code: 
  
  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				:
      lowerCAmelCase_							:		List[Any]						=							MarianConfig
      lowerCAmelCase_							:		Dict						=							{}
      lowerCAmelCase_							:		Optional[int]						=							"gelu"
  
  
  
  
  
  
      def __init__(	self			:    int    ,				UpperCAmelCase_			:    List[str]    ,				UpperCAmelCase_			:    Optional[int]=13    ,				UpperCAmelCase_			:    List[str]=7    ,				UpperCAmelCase_			:    int=True    ,				UpperCAmelCase_			:    Optional[Any]=False    ,				UpperCAmelCase_			:    List[Any]=99    ,				UpperCAmelCase_			:    Optional[Any]=32    ,				UpperCAmelCase_			:    List[Any]=2    ,				UpperCAmelCase_			:    str=4    ,				UpperCAmelCase_			:    Dict=37    ,				UpperCAmelCase_			:    int=0.1    ,				UpperCAmelCase_			:    str=0.1    ,				UpperCAmelCase_			:    List[str]=20    ,				UpperCAmelCase_			:    str=2    ,				UpperCAmelCase_			:    Any=1    ,				UpperCAmelCase_			:    List[str]=0    ,				):
          lowerCAmelCase				:			Optional[int]    			=      parent
          lowerCAmelCase				:			Any    			=      batch_size
          lowerCAmelCase				:			str    			=      seq_length
          lowerCAmelCase				:			Tuple    			=      is_training
          lowerCAmelCase				:			List[Any]    			=      use_labels
          lowerCAmelCase				:			int    			=      vocab_size
          lowerCAmelCase				:			Union[str, Any]    			=      hidden_size
          lowerCAmelCase				:			str    			=      num_hidden_layers
          lowerCAmelCase				:			Union[str, Any]    			=      num_attention_heads
          lowerCAmelCase				:			int    			=      intermediate_size
  
          lowerCAmelCase				:			Any    			=      hidden_dropout_prob
          lowerCAmelCase				:			str    			=      attention_probs_dropout_prob
          lowerCAmelCase				:			List[str]    			=      max_position_embeddings
          lowerCAmelCase				:			Union[str, Any]    			=      eos_token_id
          lowerCAmelCase				:			int    			=      pad_token_id
          lowerCAmelCase				:			Tuple    			=      bos_token_id
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Any    ):
          lowerCAmelCase				:			Dict    			=      ids_tensor([self.batch_size, self.seq_length - 1]    ,				self.vocab_size    )
          lowerCAmelCase				:			List[str]    			=      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size    )    ,				1    )
          lowerCAmelCase				:			Dict    			=      tf.concat([input_ids, eos_tensor]    ,				axis=1    )
  
          lowerCAmelCase				:			int    			=      ids_tensor([self.batch_size, self.seq_length]    ,				self.vocab_size    )
  
          lowerCAmelCase				:			Tuple    			=      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    ,				)
          lowerCAmelCase				:			Tuple    			=      prepare_marian_inputs_dict(UpperCAmelCase_    ,				UpperCAmelCase_    ,				UpperCAmelCase_    )
          return config, inputs_dict
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Optional[int]    ,				UpperCAmelCase_			:    str    ,				UpperCAmelCase_			:    Optional[Any]    ):
          lowerCAmelCase				:			Dict    			=      TFMarianModel(config=UpperCAmelCase_    ).get_decoder()
          lowerCAmelCase				:			List[Any]    			=      inputs_dict['input_ids']
  
          lowerCAmelCase				:			Optional[int]    			=      input_ids[:1, :]
          lowerCAmelCase				:			Optional[Any]    			=      inputs_dict['attention_mask'][:1, :]
          lowerCAmelCase				:			int    			=      inputs_dict['head_mask']
          lowerCAmelCase				:			int    			=      1
  
          # first forward pass
          lowerCAmelCase				:			Optional[Any]    			=      model(UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				head_mask=UpperCAmelCase_    ,				use_cache=UpperCAmelCase_    )
  
          lowerCAmelCase			,  lowerCAmelCase				:			Union[str, Any]    			=      outputs.to_tuple()
  
          # create hypothetical next token and extent to next_input_ids
          lowerCAmelCase				:			Any    			=      ids_tensor((self.batch_size, 3)    ,				config.vocab_size    )
          lowerCAmelCase				:			str    			=      tf.cast(ids_tensor((self.batch_size, 3)    ,				2    )    ,				tf.inta    )
  
          # append to next input_ids and
          lowerCAmelCase				:			str    			=      tf.concat([input_ids, next_tokens]    ,				axis=-1    )
          lowerCAmelCase				:			int    			=      tf.concat([attention_mask, next_attn_mask]    ,				axis=-1    )
  
          lowerCAmelCase				:			int    			=      model(UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    )[0]
          lowerCAmelCase				:			Any    			=      model(UpperCAmelCase_    ,				attention_mask=UpperCAmelCase_    ,				past_key_values=UpperCAmelCase_    )[0]
  
          self.parent.assertEqual(next_tokens.shape[1]    ,				output_from_past.shape[1]    )
  
          # select random slice
          lowerCAmelCase				:			Union[str, Any]    			=      int(ids_tensor((1,)    ,				output_from_past.shape[-1]    )    )
          lowerCAmelCase				:			Dict    			=      output_from_no_past[:, -3:, random_slice_idx]
          lowerCAmelCase				:			List[Any]    			=      output_from_past[:, :, random_slice_idx]
  
          # test that outputs are equal for slice
          tf.debugging.assert_near(UpperCAmelCase_    ,				UpperCAmelCase_    ,				rtol=1E-3    )
  
  
  
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase=None,      _UpperCAmelCase=None,      _UpperCAmelCase=None,      _UpperCAmelCase=None,      _UpperCAmelCase=None,      )					-> Dict:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      if attention_mask is None:
          lowerCAmelCase				:			Optional[Any]    			=      tf.cast(tf.math.not_equal(_UpperCAmelCase,      config.pad_token_id				),      tf.inta				)
      if decoder_attention_mask is None:
          lowerCAmelCase				:			Union[str, Any]    			=      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:
          lowerCAmelCase				:			List[str]    			=      tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
      if decoder_head_mask is None:
          lowerCAmelCase				:			Dict    			=      tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
      if cross_attn_head_mask is None:
          lowerCAmelCase				:			Union[str, Any]    			=      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				(							lowerCAmelCase						,						lowerCAmelCase						,						unittest.TestCase ):
      lowerCAmelCase_							:		Any						=							(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
      lowerCAmelCase_							:		Optional[Any]						=							(TFMarianMTModel,) if is_tf_available() else ()
      lowerCAmelCase_							:		Union[str, Any]						=							(
          {
              "conversational": TFMarianMTModel,
              "feature-extraction": TFMarianModel,
              "summarization": TFMarianMTModel,
              "text2text-generation": TFMarianMTModel,
              "translation": TFMarianMTModel,
          }
          if is_tf_available()
          else {}
      )
      lowerCAmelCase_							:		int						=							True
      lowerCAmelCase_							:		Union[str, Any]						=							False
      lowerCAmelCase_							:		List[Any]						=							False
  
  
  
  
  
  
      def 	lowercase__ (	self			:    str    ):
          lowerCAmelCase				:			Tuple    			=      TFMarianModelTester(self    )
          lowerCAmelCase				:			List[Any]    			=      ConfigTester(self    ,				config_class=UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    int    ):
          self.config_tester.run_common_tests()
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[str]    ):
          lowerCAmelCase				:			Optional[Any]    			=      self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase_    )
  
  
  
  
  
  
  @require_tf
  class    __A				(							unittest.TestCase ):
      lowerCAmelCase_							:		Optional[int]						=							1000  # show more chars for failing integration tests
  
  
  
  
  
  
      @classmethod
      def 	lowercase__ (	cls			:    Dict    ):
          lowerCAmelCase				:			Optional[Any]    			=      f"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
          return cls
  
  
  
  
  
  
      @cached_property
      def 	lowercase__ (	self			:    List[str]    ):
          return AutoTokenizer.from_pretrained(self.model_name    )
  
  
  
  
  
  
      @property
      def 	lowercase__ (	self			:    int    ):
          return self.tokenizer.eos_token_id
  
  
  
  
  
  
      @cached_property
      def 	lowercase__ (	self			:    Optional[int]    ):
          warnings.simplefilter('error'    )
          lowerCAmelCase				:			TFMarianMTModel    			=      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name    )
          assert isinstance(UpperCAmelCase_    ,				UpperCAmelCase_    )
          lowerCAmelCase				:			Any    			=      model.config
          self.assertListEqual(c.bad_words_ids    ,				[[c.pad_token_id]]    )
          self.assertEqual(c.max_length    ,				512    )
          self.assertEqual(c.decoder_start_token_id    ,				c.pad_token_id    )
          return model
  
  
  
  
  
  
      def 	lowercase__ (	self			:    int    ,				**UpperCAmelCase_			:    Optional[Any]    ):
          lowerCAmelCase				:			Any    			=      self.translate_src_text(**UpperCAmelCase_    )
          self.assertListEqual(self.expected_text    ,				UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[str]    ,				**UpperCAmelCase_			:    Optional[Any]    ):
          lowerCAmelCase				:			Tuple    			=      self.tokenizer(self.src_text    ,				**UpperCAmelCase_    ,				padding=UpperCAmelCase_    ,				return_tensors='tf'    )
          lowerCAmelCase				:			Union[str, Any]    			=      self.model.generate(
              model_inputs.input_ids    ,				attention_mask=model_inputs.attention_mask    ,				num_beams=2    ,				max_length=128    )
          lowerCAmelCase				:			Union[str, Any]    			=      self.tokenizer.batch_decode(generated_ids.numpy()    ,				skip_special_tokens=UpperCAmelCase_    )
          return generated_words
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class    __A				(							lowerCAmelCase ):
      lowerCAmelCase_							:		Optional[Any]						=							"mt"
      lowerCAmelCase_							:		Dict						=							"en"
      lowerCAmelCase_							:		Union[str, Any]						=							["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
      lowerCAmelCase_							:		Dict						=							["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]
  
  
  
  
  
  
      @unittest.skip('Skipping until #12647 is resolved.'    )
      @slow
      def 	lowercase__ (	self			:    Tuple    ):
          self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class    __A				(							lowerCAmelCase ):
      lowerCAmelCase_							:		Optional[Any]						=							"en"
      lowerCAmelCase_							:		int						=							"zh"
      lowerCAmelCase_							:		int						=							["My name is Wolfgang and I live in Berlin"]
      lowerCAmelCase_							:		str						=							["我叫沃尔夫冈 我住在柏林"]
  
  
  
  
  
  
      @unittest.skip('Skipping until #12647 is resolved.'    )
      @slow
      def 	lowercase__ (	self			:    List[Any]    ):
          self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class    __A				(							lowerCAmelCase ):
      lowerCAmelCase_							:		str						=							"en"
      lowerCAmelCase_							:		Tuple						=							"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_							:		Union[str, Any]						=							[
          "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 	lowercase__ (	self			:    Union[str, Any]    ):
          self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
      @unittest.skip('Skipping until #12647 is resolved.'    )
      @slow
      def 	lowercase__ (	self			:    Dict    ):
          lowerCAmelCase				:			Optional[Any]    			=      TranslationPipeline(self.model    ,				self.tokenizer    ,				framework='tf'    )
          lowerCAmelCase				:			Tuple    			=      pipeline(self.src_text    )
          self.assertEqual(self.expected_text    ,				[x['translation_text'] for x in output]    )
  
  
  
  
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  __A   :     Optional[int]      =			{
      '''configuration_splinter''': ['''SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SplinterConfig'''],
      '''tokenization_splinter''': ['''SplinterTokenizer'''],
  }
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     Optional[int]      =			['''SplinterTokenizerFast''']
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     Union[str, Any]      =			[
       '''SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''SplinterForQuestionAnswering''',
       '''SplinterForPreTraining''',
       '''SplinterLayer''',
       '''SplinterModel''',
       '''SplinterPreTrainedModel''',
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
   from .tokenization_splinter import SplinterTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_splinter_fast import SplinterTokenizerFast
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_splinter import (
        SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
        SplinterForPreTraining,
        SplinterForQuestionAnswering,
        SplinterLayer,
        SplinterModel,
        SplinterPreTrainedModel,
    )
  
  
  else:
   import sys
  
   __A   :     Optional[Any]      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  