code: 
  
  
  
  
  
  from __future__ import annotations
  
  import unittest
  
  from transformers import DebertaConfig, is_tf_available
  from transformers.testing_utils import require_tf, slow
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
     import tensorflow as tf
  
     from transformers import (
         TFDebertaForMaskedLM,
         TFDebertaForQuestionAnswering,
         TFDebertaForSequenceClassification,
         TFDebertaForTokenClassification,
         TFDebertaModel,
     )
  
  class 				A    :
  
  
  
        def __init__(					self     ,  lowercase_     ,  lowercase_=13     ,  lowercase_=7     ,  lowercase_=True     ,  lowercase_=True     ,  lowercase_=True     ,  lowercase_=True     ,  lowercase_=99     ,  lowercase_=32     ,  lowercase_=2     ,  lowercase_=4     ,  lowercase_=37     ,  lowercase_="gelu"     ,  lowercase_=0.1     ,  lowercase_=0.1     ,  lowercase_=512     ,  lowercase_=16     ,  lowercase_=2     ,  lowercase_=0.02     ,  lowercase_=3     ,  lowercase_=4     ,  lowercase_=None     ,  )      ->   Any:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Dict     				=						parent
               _snake_case      :							List[str]     				=						13
               _snake_case      :							Union[str, Any]     				=						7
               _snake_case      :							Tuple     				=						True
               _snake_case      :							Tuple     				=						True
               _snake_case      :							Optional[Any]     				=						True
               _snake_case      :							List[str]     				=						True
               _snake_case      :							Dict     				=						99
               _snake_case      :							Optional[Any]     				=						32
               _snake_case      :							str     				=						2
               _snake_case      :							Any     				=						4
               _snake_case      :							Tuple     				=						37
               _snake_case      :							Any     				=						'''gelu'''
               _snake_case      :							Dict     				=						0.1
               _snake_case      :							Any     				=						0.1
               _snake_case      :							List[str]     				=						512
               _snake_case      :							int     				=						16
               _snake_case      :							Union[str, Any]     				=						False
               _snake_case      :							Tuple     				=						-1
               _snake_case      :							Optional[Any]     				=						True
               _snake_case      :							List[str]     				=						2
               _snake_case      :							Any     				=						0.02
               _snake_case      :							Dict     				=						3
               _snake_case      :							List[str]     				=						4
               _snake_case      :							str     				=						None
  
  
  
        def    __a (					self					)      ->   List[str]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Any     				=						ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size					)
  
               _snake_case      :							Optional[Any]     				=						None
               if self.use_input_mask:
                      _snake_case      :							Union[str, Any]     				=						random_attention_mask([self.batch_size, self.seq_length]					)
  
               _snake_case      :							Tuple     				=						None
               if self.use_token_type_ids:
                      _snake_case      :							List[Any]     				=						ids_tensor([self.batch_size, self.seq_length]     ,  self.type_vocab_size					)
  
               _snake_case      :							Dict     				=						None
               _snake_case      :							Tuple     				=						None
               _snake_case      :							int     				=						None
               if self.use_labels:
                      _snake_case      :							str     				=						ids_tensor([self.batch_size]     ,  self.type_sequence_label_size					)
                      _snake_case      :							Optional[Any]     				=						ids_tensor([self.batch_size, self.seq_length]     ,  self.num_labels					)
  
               _snake_case      :							str     				=						DebertaConfig(
                   vocab_size=self.vocab_size     ,  hidden_size=self.hidden_size     ,  num_hidden_layers=self.num_hidden_layers     ,  num_attention_heads=self.num_attention_heads     ,  intermediate_size=self.intermediate_size     ,  hidden_act=self.hidden_act     ,  hidden_dropout_prob=self.hidden_dropout_prob     ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,  max_position_embeddings=self.max_position_embeddings     ,  type_vocab_size=self.type_vocab_size     ,  relative_attention=self.relative_attention     ,  max_relative_positions=self.max_relative_positions     ,  position_biased_input=self.position_biased_input     ,  initializer_range=self.initializer_range     ,  return_dict=lowercase_     ,  )
  
               return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							int     				=						TFDebertaModel(config=lowercase_					)
               _snake_case      :							int     				=						{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
  
               _snake_case      :							List[str]     				=						[input_ids, input_mask]
               _snake_case      :							Tuple     				=						model(lowercase_					)
  
               _snake_case      :							List[Any]     				=						model(lowercase_					)
  
               self.parent.assertEqual(result.last_hidden_state.shape     ,  (self.batch_size, self.seq_length, self.hidden_size)					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					)      ->   Union[str, Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[int]     				=						TFDebertaForMaskedLM(config=lowercase_					)
               _snake_case      :							str     				=						{
                   '''input_ids''': input_ids,
                   '''attention_mask''': input_mask,
                   '''token_type_ids''': token_type_ids,
               }
               _snake_case      :							Dict     				=						model(lowercase_					)
               self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.seq_length, self.vocab_size)					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					)      ->   List[str]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[int]     				=						self.num_labels
               _snake_case      :							Tuple     				=						TFDebertaForSequenceClassification(config=lowercase_					)
               _snake_case      :							str     				=						{
                   '''input_ids''': input_ids,
                   '''attention_mask''': input_mask,
                   '''token_type_ids''': token_type_ids,
               }
  
               _snake_case      :							Any     				=						model(lowercase_					)
               self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.num_labels)					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					)      ->   int:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Any     				=						self.num_labels
               _snake_case      :							Dict     				=						TFDebertaForTokenClassification(config=lowercase_					)
               _snake_case      :							Dict     				=						{
                   '''input_ids''': input_ids,
                   '''attention_mask''': input_mask,
                   '''token_type_ids''': token_type_ids,
               }
               _snake_case      :							Any     				=						model(lowercase_					)
               self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.seq_length, self.num_labels)					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					)      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							str     				=						TFDebertaForQuestionAnswering(config=lowercase_					)
               _snake_case      :							List[Any]     				=						{
                   '''input_ids''': input_ids,
                   '''attention_mask''': input_mask,
                   '''token_type_ids''': token_type_ids,
               }
  
               _snake_case      :							Any     				=						model(lowercase_					)
               self.parent.assertEqual(result.start_logits.shape     ,  (self.batch_size, self.seq_length)					)
               self.parent.assertEqual(result.end_logits.shape     ,  (self.batch_size, self.seq_length)					)
  
  
  
        def    __a (					self					)      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[int]     				=						self.prepare_config_and_inputs()
               (
                   (
                   _snake_case
               )				,    (
                   _snake_case
               )				,    (
                   _snake_case
               )				,    (
                   _snake_case
               )				,    (
                   _snake_case
               )				,    (
                   _snake_case
               )				,    (
                   _snake_case
               )				,    
               )      :							Optional[int]     				=						config_and_inputs
               _snake_case      :							Optional[int]     				=						{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
               return config, inputs_dict
  
  
  @require_tf
  class 				A    (__UpperCAmelCase       ,__UpperCAmelCase       ,unittest.TestCase				):
        _SCREAMING_SNAKE_CASE					=							(
            (
                TFDebertaModel,
                TFDebertaForMaskedLM,
                TFDebertaForQuestionAnswering,
                TFDebertaForSequenceClassification,
                TFDebertaForTokenClassification,
            )
            if is_tf_available()
            else ()
        )
        _SCREAMING_SNAKE_CASE					=							(
            {
                """feature-extraction""": TFDebertaModel,
                """fill-mask""": TFDebertaForMaskedLM,
                """question-answering""": TFDebertaForQuestionAnswering,
                """text-classification""": TFDebertaForSequenceClassification,
                """token-classification""": TFDebertaForTokenClassification,
                """zero-shot""": TFDebertaForSequenceClassification,
            }
            if is_tf_available()
            else {}
        )
  
        _SCREAMING_SNAKE_CASE					=							False
        _SCREAMING_SNAKE_CASE					=							False
  
  
  
        def    __a (					self					)      ->   Union[str, Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Union[str, Any]     				=						TFDebertaModelTester(self					)
               _snake_case      :							Optional[int]     				=						ConfigTester(self     ,  config_class=lowercase_     ,  hidden_size=37					)
  
  
  
        def    __a (					self					)      ->   int:
  
  
  
               '''simple docstring'''
  
               self.config_tester.run_common_tests()
  
  
  
        def    __a (					self					)      ->   str:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						self.model_tester.prepare_config_and_inputs()
               self.model_tester.create_and_check_model(*lowercase_					)
  
  
  
        def    __a (					self					)      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[Any]     				=						self.model_tester.prepare_config_and_inputs()
               self.model_tester.create_and_check_for_masked_lm(*lowercase_					)
  
  
  
        def    __a (					self					)      ->   List[str]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						self.model_tester.prepare_config_and_inputs()
               self.model_tester.create_and_check_for_question_answering(*lowercase_					)
  
  
  
        def    __a (					self					)      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[Any]     				=						self.model_tester.prepare_config_and_inputs()
               self.model_tester.create_and_check_for_sequence_classification(*lowercase_					)
  
  
  
        def    __a (					self					)      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							int     				=						self.model_tester.prepare_config_and_inputs()
               self.model_tester.create_and_check_for_token_classification(*lowercase_					)
  
  
  
        @slow
        def    __a (					self					)      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Dict     				=						TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''					)
               self.assertIsNotNone(lowercase_					)
  
  
  @require_tf
  class 				A    (unittest.TestCase				):
  
  
  
        @unittest.skip(reason='''Model not available yet'''					)
        def    __a (					self					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               pass
  
  
  
        @slow
        def    __a (					self					)      ->   int:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						TFDebertaModel.from_pretrained('''kamalkraj/deberta-base'''					)
               _snake_case      :							Tuple     				=						tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]					)
               _snake_case      :							Optional[int]     				=						tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]					)
               _snake_case      :							int     				=						model(lowercase_     ,  attention_mask=lowercase_					)[0]
  
               _snake_case      :							int     				=						tf.constant(
                   [
                       [
                           [-0.5985_5896, -0.8055_2566, -0.846_2135],
                           [1.448_4025, -0.9348_3794, -0.8059_3085],
                           [0.312_2741, 0.0031_6059, -1.413_1377],
                       ]
                   ]					)
               tf.debugging.assert_near(output[:, 1:4, 1:4]     ,  lowercase_     ,  atol=1E-4					)
  
  
  
  
  
  
  
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)
  
  
  
  
  
  
  