase       ,							)

     __lowerCAmelCase  :			Optional[Any]					    =						model.decode(lowerCAmelCase       ,							lowerCAmelCase       ,							decoder_attention_mask=lowerCAmelCase    )

     __lowerCAmelCase  :			Optional[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}'''    )






@require_flax
class SCREAMING_SNAKE_CASE	(  a_   ,							unittest.TestCase   ,							a_ ):
  """simple docstring"""






  lowerCamelCase    :			List[Any]						      =True
  lowerCamelCase    :			Any						      =(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
  lowerCamelCase    :			List[Any]						      =(FlaxMarianMTModel,) if is_flax_available() else ()







  def        SCREAMING_SNAKE_CASE    (			self :    Optional[Any]    )							->	int:






     """simple docstring"""







     __lowerCAmelCase  :			Union[str, Any]					    =						FlaxMarianModelTester(self    )







  def        SCREAMING_SNAKE_CASE    (			self :    int    )							->	Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase    ,__lowerCAmelCase  :			Optional[Any]					    =						self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
        self.model_tester.check_use_cache_forward(lowerCAmelCase       ,							lowerCAmelCase       ,							lowerCAmelCase    )







  def        SCREAMING_SNAKE_CASE    (			self :    List[str]    )							->	Dict:






     """simple docstring"""







     __lowerCAmelCase    ,__lowerCAmelCase  :			List[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(lowerCAmelCase       ,							lowerCAmelCase       ,							lowerCAmelCase    )







  def        SCREAMING_SNAKE_CASE    (			self :    Optional[Any]    )							->	Tuple:






     """simple docstring"""







     __lowerCAmelCase    ,__lowerCAmelCase  :			List[str]					    =						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__    ):
           __lowerCAmelCase  :			Union[str, Any]					    =						self._prepare_for_class(lowerCAmelCase       ,							lowerCAmelCase    )
           __lowerCAmelCase  :			List[Any]					    =						model_class(lowerCAmelCase    )

           @jax.jit
           def encode_jitted(lowerCAmelCase :    Any       ,							lowerCAmelCase :    Optional[Any]=None       ,							**lowerCAmelCase :    int    ):
              return model.encode(input_ids=lowerCAmelCase       ,							attention_mask=lowerCAmelCase    )

           with self.subTest("""JIT Enabled"""    ):
              __lowerCAmelCase  :			Dict					    =						encode_jitted(**lowerCAmelCase    ).to_tuple()

           with self.subTest("""JIT Disabled"""    ):
              with jax.disable_jit():
                 __lowerCAmelCase  :			Union[str, Any]					    =						encode_jitted(**lowerCAmelCase    ).to_tuple()

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







  def        SCREAMING_SNAKE_CASE    (			self :    int    )							->	Dict:






     """simple docstring"""







     __lowerCAmelCase    ,__lowerCAmelCase  :			Any					    =						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__    ):
           __lowerCAmelCase  :			Any					    =						model_class(lowerCAmelCase    )
           __lowerCAmelCase  :			str					    =						model.encode(inputs_dict["""input_ids"""]       ,							inputs_dict["""attention_mask"""]    )

           __lowerCAmelCase  :			Optional[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(lowerCAmelCase :    Tuple       ,							lowerCAmelCase :    List[str]       ,							lowerCAmelCase :    Tuple    ):
              return model.decode(
                  decoder_input_ids=lowerCAmelCase       ,							decoder_attention_mask=lowerCAmelCase       ,							encoder_outputs=lowerCAmelCase       ,							)

           with self.subTest("""JIT Enabled"""    ):
              __lowerCAmelCase  :			Optional[Any]					    =						decode_jitted(**lowerCAmelCase    ).to_tuple()

           with self.subTest("""JIT Disabled"""    ):
              with jax.disable_jit():
                 __lowerCAmelCase  :			Any					    =						decode_jitted(**lowerCAmelCase    ).to_tuple()

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







  @slow
  def        SCREAMING_SNAKE_CASE    (			self :    Any    )							->	Any:






     """simple docstring"""







     for model_class_name in self.all_model_classes:
        __lowerCAmelCase  :			Tuple					    =						model_class_name.from_pretrained("""Helsinki-NLP/opus-mt-en-de"""    )
        # FlaxMarianForSequenceClassification expects eos token in input_ids
        __lowerCAmelCase  :			Tuple					    =						np.ones((1, 1)    ) * model.config.eos_token_id
        __lowerCAmelCase  :			Union[str, Any]					    =						model(lowerCAmelCase    )
        self.assertIsNotNone(lowerCAmelCase    )






@require_flax
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE	(  unittest.TestCase ):
  """simple docstring"""






  lowerCamelCase    :			List[str]						      =None
  lowerCamelCase    :			str						      =None







  @classmethod
  def        SCREAMING_SNAKE_CASE    (			cls :    List[Any]    )							->	None:






     """simple docstring"""







     __lowerCAmelCase  :			List[str]					    =						f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
     return cls







  @cached_property
  def        SCREAMING_SNAKE_CASE    (			self :    str    )							->	Any:






     """simple docstring"""







     return MarianTokenizer.from_pretrained(self.model_name    )







  @property
  def        SCREAMING_SNAKE_CASE    (			self :    Any    )							->	int:






     """simple docstring"""







     return self.tokenizer.eos_token_id







  @cached_property
  def        SCREAMING_SNAKE_CASE    (			self :    Optional[int]    )							->	Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :			FlaxMarianMTModel					    =						FlaxMarianMTModel.from_pretrained(self.model_name    )
     self.assertEqual(model.config.decoder_start_token_id       ,							model.config.pad_token_id    )
     return model







  def        SCREAMING_SNAKE_CASE    (			self :    Optional[int]       ,							**lowerCAmelCase :    List[str]    )							->	Tuple:






     """simple docstring"""







     __lowerCAmelCase  :			Tuple					    =						self.translate_src_text(**lowerCAmelCase    )
     self.assertListEqual(self.expected_text       ,							lowerCAmelCase    )







  def        SCREAMING_SNAKE_CASE    (			self :    Optional[Any]       ,							**lowerCAmelCase :    List[Any]    )							->	int:






     """simple docstring"""







     __lowerCAmelCase  :			Optional[int]					    =						self.tokenizer(self.src_text       ,							padding=lowerCAmelCase       ,							return_tensors="""np"""       ,							**lowerCAmelCase    )
     __lowerCAmelCase  :			Dict					    =						self.model.generate(
         model_inputs.input_ids       ,							attention_mask=model_inputs.attention_mask       ,							num_beams=2       ,							max_length=1_28       ,							).sequences
     __lowerCAmelCase  :			Dict					    =						self.tokenizer.batch_decode(lowerCAmelCase       ,							skip_special_tokens=lowerCAmelCase    )
     return generated_words






@require_flax
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE	(  a_ ):
  """simple docstring"""






  lowerCamelCase    :			int						      ="en"
  lowerCamelCase    :			Optional[Any]						      ="fr"
  lowerCamelCase    :			List[str]						      =[
      "I am a small frog.",
      "Now I can forget the 100 words of german that I know.",
  ]
  lowerCamelCase    :			int						      =[
      "Je suis une petite grenouille.",
      "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
  ]







  @slow
  def        SCREAMING_SNAKE_CASE    (			self :    Union[str, Any]    )							->	Dict:






     """simple docstring"""







     self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE	(  a_ ):
  """simple docstring"""






  lowerCamelCase    :			Tuple						      ="fr"
  lowerCamelCase    :			Union[str, Any]						      ="en"
  lowerCamelCase    :			Union[str, Any]						      =[
      "Donnez moi le micro.",
      "Tom et Mary étaient assis à une table.",  # Accents
  ]
  lowerCamelCase    :			Union[str, Any]						      =[
      "Give me the microphone.",
      "Tom and Mary were sitting at a table.",
  ]







  @slow
  def        SCREAMING_SNAKE_CASE    (			self :    List[str]    )							->	Optional[Any]:






     """simple docstring"""







     self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE	(  a_ ):
  """simple docstring"""






  lowerCamelCase    :			Any						      ="mt"
  lowerCamelCase    :			int						      ="en"
  lowerCamelCase    :			Union[str, Any]						      =["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
  lowerCamelCase    :			Tuple						      =["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]







  @slow
  def        SCREAMING_SNAKE_CASE    (			self :    Optional[Any]    )							->	Any:






     """simple docstring"""







     self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE	(  a_ ):
  """simple docstring"""






  lowerCamelCase    :			Union[str, Any]						      ="en"
  lowerCamelCase    :			Tuple						      ="de"
  lowerCamelCase    :			Optional[int]						      =[
      "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    :			Optional[int]						      =[
      "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        SCREAMING_SNAKE_CASE    (			self :    Union[str, Any]    )							->	Optional[int]:






     """simple docstring"""







     self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE	(  a_ ):
  """simple docstring"""






  lowerCamelCase    :			int						      ="en"
  lowerCamelCase    :			str						      ="zh"
  lowerCamelCase    :			Tuple						      =["My name is Wolfgang and I live in Berlin"]
  lowerCamelCase    :			str						      =["我叫沃尔夫冈 我住在柏林"]







  @slow
  def        SCREAMING_SNAKE_CASE    (			self :    Any    )							->	Any:






     """simple docstring"""







     self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE	(  a_ ):
  """simple docstring"""






  lowerCamelCase    :			str						      ="ru"
  lowerCamelCase    :			Optional[int]						      ="fr"
  lowerCamelCase    :			Dict						      =["Он показал мне рукопись своей новой пьесы."]
  lowerCamelCase    :			int						      =["Il m'a montré le manuscrit de sa nouvelle pièce."]







  @slow
  def        SCREAMING_SNAKE_CASE    (			self :    Optional[Any]    )							->	Union[str, Any]:






     """simple docstring"""







     self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE	(  a_ ):
  """simple docstring"""






  lowerCamelCase    :			Tuple						      ="en"
  lowerCamelCase    :			Union[str, Any]						      ="ROMANCE"
  lowerCamelCase    :			Tuple						      =[
      ">>fr<< Don't spend so much time watching TV.",
      ">>pt<< Your message has been sent.",
      ">>es<< He's two years older than me.",
  ]
  lowerCamelCase    :			Optional[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        SCREAMING_SNAKE_CASE    (			self :    Dict    )							->	Any:






     """simple docstring"""







     self._assert_generated_batch_equal_expected()





from math import sqrt





def 	snake_case_      (__A     :						int     )			->	list[int]:
   __lowerCAmelCase  :			List[str]					    =						[1]
   for i in range(2					,   int(sqrt(__A     )     ) + 1					,   1     ):
      if number % i == 0:
         values.append(__A     )
         if int(number // i     ) != i:
            values.append(int(number // i     )     )
   return sorted(__A     )





def 	snake_case_      (__A     :						int     )			->	bool:
   return sum(factors(__A     )     ) > n





def 	snake_case_      (__A     :						int     )			->	bool:
   __lowerCAmelCase  :			Any					    =						factors(__A     )
   __lowerCAmelCase  :			Union[str, Any]					    =						len(__A     )
   __lowerCAmelCase  :			str					    =						[[0 for i in range(number + 1     )] for j in range(r + 1     )]
   for i in range(r + 1     ):
      __lowerCAmelCase  :			Tuple					    =						True

   for i in range(1					,   number + 1     ):
      __lowerCAmelCase  :			List[str]					    =						False

   for i in range(1					,   r + 1     ):
      for j in range(1					,   number + 1     ):
         if j < values[i - 1]:
            __lowerCAmelCase  :			Dict					    =						subset[i - 1][j]
         else:
            __lowerCAmelCase  :			Dict					    =						subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

   return subset[r][number] != 0





def 	snake_case_      (__A     :						int     )			->	bool:
   return abundant(__A     ) and not semi_perfect(__A     )


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   for number in (69, 70, 71):
      print(F'{number} is {"" if weird(number) else "not "}weird.')


