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