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    :
        _SCREAMING_SNAKE_CASE					=							MarianConfig
        _SCREAMING_SNAKE_CASE					=							{}
        _SCREAMING_SNAKE_CASE					=							"""gelu"""
  
  
  
        def __init__(					self     ,  lowercase_     ,  lowercase_=13     ,  lowercase_=7     ,  lowercase_=True     ,  lowercase_=False     ,  lowercase_=99     ,  lowercase_=32     ,  lowercase_=2     ,  lowercase_=4     ,  lowercase_=37     ,  lowercase_=0.1     ,  lowercase_=0.1     ,  lowercase_=20     ,  lowercase_=2     ,  lowercase_=1     ,  lowercase_=0     ,  )      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Union[str, Any]     				=						parent
               _snake_case      :							int     				=						batch_size
               _snake_case      :							int     				=						seq_length
               _snake_case      :							Any     				=						is_training
               _snake_case      :							Dict     				=						use_labels
               _snake_case      :							List[Any]     				=						vocab_size
               _snake_case      :							Any     				=						hidden_size
               _snake_case      :							int     				=						num_hidden_layers
               _snake_case      :							Optional[Any]     				=						num_attention_heads
               _snake_case      :							str     				=						intermediate_size
  
               _snake_case      :							Tuple     				=						hidden_dropout_prob
               _snake_case      :							List[Any]     				=						attention_probs_dropout_prob
               _snake_case      :							Dict     				=						max_position_embeddings
               _snake_case      :							Union[str, Any]     				=						eos_token_id
               _snake_case      :							List[str]     				=						pad_token_id
               _snake_case      :							List[str]     				=						bos_token_id
  
  
  
        def    __a (					self					)      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[str]     				=						ids_tensor([self.batch_size, self.seq_length - 1]     ,  self.vocab_size					)
               _snake_case      :							List[str]     				=						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size					)     ,  1					)
               _snake_case      :							str     				=						tf.concat([input_ids, eos_tensor]     ,  axis=1					)
  
               _snake_case      :							Tuple     				=						ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size					)
  
               _snake_case      :							str     				=						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      :							Optional[Any]     				=						prepare_marian_inputs_dict(lowercase_     ,  lowercase_     ,  lowercase_					)
               return config, inputs_dict
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   List[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Any     				=						TFMarianModel(config=lowercase_					).get_decoder()
               _snake_case      :							int     				=						inputs_dict['''input_ids''']
  
               _snake_case      :							Optional[Any]     				=						input_ids[:1, :]
               _snake_case      :							int     				=						inputs_dict['''attention_mask'''][:1, :]
               _snake_case      :							Tuple     				=						inputs_dict['''head_mask''']
               _snake_case      :							List[Any]     				=						1
  
               # first forward pass
               _snake_case      :							int     				=						model(lowercase_     ,  attention_mask=lowercase_     ,  head_mask=lowercase_     ,  use_cache=lowercase_					)
  
               _snake_case				,    _snake_case      :							Optional[int]     				=						outputs.to_tuple()
  
               # create hypothetical next token and extent to next_input_ids
               _snake_case      :							Union[str, Any]     				=						ids_tensor((self.batch_size, 3)     ,  config.vocab_size					)
               _snake_case      :							Union[str, Any]     				=						tf.cast(ids_tensor((self.batch_size, 3)     ,  2					)     ,  tf.inta					)
  
               # append to next input_ids and
               _snake_case      :							Dict     				=						tf.concat([input_ids, next_tokens]     ,  axis=-1					)
               _snake_case      :							Optional[int]     				=						tf.concat([attention_mask, next_attn_mask]     ,  axis=-1					)
  
               _snake_case      :							List[Any]     				=						model(lowercase_     ,  attention_mask=lowercase_					)[0]
               _snake_case      :							str     				=						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
               _snake_case      :							int     				=						int(ids_tensor((1,)     ,  output_from_past.shape[-1]					)					)
               _snake_case      :							Union[str, Any]     				=						output_from_no_past[:, -3:, random_slice_idx]
               _snake_case      :							str     				=						output_from_past[:, :, random_slice_idx]
  
               # test that outputs are equal for slice
               tf.debugging.assert_near(lowercase_     ,  lowercase_     ,  rtol=1E-3					)
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_=None      ,		lowercase_=None      ,		lowercase_=None      ,		lowercase_=None      ,		lowercase_=None      ,		)			->						Optional[int]:
         if attention_mask is None:
                _snake_case      :							str     				=						tf.cast(tf.math.not_equal(lowercase_      ,		config.pad_token_id  )      ,		tf.inta  )
         if decoder_attention_mask is None:
                _snake_case      :							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:
                _snake_case      :							List[Any]     				=						tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
         if decoder_head_mask is None:
                _snake_case      :							Union[str, Any]     				=						tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
         if cross_attn_head_mask is None:
                _snake_case      :							List[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    (__UpperCAmelCase       ,__UpperCAmelCase       ,unittest.TestCase				):
        _SCREAMING_SNAKE_CASE					=							(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
        _SCREAMING_SNAKE_CASE					=							(TFMarianMTModel,) if is_tf_available() else ()
        _SCREAMING_SNAKE_CASE					=							(
            {
                """conversational""": TFMarianMTModel,
                """feature-extraction""": TFMarianModel,
                """summarization""": TFMarianMTModel,
                """text2text-generation""": TFMarianMTModel,
                """translation""": TFMarianMTModel,
            }
            if is_tf_available()
            else {}
        )
        _SCREAMING_SNAKE_CASE					=							True
        _SCREAMING_SNAKE_CASE					=							False
        _SCREAMING_SNAKE_CASE					=							False
  
  
  
        def    __a (					self					)      ->   List[str]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Union[str, Any]     				=						TFMarianModelTester(self					)
               _snake_case      :							List[Any]     				=						ConfigTester(self     ,  config_class=lowercase_					)
  
  
  
        def    __a (					self					)      ->   Any:
  
  
  
               '''simple docstring'''
  
               self.config_tester.run_common_tests()
  
  
  
        def    __a (					self					)      ->   Any:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[int]     				=						self.model_tester.prepare_config_and_inputs_for_common()
               self.model_tester.check_decoder_model_past_large_inputs(*lowercase_					)
  
  
  @require_tf
  class 				A    (unittest.TestCase				):
        _SCREAMING_SNAKE_CASE					=							1_000  # show more chars for failing integration tests
  
  
  
        @classmethod
        def    __a (					cls					)      ->   None:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							str     				=						F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
               return cls
  
  
  
        @cached_property
        def    __a (					self					)      ->   MarianTokenizer:
  
  
  
               '''simple docstring'''
  
               return AutoTokenizer.from_pretrained(self.model_name					)
  
  
  
        @property
        def    __a (					self					)      ->   int:
  
  
  
               '''simple docstring'''
  
               return self.tokenizer.eos_token_id
  
  
  
        @cached_property
        def    __a (					self					)      ->   List[str]:
  
  
  
               '''simple docstring'''
  
               warnings.simplefilter('''error'''					)
               _snake_case      :							TFMarianMTModel     				=						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
               assert isinstance(lowercase_     ,  lowercase_					)
               _snake_case      :							Dict     				=						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    __a (					self     ,  **lowercase_					)      ->   List[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Dict     				=						self.translate_src_text(**lowercase_					)
               self.assertListEqual(self.expected_text     ,  lowercase_					)
  
  
  
        def    __a (					self     ,  **lowercase_					)      ->   int:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Any     				=						self.tokenizer(self.src_text     ,  **lowercase_     ,  padding=lowercase_     ,  return_tensors='''tf'''					)
               _snake_case      :							str     				=						self.model.generate(
                   model_inputs.input_ids     ,  attention_mask=model_inputs.attention_mask     ,  num_beams=2     ,  max_length=128					)
               _snake_case      :							str     				=						self.tokenizer.batch_decode(generated_ids.numpy()     ,  skip_special_tokens=lowercase_					)
               return generated_words
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class 				A    (__UpperCAmelCase				):
        _SCREAMING_SNAKE_CASE					=							"""mt"""
        _SCREAMING_SNAKE_CASE					=							"""en"""
        _SCREAMING_SNAKE_CASE					=							["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
        _SCREAMING_SNAKE_CASE					=							["""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					)      ->   str:
  
  
  
               '''simple docstring'''
  
               self._assert_generated_batch_equal_expected()
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class 				A    (__UpperCAmelCase				):
        _SCREAMING_SNAKE_CASE					=							"""en"""
        _SCREAMING_SNAKE_CASE					=							"""zh"""
        _SCREAMING_SNAKE_CASE					=							["""My name is Wolfgang and I live in Berlin"""]
        _SCREAMING_SNAKE_CASE					=							["""我叫沃尔夫冈 我住在柏林"""]
  
  
  
        @unittest.skip('''Skipping until #12647 is resolved.'''					)
        @slow
        def    __a (					self					)      ->   Any:
  
  
  
               '''simple docstring'''
  
               self._assert_generated_batch_equal_expected()
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class 				A    (__UpperCAmelCase				):
        _SCREAMING_SNAKE_CASE					=							"""en"""
        _SCREAMING_SNAKE_CASE					=							"""ROMANCE"""
        _SCREAMING_SNAKE_CASE					=							[
            """>>fr<< Don't spend so much time watching TV.""",
            """>>pt<< Your message has been sent.""",
            """>>es<< He's two years older than me.""",
        ]
        _SCREAMING_SNAKE_CASE					=							[
            """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					)      ->   Union[str, Any]:
  
  
  
               '''simple docstring'''
  
               self._assert_generated_batch_equal_expected()
  
  
  
        @unittest.skip('''Skipping until #12647 is resolved.'''					)
        @slow
        def    __a (					self					)      ->   Tuple:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Dict     				=						TranslationPipeline(self.model     ,  self.tokenizer     ,  framework='''tf'''					)
               _snake_case      :							Optional[Any]     				=						pipeline(self.src_text					)
               self.assertEqual(self.expected_text     ,  [x['''translation_text'''] for x in output]					)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  from __future__ import annotations
  
  class 				A    :
  
  
  
        def __init__(					self     ,  lowercase_					)      ->   None:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[Any]     				=						vertices
               _snake_case      :							int     				=						[[0] * vertices for _ in range(lowercase_					)]
  
  
  
        def    __a (					self     ,  lowercase_					)      ->   None:
  
  
  
               '''simple docstring'''
  
               print('''Vertex \t Distance from Source'''					)
               for vertex in range(self.vertices					):
                      print(lowercase_     ,  '''\t\t'''     ,  distances_from_source[vertex]					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   int:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						1E7
               _snake_case      :							Tuple     				=						0
  
               # Search not nearest vertex not in the shortest path tree
               for vertex in range(self.vertices					):
                      if distances_from_source[vertex] < minimum and visited[vertex] is False:
                             _snake_case      :							Dict     				=						distances_from_source[vertex]
                             _snake_case      :							int     				=						vertex
               return min_index
  
  
  
        def    __a (					self     ,  lowercase_					)      ->   None:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						[int(1E7					)] * self.vertices  # distances from the source
               _snake_case      :							List[Any]     				=						0
               _snake_case      :							List[str]     				=						[False] * self.vertices
  
               for _ in range(self.vertices					):
                      _snake_case      :							List[Any]     				=						self.minimum_distance(lowercase_     ,  lowercase_					)
                      _snake_case      :							List[str]     				=						True
  
                      # Update dist value of the adjacent vertices
                      # of the picked vertex only if the current
                      # distance is greater than new distance and
                      # the vertex in not in the shortest path tree
                      for v in range(self.vertices					):
                             if (
                                 self.graph[u][v] > 0
                                 and visited[v] is False
                                 and distances[v] > distances[u] + self.graph[u][v]
                             ):
                                    _snake_case      :							Any     				=						distances[u] + self.graph[u][v]
  
               self.print_solution(lowercase_					)
  
  
  if __name__ == "__main__":
     lowerCAmelCase_   =   Graph(9)
     lowerCAmelCase_   =   [
         [0, 4, 0, 0, 0, 0, 0, 8, 0],
         [4, 0, 8, 0, 0, 0, 0, 1_1, 0],
         [0, 8, 0, 7, 0, 4, 0, 0, 2],
         [0, 0, 7, 0, 9, 1_4, 0, 0, 0],
         [0, 0, 0, 9, 0, 1_0, 0, 0, 0],
         [0, 0, 4, 1_4, 1_0, 0, 2, 0, 0],
         [0, 0, 0, 0, 0, 2, 0, 1, 6],
         [8, 1_1, 0, 0, 0, 0, 1, 0, 7],
         [0, 0, 2, 0, 0, 0, 6, 7, 0],
     ]
     graph.dijkstra(0)
  
  
  
  
  
  
  