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
  	lowercase__    :       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_  (		snake_case__     ,						snake_case__     ,						snake_case__=None     ,						snake_case__=None     ,						snake_case__=None     ,						snake_case__=None     ,						snake_case__=None     ,						snake_case__=None     ,						)  ->			Tuple:
  							if attention_mask is None:
  														lowerCAmelCase				=	np.where(input_ids != config.pad_token_id     ,						1     ,						0							)
  							if decoder_attention_mask is None:
  														lowerCAmelCase				=	np.where(decoder_input_ids != config.pad_token_id     ,						1     ,						0							)
  							if head_mask is None:
  														lowerCAmelCase				=	np.ones((config.encoder_layers, config.encoder_attention_heads)							)
  							if decoder_head_mask is None:
  														lowerCAmelCase				=	np.ones((config.decoder_layers, config.decoder_attention_heads)							)
  							if cross_attn_head_mask is None:
  														lowerCAmelCase				=	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	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=13	,    __SCREAMING_SNAKE_CASE=7	,    __SCREAMING_SNAKE_CASE=True	,    __SCREAMING_SNAKE_CASE=False	,    __SCREAMING_SNAKE_CASE=99	,    __SCREAMING_SNAKE_CASE=16	,    __SCREAMING_SNAKE_CASE=2	,    __SCREAMING_SNAKE_CASE=4	,    __SCREAMING_SNAKE_CASE=4	,    __SCREAMING_SNAKE_CASE="gelu"	,    __SCREAMING_SNAKE_CASE=0.1	,    __SCREAMING_SNAKE_CASE=0.1	,    __SCREAMING_SNAKE_CASE=32	,    __SCREAMING_SNAKE_CASE=2	,    __SCREAMING_SNAKE_CASE=1	,    __SCREAMING_SNAKE_CASE=0	,    __SCREAMING_SNAKE_CASE=0.0_2	,    )		->Dict:
  								lowerCAmelCase				=	parent
  								lowerCAmelCase				=	batch_size
  								lowerCAmelCase				=	seq_length
  								lowerCAmelCase				=	is_training
  								lowerCAmelCase				=	use_labels
  								lowerCAmelCase				=	vocab_size
  								lowerCAmelCase				=	hidden_size
  								lowerCAmelCase				=	num_hidden_layers
  								lowerCAmelCase				=	num_attention_heads
  								lowerCAmelCase				=	intermediate_size
  								lowerCAmelCase				=	hidden_act
  								lowerCAmelCase				=	hidden_dropout_prob
  								lowerCAmelCase				=	attention_probs_dropout_prob
  								lowerCAmelCase				=	max_position_embeddings
  								lowerCAmelCase				=	eos_token_id
  								lowerCAmelCase				=	pad_token_id
  								lowerCAmelCase				=	bos_token_id
  								lowerCAmelCase				=	initializer_range
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->List[str]:
  								lowerCAmelCase				=	np.clip(ids_tensor([self.batch_size, self.seq_length - 1]	,    self.vocab_size       )	,    3	,    self.vocab_size       )
  								lowerCAmelCase				=	np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)	,    dtype=np.intaa       ))	,    -1       )
  
  								lowerCAmelCase				=	shift_tokens_right(__SCREAMING_SNAKE_CASE	,    1	,    2       )
  
  								lowerCAmelCase				=	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	,    )
  								lowerCAmelCase				=	prepare_marian_inputs_dict(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
  								return config, inputs_dict
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->str:
  								lowerCAmelCase				=	self.prepare_config_and_inputs()
  								return config, inputs_dict
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )		->List[str]:
  								lowerCAmelCase				=	20
  								lowerCAmelCase				=	model_class_name(__SCREAMING_SNAKE_CASE       )
  
  								lowerCAmelCase				=	model.encode(inputs_dict['''input_ids''']       )
  
  								lowerCAmelCase				=	(
  								    inputs_dict["decoder_input_ids"],
  								    inputs_dict["decoder_attention_mask"],
  								)
  
  								lowerCAmelCase				=	model.init_cache(decoder_input_ids.shape[0]	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)	,    dtype='''i4'''       )
  
  								lowerCAmelCase				=	jnp.broadcast_to(
  								    jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :]	,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,    )
  								lowerCAmelCase				=	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	,    )
  
  								lowerCAmelCase				=	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,    dtype='''i4'''       )
  								lowerCAmelCase				=	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	,    )
  
  								lowerCAmelCase				=	model.decode(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
  
  								lowerCAmelCase				=	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	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )		->Tuple:
  								lowerCAmelCase				=	20
  								lowerCAmelCase				=	model_class_name(__SCREAMING_SNAKE_CASE       )
  
  								lowerCAmelCase				=	model.encode(inputs_dict['''input_ids''']       )
  
  								lowerCAmelCase				=	(
  								    inputs_dict["decoder_input_ids"],
  								    inputs_dict["decoder_attention_mask"],
  								)
  
  								lowerCAmelCase				=	jnp.concatenate(
  								    [
  								        decoder_attention_mask,
  								        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])       ),
  								    ]	,    axis=-1	,    )
  
  								lowerCAmelCase				=	model.init_cache(decoder_input_ids.shape[0]	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	jnp.broadcast_to(
  								    jnp.arange(decoder_input_ids.shape[-1] - 1       )[None, :]	,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)	,    )
  
  								lowerCAmelCase				=	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	,    )
  								lowerCAmelCase				=	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]	,    dtype='''i4'''       )
  								lowerCAmelCase				=	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	,    )
  
  								lowerCAmelCase				=	model.decode(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    decoder_attention_mask=__SCREAMING_SNAKE_CASE       )
  
  								lowerCAmelCase				=	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_						(						A__   ,  unittest.TestCase   ,  A__		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      Union[str, Any]     =       True
  	UpperCAmelCase_						:      int     =       (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
  	UpperCAmelCase_						:      List[str]     =       (FlaxMarianMTModel,) if is_flax_available() else ()
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->str:
  								lowerCAmelCase				=	FlaxMarianModelTester(self       )
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Optional[Any]:
  								lowerCAmelCase				=	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     SCREAMING_SNAKE_CASE_       (							self       )		->Optional[int]:
  								lowerCAmelCase				=	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     SCREAMING_SNAKE_CASE_       (							self       )		->str:
  								lowerCAmelCase				=	self.model_tester.prepare_config_and_inputs_for_common()
  
  								for model_class in self.all_model_classes:
  															with self.subTest(model_class.__name__       ):
  																						lowerCAmelCase				=	self._prepare_for_class(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
  																						lowerCAmelCase				=	model_class(__SCREAMING_SNAKE_CASE       )
  
  																						@jax.jit
  																						def encode_jitted(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=None	,    **__SCREAMING_SNAKE_CASE       ):
  																													return model.encode(input_ids=__SCREAMING_SNAKE_CASE	,    attention_mask=__SCREAMING_SNAKE_CASE       )
  
  																						with self.subTest('''JIT Enabled'''       ):
  																													lowerCAmelCase				=	encode_jitted(**__SCREAMING_SNAKE_CASE       ).to_tuple()
  
  																						with self.subTest('''JIT Disabled'''       ):
  																													with jax.disable_jit():
  																																				lowerCAmelCase				=	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     SCREAMING_SNAKE_CASE_       (							self       )		->str:
  								lowerCAmelCase				=	self.model_tester.prepare_config_and_inputs_for_common()
  
  								for model_class in self.all_model_classes:
  															with self.subTest(model_class.__name__       ):
  																						lowerCAmelCase				=	model_class(__SCREAMING_SNAKE_CASE       )
  																						lowerCAmelCase				=	model.encode(inputs_dict['''input_ids''']	,    inputs_dict['''attention_mask''']       )
  
  																						lowerCAmelCase				=	{
  																						    "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	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ):
  																													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'''       ):
  																													lowerCAmelCase				=	decode_jitted(**__SCREAMING_SNAKE_CASE       ).to_tuple()
  
  																						with self.subTest('''JIT Disabled'''       ):
  																													with jax.disable_jit():
  																																				lowerCAmelCase				=	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     SCREAMING_SNAKE_CASE_       (							self       )		->Tuple:
  
  
  
  
  
  
  								for model_class_name in self.all_model_classes:
  															lowerCAmelCase				=	model_class_name.from_pretrained('''Helsinki-NLP/opus-mt-en-de'''       )
  															# FlaxMarianForSequenceClassification expects eos token in input_ids
  															lowerCAmelCase				=	np.ones((1, 1)       ) * model.config.eos_token_id
  															lowerCAmelCase				=	model(__SCREAMING_SNAKE_CASE       )
  															self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class      lowercase_						(						unittest.TestCase		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      Any     =       None
  	UpperCAmelCase_						:      Tuple     =       None
  
  
  	@classmethod
  	def     SCREAMING_SNAKE_CASE_       (							cls       )		->List[Any]:
  								lowerCAmelCase				=	F"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
  								return cls
  
  
  	@cached_property
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->int:
  								return MarianTokenizer.from_pretrained(self.model_name       )
  
  
  	@property
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Tuple:
  								return self.tokenizer.eos_token_id
  
  
  	@cached_property
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Dict:
  								lowerCAmelCase				=	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	,    **__SCREAMING_SNAKE_CASE       )		->int:
  								lowerCAmelCase				=	self.translate_src_text(**__SCREAMING_SNAKE_CASE       )
  								self.assertListEqual(self.expected_text	,    __SCREAMING_SNAKE_CASE       )
  
  
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    **__SCREAMING_SNAKE_CASE       )		->Union[str, Any]:
  								lowerCAmelCase				=	self.tokenizer(self.src_text	,    padding=__SCREAMING_SNAKE_CASE	,    return_tensors='''np'''	,    **__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	self.model.generate(
  								    model_inputs.input_ids	,    attention_mask=model_inputs.attention_mask	,    num_beams=2	,    max_length=128	,    ).sequences
  								lowerCAmelCase				=	self.tokenizer.batch_decode(__SCREAMING_SNAKE_CASE	,    skip_special_tokens=__SCREAMING_SNAKE_CASE       )
  								return generated_words
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class      lowercase_						(						A__		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      int     =       '''en'''
  	UpperCAmelCase_						:      List[Any]     =       '''fr'''
  	UpperCAmelCase_						:      int     =       [
  	    '''I am a small frog.''',
  	    '''Now I can forget the 100 words of german that I know.''',
  	]
  	UpperCAmelCase_						:      List[str]     =       [
  	    '''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]:
  								self._assert_generated_batch_equal_expected()
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class      lowercase_						(						A__		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      int     =       '''fr'''
  	UpperCAmelCase_						:      List[Any]     =       '''en'''
  	UpperCAmelCase_						:      Optional[Any]     =       [
  	    '''Donnez moi le micro.''',
  	    '''Tom et Mary étaient assis à une table.''',  # Accents
  	]
  	UpperCAmelCase_						:      str     =       [
  	    '''Give me the microphone.''',
  	    '''Tom and Mary were sitting at a table.''',
  	]
  
  
  	@slow
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->int:
  								self._assert_generated_batch_equal_expected()
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class      lowercase_						(						A__		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      Optional[int]     =       '''mt'''
  	UpperCAmelCase_						:      Tuple     =       '''en'''
  	UpperCAmelCase_						:      Optional[int]     =       ['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
  	UpperCAmelCase_						:      int     =       ['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']
  
  
  	@slow
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->int:
  								self._assert_generated_batch_equal_expected()
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class      lowercase_						(						A__		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      List[str]     =       '''en'''
  	UpperCAmelCase_						:      Any     =       '''de'''
  	UpperCAmelCase_						:      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.''',
  	]
  	UpperCAmelCase_						:      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     SCREAMING_SNAKE_CASE_       (							self       )		->Optional[int]:
  								self._assert_generated_batch_equal_expected()
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class      lowercase_						(						A__		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      List[Any]     =       '''en'''
  	UpperCAmelCase_						:      List[Any]     =       '''zh'''
  	UpperCAmelCase_						:      Optional[int]     =       ['''My name is Wolfgang and I live in Berlin''']
  	UpperCAmelCase_						:      int     =       ['''我叫沃尔夫冈 我住在柏林''']
  
  
  	@slow
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Union[str, Any]:
  								self._assert_generated_batch_equal_expected()
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class      lowercase_						(						A__		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      int     =       '''ru'''
  	UpperCAmelCase_						:      List[Any]     =       '''fr'''
  	UpperCAmelCase_						:      List[Any]     =       ['''Он показал мне рукопись своей новой пьесы.''']
  	UpperCAmelCase_						:      List[str]     =       ['''Il m\'a montré le manuscrit de sa nouvelle pièce.''']
  
  
  	@slow
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Tuple:
  								self._assert_generated_batch_equal_expected()
  
  
  
  
  @require_flax
  @require_sentencepiece
  @require_tokenizers
  class      lowercase_						(						A__		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      str     =       '''en'''
  	UpperCAmelCase_						:      List[str]     =       '''ROMANCE'''
  	UpperCAmelCase_						:      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.''',
  	]
  	UpperCAmelCase_						:      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       )		->Optional[Any]:
  								self._assert_generated_batch_equal_expected()
  
  
  
  
style_context: import math
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			float:
  							lowerCAmelCase				=	math.log(2     ,						1_0							) * number
  							lowerCAmelCase				=	round((log_number - int(snake_case__							))     ,						1_5							)
  
  							return difference
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__ = 6_7_8_9_1_0							)  ->			int:
  							lowerCAmelCase				=	9_0
  							lowerCAmelCase				=	0
  
  							lowerCAmelCase				=	math.log(1.23     ,						1_0							)
  							lowerCAmelCase				=	math.log(1.24     ,						1_0							)
  							lowerCAmelCase				=	0
  
  							while position < number:
  														lowerCAmelCase				=	log_difference(snake_case__							)
  
  														if difference >= upper_limit:
  																					power_iterator += 9_3
  
  														elif difference < lower_limit:
  																					power_iterator += 1_9_6
  
  														else:
  																					lowerCAmelCase				=	power_iterator
  																					power_iterator += 1_9_6
  																					position += 1
  
  							return previous_power
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  	print(f'{solution() = }')
  
  
  
  