code: 
  
  
  
  
  
  '''simple docstring'''
  
  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      UpperCAmelCase			:
   __lowercase     	=       MarianConfig
   __lowercase     	=       {}
   __lowercase     	=       """gelu"""
   def __init__(       self   :Any		, lowercase_   :Union[str, Any]		, lowercase_   :List[str]=13		, lowercase_   :str=7		, lowercase_   :Optional[Any]=True		, lowercase_   :Optional[int]=False		, lowercase_   :Tuple=99		, lowercase_   :str=32		, lowercase_   :Dict=2		, lowercase_   :int=4		, lowercase_   :Union[str, Any]=37		, lowercase_   :int=0.1		, lowercase_   :Optional[Any]=0.1		, lowercase_   :List[str]=20		, lowercase_   :List[str]=2		, lowercase_   :List[str]=1		, lowercase_   :Tuple=0		, )->							Dict:
       A__       =    parent
       A__       =    batch_size
       A__       =    seq_length
       A__       =    is_training
       A__       =    use_labels
       A__       =    vocab_size
       A__       =    hidden_size
       A__       =    num_hidden_layers
       A__       =    num_attention_heads
       A__       =    intermediate_size
  
       A__       =    hidden_dropout_prob
       A__       =    attention_probs_dropout_prob
       A__       =    max_position_embeddings
       A__       =    eos_token_id
       A__       =    pad_token_id
       A__       =    bos_token_id
   def   UpperCAmelCase_							(       self   :Optional[int]							)->							Optional[int]:
       A__       =    ids_tensor([self.batch_size, self.seq_length - 1]		, self.vocab_size							)
       A__       =    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)		, 1							)
       A__       =    tf.concat([input_ids, eos_tensor]		, axis=1							)
  
       A__       =    ids_tensor([self.batch_size, self.seq_length]		, self.vocab_size							)
  
       A__       =    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		, )
       A__       =    prepare_marian_inputs_dict(lowercase_		, lowercase_		, lowercase_							)
       return config, inputs_dict
  
  
  
   def   UpperCAmelCase_							(       self   :Union[str, Any]		, lowercase_   :str		, lowercase_   :Optional[int]							)->							Dict:
       A__       =    TFMarianModel(config=lowercase_							).get_decoder()
       A__       =    inputs_dict["input_ids"]
  
       A__       =    input_ids[:1, :]
       A__       =    inputs_dict["attention_mask"][:1, :]
       A__       =    inputs_dict["head_mask"]
       A__       =    1
  
       # first forward pass
       A__       =    model(lowercase_		, attention_mask=lowercase_		, head_mask=lowercase_		, use_cache=lowercase_							)
  
       A__       =    outputs.to_tuple()
  
       # create hypothetical next token and extent to next_input_ids
       A__       =    ids_tensor((self.batch_size, 3)		, config.vocab_size							)
       A__       =    tf.cast(ids_tensor((self.batch_size, 3)		, 2							)		, tf.inta							)
  
       # append to next input_ids and
       A__       =    tf.concat([input_ids, next_tokens]		, axis=-1							)
       A__       =    tf.concat([attention_mask, next_attn_mask]		, axis=-1							)
  
       A__       =    model(lowercase_		, attention_mask=lowercase_							)[0]
       A__       =    model(lowercase_		, attention_mask=lowercase_		, past_key_values=lowercase_							)[0]
  
       self.parent.assertEqual(next_tokens.shape[1]		, output_from_past.shape[1]							)
  
       # select random slice
       A__       =    int(ids_tensor((1,)		, output_from_past.shape[-1]							)							)
       A__       =    output_from_no_past[:, -3:, random_slice_idx]
       A__       =    output_from_past[:, :, random_slice_idx]
  
       # test that outputs are equal for slice
       tf.debugging.assert_near(lowercase_		, lowercase_		, rtol=1E-3							)
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      List[str]		, _lowerCamelCase      :      int		, _lowerCamelCase      :      Dict		, _lowerCamelCase      :      int=None		, _lowerCamelCase      :      int=None		, _lowerCamelCase      :      Optional[int]=None		, _lowerCamelCase      :      Any=None		, _lowerCamelCase      :      Optional[int]=None		, ):
      if attention_mask is None:
          A__       =    tf.cast(tf.math.not_equal(lowercase__		, config.pad_token_id )		, tf.inta )
      if decoder_attention_mask is None:
          A__       =    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:
          A__       =    tf.ones((config.encoder_layers, config.encoder_attention_heads) )
      if decoder_head_mask is None:
          A__       =    tf.ones((config.decoder_layers, config.decoder_attention_heads) )
      if cross_attn_head_mask is None:
          A__       =    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      UpperCAmelCase			( lowerCamelCase_    , lowerCamelCase_    , unittest.TestCase       ):
   __lowercase     	=       (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
   __lowercase     	=       (TFMarianMTModel,) if is_tf_available() else ()
   __lowercase     	=       (
       {
           """conversational""": TFMarianMTModel,
           """feature-extraction""": TFMarianModel,
           """summarization""": TFMarianMTModel,
           """text2text-generation""": TFMarianMTModel,
           """translation""": TFMarianMTModel,
       }
       if is_tf_available()
       else {}
   )
   __lowercase     	=       True
   __lowercase     	=       False
   __lowercase     	=       False
   def   UpperCAmelCase_							(       self   :Dict							)->							Optional[int]:
       A__       =    TFMarianModelTester(self							)
       A__       =    ConfigTester(self		, config_class=lowercase_							)
   def   UpperCAmelCase_							(       self   :Optional[int]							)->							Any:
       self.config_tester.run_common_tests()
  
  
  
   def   UpperCAmelCase_							(       self   :Optional[Any]							)->							Union[str, Any]:
       A__       =    self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*lowercase_							)
  
  
  
  
  
  
  @require_tf
  class      UpperCAmelCase			( unittest.TestCase       ):
   __lowercase     	=       1000  # show more chars for failing integration tests
   @classmethod
   def   UpperCAmelCase_							(       cls   :Any							)->							List[str]:
       A__       =    F"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
       return cls
   @cached_property
   def   UpperCAmelCase_							(       self   :Optional[Any]							)->							str:
       return AutoTokenizer.from_pretrained(self.model_name							)
   @property
   def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							Union[str, Any]:
       return self.tokenizer.eos_token_id
   @cached_property
   def   UpperCAmelCase_							(       self   :List[str]							)->							Optional[int]:
       warnings.simplefilter("error"							)
       A__       =    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
       assert isinstance(lowercase_		, lowercase_							)
       A__       =    model.config
       self.assertListEqual(c.bad_words_ids		, [[c.pad_token_id]]							)
       self.assertEqual(c.max_length		, 5_12							)
       self.assertEqual(c.decoder_start_token_id		, c.pad_token_id							)
       return model
   def   UpperCAmelCase_							(       self   :Tuple		, **lowercase_   :Any							)->							Union[str, Any]:
       A__       =    self.translate_src_text(**lowercase_							)
       self.assertListEqual(self.expected_text		, lowercase_							)
  
  
  
   def   UpperCAmelCase_							(       self   :int		, **lowercase_   :List[Any]							)->							List[Any]:
       A__       =    self.tokenizer(self.src_text		, **lowercase_		, padding=lowercase_		, return_tensors="tf"							)
       A__       =    self.model.generate(
           model_inputs.input_ids		, attention_mask=model_inputs.attention_mask		, num_beams=2		, max_length=1_28							)
       A__       =    self.tokenizer.batch_decode(generated_ids.numpy()		, skip_special_tokens=lowercase_							)
       return generated_words
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class      UpperCAmelCase			( lowerCamelCase_       ):
   __lowercase     	=       """mt"""
   __lowercase     	=       """en"""
   __lowercase     	=       ["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
   __lowercase     	=       ["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]
   @unittest.skip("Skipping until #12647 is resolved."							)
   @slow
   def   UpperCAmelCase_							(       self   :Any							)->							Tuple:
       self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class      UpperCAmelCase			( lowerCamelCase_       ):
   __lowercase     	=       """en"""
   __lowercase     	=       """zh"""
   __lowercase     	=       ["""My name is Wolfgang and I live in Berlin"""]
   __lowercase     	=       ["""我叫沃尔夫冈 我住在柏林"""]
   @unittest.skip("Skipping until #12647 is resolved."							)
   @slow
   def   UpperCAmelCase_							(       self   :Any							)->							List[str]:
       self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class      UpperCAmelCase			( lowerCamelCase_       ):
   __lowercase     	=       """en"""
   __lowercase     	=       """ROMANCE"""
   __lowercase     	=       [
       """>>fr<< Don't spend so much time watching TV.""",
       """>>pt<< Your message has been sent.""",
       """>>es<< He's two years older than me.""",
   ]
   __lowercase     	=       [
       """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   UpperCAmelCase_							(       self   :str							)->							Union[str, Any]:
       self._assert_generated_batch_equal_expected()
  
  
  
   @unittest.skip("Skipping until #12647 is resolved."							)
   @slow
   def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							List[str]:
       A__       =    TranslationPipeline(self.model		, self.tokenizer		, framework="tf"							)
       A__       =    pipeline(self.src_text							)
       self.assertEqual(self.expected_text		, [x["translation_text"] for x in output]							)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  import warnings
  
  from ...utils import logging
  from .image_processing_vilt import ViltImageProcessor
  
  
  __lowerCAmelCase							:			Dict				=logging.get_logger(__name__)
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   def __init__(       self   :List[str]		, *lowercase_   :Dict		, **lowercase_   :int							)->							None:
       warnings.warn(
           "The class ViltFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
           " use ViltImageProcessor instead."		, lowercase_		, )
       super().__init__(*lowercase_		, **lowercase_							)
  
  
  
  