code: 
  
  
  def  _lowerCamelCase(			lowercase__			)		->			str:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		return " ".join([word[::-1] for word in input_str.split()]			)
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  
  
  
  
  
  
style_context: 
  
  
  from __future__ import annotations
  
  import unittest
  import warnings
  
  from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
  from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
  from transformers.utils import cached_property
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
  	import tensorflow as tf
  
  	from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel
  
  
  
  
  
  @require_tf
  class    A					:
  							UpperCamelCase_     :							int =MarianConfig
  							UpperCamelCase_     :							List[Any] ={}
  							UpperCamelCase_     :							Optional[int] ='''gelu'''
  
  
  							def __init__(self   ,       lowerCAmelCase   ,       lowerCAmelCase=1_3   ,       lowerCAmelCase=7   ,       lowerCAmelCase=True   ,       lowerCAmelCase=False   ,       lowerCAmelCase=9_9   ,       lowerCAmelCase=3_2   ,       lowerCAmelCase=2   ,       lowerCAmelCase=4   ,       lowerCAmelCase=3_7   ,       lowerCAmelCase=0.1   ,       lowerCAmelCase=0.1   ,       lowerCAmelCase=2_0   ,       lowerCAmelCase=2   ,       lowerCAmelCase=1   ,       lowerCAmelCase=0   ,       ):
  									__lowercase=    parent
  									__lowercase=    batch_size
  									__lowercase=    seq_length
  									__lowercase=    is_training
  									__lowercase=    use_labels
  									__lowercase=    vocab_size
  									__lowercase=    hidden_size
  									__lowercase=    num_hidden_layers
  									__lowercase=    num_attention_heads
  									__lowercase=    intermediate_size
  
  									__lowercase=    hidden_dropout_prob
  									__lowercase=    attention_probs_dropout_prob
  									__lowercase=    max_position_embeddings
  									__lowercase=    eos_token_id
  									__lowercase=    pad_token_id
  									__lowercase=    bos_token_id
  
  
  							def     _A (self      ):
  									__lowercase=    ids_tensor([self.batch_size, self.seq_length - 1]   ,       self.vocab_size      )
  									__lowercase=    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size      )   ,       1      )
  									__lowercase=    tf.concat([input_ids, eos_tensor]   ,       axis=1      )
  
  									__lowercase=    ids_tensor([self.batch_size, self.seq_length]   ,       self.vocab_size      )
  
  									__lowercase=    self.config_cls(
  									    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_ids=[2]   ,       bos_token_id=self.bos_token_id   ,       pad_token_id=self.pad_token_id   ,       decoder_start_token_id=self.pad_token_id   ,       **self.config_updates   ,       )
  									__lowercase=    prepare_marian_inputs_dict(lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      )
  									return config, inputs_dict
  
  
  
  
  
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase      ):
  									__lowercase=    TFMarianModel(config=lowerCAmelCase      ).get_decoder()
  									__lowercase=    inputs_dict['input_ids']
  
  									__lowercase=    input_ids[:1, :]
  									__lowercase=    inputs_dict['attention_mask'][:1, :]
  									__lowercase=    inputs_dict['head_mask']
  									__lowercase=    1
  
  									# first forward pass
  									__lowercase=    model(lowerCAmelCase   ,       attention_mask=lowerCAmelCase   ,       head_mask=lowerCAmelCase   ,       use_cache=lowerCAmelCase      )
  
  									__lowercase,							__lowercase=    outputs.to_tuple()
  
  									# create hypothetical next token and extent to next_input_ids
  									__lowercase=    ids_tensor((self.batch_size, 3)   ,       config.vocab_size      )
  									__lowercase=    tf.cast(ids_tensor((self.batch_size, 3)   ,       2      )   ,       tf.inta      )
  
  									# append to next input_ids and
  									__lowercase=    tf.concat([input_ids, next_tokens]   ,       axis=-1      )
  									__lowercase=    tf.concat([attention_mask, next_attn_mask]   ,       axis=-1      )
  
  									__lowercase=    model(lowerCAmelCase   ,       attention_mask=lowerCAmelCase      )[0]
  									__lowercase=    model(lowerCAmelCase   ,       attention_mask=lowerCAmelCase   ,       past_key_values=lowerCAmelCase      )[0]
  
  									self.parent.assertEqual(next_tokens.shape[1]   ,       output_from_past.shape[1]      )
  
  									# select random slice
  									__lowercase=    int(ids_tensor((1,)   ,       output_from_past.shape[-1]      )      )
  									__lowercase=    output_from_no_past[:, -3:, random_slice_idx]
  									__lowercase=    output_from_past[:, :, random_slice_idx]
  
  									# test that outputs are equal for slice
  									tf.debugging.assert_near(lowerCAmelCase   ,       lowerCAmelCase   ,       rtol=1E-3      )
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__=None     ,				lowercase__=None     ,				lowercase__=None     ,				lowercase__=None     ,				lowercase__=None     ,				)		->			Any:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if attention_mask is None:
  				__lowercase=    tf.cast(tf.math.not_equal(lowercase__     ,				config.pad_token_id			)     ,				tf.inta			)
  		if decoder_attention_mask is None:
  				__lowercase=    tf.concat(
  				    [
  				        tf.ones(decoder_input_ids[:, :1].shape     ,				dtype=tf.inta			),
  				        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]     ,				config.pad_token_id			)     ,				tf.inta			),
  				    ]     ,				axis=-1     ,				)
  		if head_mask is None:
  				__lowercase=    tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
  		if decoder_head_mask is None:
  				__lowercase=    tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
  		if cross_attn_head_mask is None:
  				__lowercase=    tf.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": decoder_attention_mask,
  		    "head_mask": head_mask,
  		    "decoder_head_mask": decoder_head_mask,
  		    "cross_attn_head_mask": cross_attn_head_mask,
  		}
  
  
  
  
  
  @require_tf
  class    A					(     A_      ,       A_      ,       unittest.TestCase				):
  							UpperCamelCase_     :							str =(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
  							UpperCamelCase_     :							Union[str, Any] =(TFMarianMTModel,) if is_tf_available() else ()
  							UpperCamelCase_     :							List[str] =(
  							    {
  							        '''conversational''': TFMarianMTModel,
  							        '''feature-extraction''': TFMarianModel,
  							        '''summarization''': TFMarianMTModel,
  							        '''text2text-generation''': TFMarianMTModel,
  							        '''translation''': TFMarianMTModel,
  							    }
  							    if is_tf_available()
  							    else {}
  							)
  							UpperCamelCase_     :							str =True
  							UpperCamelCase_     :							List[str] =False
  							UpperCamelCase_     :							Optional[int] =False
  
  
  							def     _A (self      ):
  									__lowercase=    TFMarianModelTester(self      )
  									__lowercase=    ConfigTester(self   ,       config_class=lowerCAmelCase      )
  
  
  							def     _A (self      ):
  									self.config_tester.run_common_tests()
  
  
  
  
  
  
  
  							def     _A (self      ):
  									__lowercase=    self.model_tester.prepare_config_and_inputs_for_common()
  									self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase      )
  
  
  
  
  
  @require_tf
  class    A					(     unittest.TestCase				):
  							UpperCamelCase_     :							Tuple =1_000  # show more chars for failing integration tests
  
  
  							@classmethod
  							def     _A (cls      ):
  									__lowercase=    f'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
  									return cls
  
  
  							@cached_property
  							def     _A (self      ):
  									return AutoTokenizer.from_pretrained(self.model_name      )
  
  
  							@property
  							def     _A (self      ):
  									return self.tokenizer.eos_token_id
  
  
  							@cached_property
  							def     _A (self      ):
  									warnings.simplefilter('error'      )
  									__lowercase=    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
  									assert isinstance(lowerCAmelCase   ,       lowerCAmelCase      )
  									__lowercase=    model.config
  									self.assertListEqual(c.bad_words_ids   ,       [[c.pad_token_id]]      )
  									self.assertEqual(c.max_length   ,       5_1_2      )
  									self.assertEqual(c.decoder_start_token_id   ,       c.pad_token_id      )
  									return model
  
  
  							def     _A (self   ,       **lowerCAmelCase      ):
  									__lowercase=    self.translate_src_text(**lowerCAmelCase      )
  									self.assertListEqual(self.expected_text   ,       lowerCAmelCase      )
  
  
  
  
  
  
  
  							def     _A (self   ,       **lowerCAmelCase      ):
  									__lowercase=    self.tokenizer(self.src_text   ,       **lowerCAmelCase   ,       padding=lowerCAmelCase   ,       return_tensors='tf'      )
  									__lowercase=    self.model.generate(
  									    model_inputs.input_ids   ,       attention_mask=model_inputs.attention_mask   ,       num_beams=2   ,       max_length=1_2_8      )
  									__lowercase=    self.tokenizer.batch_decode(generated_ids.numpy()   ,       skip_special_tokens=lowerCAmelCase      )
  									return generated_words
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class    A					(     A_				):
  							UpperCamelCase_     :							Union[str, Any] ='''mt'''
  							UpperCamelCase_     :							Tuple ='''en'''
  							UpperCamelCase_     :							Optional[Any] =['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
  							UpperCamelCase_     :							List[str] =['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']
  
  
  							@unittest.skip('Skipping until #12647 is resolved.'      )
  							@slow
  							def     _A (self      ):
  									self._assert_generated_batch_equal_expected()
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class    A					(     A_				):
  							UpperCamelCase_     :							List[Any] ='''en'''
  							UpperCamelCase_     :							str ='''zh'''
  							UpperCamelCase_     :							Dict =['''My name is Wolfgang and I live in Berlin''']
  							UpperCamelCase_     :							Optional[int] =['''我叫沃尔夫冈 我住在柏林''']
  
  
  							@unittest.skip('Skipping until #12647 is resolved.'      )
  							@slow
  							def     _A (self      ):
  									self._assert_generated_batch_equal_expected()
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class    A					(     A_				):
  							UpperCamelCase_     :							Tuple ='''en'''
  							UpperCamelCase_     :							List[Any] ='''ROMANCE'''
  							UpperCamelCase_     :							int =[
  							    '''>>fr<< Don\'t spend so much time watching TV.''',
  							    '''>>pt<< Your message has been sent.''',
  							    '''>>es<< He\'s two years older than me.''',
  							]
  							UpperCamelCase_     :							str =[
  							    '''Ne passez pas autant de temps à regarder la télé.''',
  							    '''A sua mensagem foi enviada.''',
  							    '''Es dos años más viejo que yo.''',
  							]
  
  
  							@unittest.skip('Skipping until #12647 is resolved.'      )
  							@slow
  							def     _A (self      ):
  									self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  
  							@unittest.skip('Skipping until #12647 is resolved.'      )
  							@slow
  							def     _A (self      ):
  									__lowercase=    TranslationPipeline(self.model   ,       self.tokenizer   ,       framework='tf'      )
  									__lowercase=    pipeline(self.src_text      )
  									self.assertEqual(self.expected_text   ,       [x['translation_text'] for x in output]      )
  
  
  
  
  
  
  