
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 						UpperCAmelCase	:



   def __init__(self	:      Any					,		snake_case__	:      Any					,		snake_case__	:      int=13					,		snake_case__	:      str=7					,		snake_case__	:      Optional[Any]=True					,		snake_case__	:      Tuple=True					,		snake_case__	:      List[str]=True					,		snake_case__	:      Any=True					,		snake_case__	:      Optional[int]=99					,		snake_case__	:      Union[str, Any]=32					,		snake_case__	:      str=2					,		snake_case__	:      List[str]=4					,		snake_case__	:      int=37					,		snake_case__	:      Tuple="gelu"					,		snake_case__	:      Tuple=0.1					,		snake_case__	:      str=0.1					,		snake_case__	:      str=5_12					,		snake_case__	:      List[str]=16					,		snake_case__	:      int=2					,		snake_case__	:      int=0.02					,		snake_case__	:      Dict=3					,		snake_case__	:      Optional[int]=4					,		snake_case__	:      Dict=None					,		)	->			int:


          '''simple docstring'''


          snake_case :     int           =   parent
          snake_case :     List[Any]           =   13
          snake_case :     Optional[int]           =   7
          snake_case :     Optional[int]           =   True
          snake_case :     List[str]           =   True
          snake_case :     List[Any]           =   True
          snake_case :     Any           =   True
          snake_case :     Optional[int]           =   99
          snake_case :     Tuple           =   32
          snake_case :     str           =   2
          snake_case :     List[Any]           =   4
          snake_case :     Optional[Any]           =   37
          snake_case :     Any           =   "gelu"
          snake_case :     Tuple           =   0.1
          snake_case :     Tuple           =   0.1
          snake_case :     List[str]           =   5_12
          snake_case :     Tuple           =   16
          snake_case :     str           =   False
          snake_case :     List[str]           =   -1
          snake_case :     Tuple           =   True
          snake_case :     Optional[int]           =   2
          snake_case :     str           =   0.02
          snake_case :     List[Any]           =   3
          snake_case :     Optional[Any]           =   4
          snake_case :     Tuple           =   None



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[Any]				)	->			Optional[Any]:


          '''simple docstring'''


          snake_case :     Union[str, Any]           =   ids_tensor([self.batch_size, self.seq_length]					,		self.vocab_size				)

          snake_case :     Tuple           =   None
          if self.use_input_mask:
                 snake_case :     Dict           =   random_attention_mask([self.batch_size, self.seq_length]				)

          snake_case :     str           =   None
          if self.use_token_type_ids:
                 snake_case :     Union[str, Any]           =   ids_tensor([self.batch_size, self.seq_length]					,		self.type_vocab_size				)

          snake_case :     str           =   None
          snake_case :     List[Any]           =   None
          snake_case :     Optional[Any]           =   None
          if self.use_labels:
                 snake_case :     Dict           =   ids_tensor([self.batch_size]					,		self.type_sequence_label_size				)
                 snake_case :     Any           =   ids_tensor([self.batch_size, self.seq_length]					,		self.num_labels				)

          snake_case :     Any           =   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=A_					,		)

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



   def        _SCREAMING_SNAKE_CASE     (self	:      Tuple					,		snake_case__	:      Union[str, Any]					,		snake_case__	:      int					,		snake_case__	:      Dict					,		snake_case__	:      Tuple					,		snake_case__	:      int					,		snake_case__	:      List[Any]					,		snake_case__	:      str				)	->			str:


          '''simple docstring'''


          snake_case :     Union[str, Any]           =   TFDebertaModel(config=A_				)
          snake_case :     int           =   {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

          snake_case :     Any           =   [input_ids, input_mask]
          snake_case :     Union[str, Any]           =   model(A_				)

          snake_case :     List[Any]           =   model(A_				)

          self.parent.assertEqual(result.last_hidden_state.shape					,		(self.batch_size, self.seq_length, self.hidden_size)				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[Any]					,		snake_case__	:      Optional[int]					,		snake_case__	:      Dict					,		snake_case__	:      List[str]					,		snake_case__	:      str					,		snake_case__	:      Dict					,		snake_case__	:      List[str]					,		snake_case__	:      Any				)	->			int:


          '''simple docstring'''


          snake_case :     List[str]           =   TFDebertaForMaskedLM(config=A_				)
          snake_case :     List[str]           =   {
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }
          snake_case :     Dict           =   model(A_				)
          self.parent.assertEqual(result.logits.shape					,		(self.batch_size, self.seq_length, self.vocab_size)				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Tuple					,		snake_case__	:      Dict					,		snake_case__	:      Optional[int]					,		snake_case__	:      Tuple					,		snake_case__	:      Any					,		snake_case__	:      Optional[Any]					,		snake_case__	:      List[str]					,		snake_case__	:      str				)	->			List[str]:


          '''simple docstring'''


          snake_case :     List[str]           =   self.num_labels
          snake_case :     Tuple           =   TFDebertaForSequenceClassification(config=A_				)
          snake_case :     Optional[Any]           =   {
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }

          snake_case :     Optional[Any]           =   model(A_				)
          self.parent.assertEqual(result.logits.shape					,		(self.batch_size, self.num_labels)				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Tuple					,		snake_case__	:      Optional[Any]					,		snake_case__	:      Union[str, Any]					,		snake_case__	:      Optional[int]					,		snake_case__	:      Optional[Any]					,		snake_case__	:      Tuple					,		snake_case__	:      List[str]					,		snake_case__	:      List[Any]				)	->			Optional[int]:


          '''simple docstring'''


          snake_case :     Union[str, Any]           =   self.num_labels
          snake_case :     Dict           =   TFDebertaForTokenClassification(config=A_				)
          snake_case :     List[str]           =   {
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }
          snake_case :     Optional[int]           =   model(A_				)
          self.parent.assertEqual(result.logits.shape					,		(self.batch_size, self.seq_length, self.num_labels)				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Any					,		snake_case__	:      Any					,		snake_case__	:      Optional[int]					,		snake_case__	:      Optional[Any]					,		snake_case__	:      Any					,		snake_case__	:      List[Any]					,		snake_case__	:      Union[str, Any]					,		snake_case__	:      str				)	->			List[str]:


          '''simple docstring'''


          snake_case :     Tuple           =   TFDebertaForQuestionAnswering(config=A_				)
          snake_case :     int           =   {
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }

          snake_case :     Any           =   model(A_				)
          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        _SCREAMING_SNAKE_CASE     (self	:      Any				)	->			Tuple:


          '''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
          )       ,      
          ) :     Union[str, Any]           =   config_and_inputs
          snake_case :     List[Any]           =   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
          return config, inputs_dict






@require_tf
class 						UpperCAmelCase	(		A_ ,A_ ,unittest.TestCase				):
   A__      :	List[str]		  =  (
       (
           TFDebertaModel,
           TFDebertaForMaskedLM,
           TFDebertaForQuestionAnswering,
           TFDebertaForSequenceClassification,
           TFDebertaForTokenClassification,
       )
       if is_tf_available()
       else ()
   )
   A__      :	List[str]		  =  (
       {
           "feature-extraction": TFDebertaModel,
           "fill-mask": TFDebertaForMaskedLM,
           "question-answering": TFDebertaForQuestionAnswering,
           "text-classification": TFDebertaForSequenceClassification,
           "token-classification": TFDebertaForTokenClassification,
           "zero-shot": TFDebertaForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )

   A__      :	List[Any]		  =  False
   A__      :	List[str]		  =  False



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]				)	->			Optional[int]:


          '''simple docstring'''


          snake_case :     Any           =   TFDebertaModelTester(self				)
          snake_case :     int           =   ConfigTester(self					,		config_class=A_					,		hidden_size=37				)



   def        _SCREAMING_SNAKE_CASE     (self	:      int				)	->			Optional[int]:


          '''simple docstring'''


          self.config_tester.run_common_tests()



   def        _SCREAMING_SNAKE_CASE     (self	:      int				)	->			Union[str, Any]:


          '''simple docstring'''


          snake_case :     Optional[Any]           =   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A_				)



   def        _SCREAMING_SNAKE_CASE     (self	:      List[str]				)	->			List[Any]:


          '''simple docstring'''


          snake_case :     Tuple           =   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*A_				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[Any]				)	->			Optional[Any]:


          '''simple docstring'''


          snake_case :     Tuple           =   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*A_				)



   def        _SCREAMING_SNAKE_CASE     (self	:      int				)	->			str:


          '''simple docstring'''


          snake_case :     Union[str, Any]           =   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*A_				)



   def        _SCREAMING_SNAKE_CASE     (self	:      List[str]				)	->			Optional[int]:


          '''simple docstring'''


          snake_case :     Tuple           =   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*A_				)



   @slow
   def        _SCREAMING_SNAKE_CASE     (self	:      List[str]				)	->			Union[str, Any]:


          '''simple docstring'''


          snake_case :     Optional[Any]           =   TFDebertaModel.from_pretrained("kamalkraj/deberta-base"				)
          self.assertIsNotNone(A_				)






@require_tf
class 						UpperCAmelCase	(		unittest.TestCase				):



   @unittest.skip(reason="Model not available yet"				)
   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]				)	->			Optional[Any]:


          '''simple docstring'''


          pass



   @slow
   def        _SCREAMING_SNAKE_CASE     (self	:      Union[str, Any]				)	->			List[Any]:


          '''simple docstring'''


          snake_case :     Dict           =   TFDebertaModel.from_pretrained("kamalkraj/deberta-base"				)
          snake_case :     Dict           =   tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]				)
          snake_case :     List[str]           =   tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
          snake_case :     Dict           =   model(A_					,		attention_mask=A_				)[0]

          snake_case :     Tuple           =   tf.constant(
              [
                  [
                      [-0.59855896, -0.80552566, -0.8462135],
                      [1.4484025, -0.93483794, -0.80593085],
                      [0.3122741, 0.00316059, -1.4131377],
                  ]
              ]				)
          tf.debugging.assert_near(output[:, 1:4, 1:4]					,		A_					,		atol=1e-4				)







from __future__ import annotations






class 						UpperCAmelCase	:



   def __init__(self	:      List[str]					,		snake_case__	:      int				)	->			None:


          '''simple docstring'''


          snake_case :     Any           =   vertices
          snake_case :     str           =   [[0] * vertices for _ in range(snake_case__				)]



   def        _SCREAMING_SNAKE_CASE     (self	:      str					,		snake_case__	:      list[int]				)	->			None:


          '''simple docstring'''


          print("Vertex \t Distance from Source"				)
          for vertex in range(self.vertices				):
                 print(snake_case__					,		"\t\t"					,		distances_from_source[vertex]				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]					,		snake_case__	:      list[int]					,		snake_case__	:      list[bool]				)	->			int:


          '''simple docstring'''


          snake_case :     Union[str, Any]           =   1e7
          snake_case :     Any           =   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 :     Union[str, Any]           =   distances_from_source[vertex]
                        snake_case :     str           =   vertex
          return min_index



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[Any]					,		snake_case__	:      int				)	->			None:


          '''simple docstring'''


          snake_case :     Optional[int]           =   [int(1e7				)] * self.vertices  # distances from the source
          snake_case :     Dict           =   0
          snake_case :     Union[str, Any]           =   [False] * self.vertices

          for _ in range(self.vertices				):
                 snake_case :     Dict           =   self.minimum_distance(snake_case__					,		snake_case__				)
                 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 :     Union[str, Any]           =   distances[u] + self.graph[u][v]

          self.print_solution(snake_case__				)


if __name__ == "__main__":
   __lowerCamelCase							= Graph(9)
   __lowerCamelCase							= [
       [0, 4, 0, 0, 0, 0, 0, 8, 0],
       [4, 0, 8, 0, 0, 0, 0, 11, 0],
       [0, 8, 0, 7, 0, 4, 0, 0, 2],
       [0, 0, 7, 0, 9, 14, 0, 0, 0],
       [0, 0, 0, 9, 0, 10, 0, 0, 0],
       [0, 0, 4, 14, 10, 0, 2, 0, 0],
       [0, 0, 0, 0, 0, 2, 0, 1, 6],
       [8, 11, 0, 0, 0, 0, 1, 0, 7],
       [0, 0, 2, 0, 0, 0, 6, 7, 0],
   ]
   graph.dijkstra(0)





