

import sys
from collections import defaultdict



class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  def __init__(      self			:   int   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						[]







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   List[str]   )							->     List[str]:






     """simple docstring"""







     return self.node_position[vertex]







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   Any   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						pos







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   Optional[Any]   )							->     List[str]:






     """simple docstring"""







     if start > size // 2 - 1:
        return
     else:
        if 2 * start + 2 >= size:
           __lowerCAmelCase  :      Optional[int]    =						2 * start + 1
        else:
           if heap[2 * start + 1] < heap[2 * start + 2]:
              __lowerCAmelCase  :      str    =						2 * start + 1
           else:
              __lowerCAmelCase  :      List[str]    =						2 * start + 2
        if heap[smallest_child] < heap[start]:
           __lowerCAmelCase  :      List[str]    =						heap[smallest_child], positions[smallest_child]
           __lowerCAmelCase  :      Optional[int]    =						(
               heap[start],
               positions[start],
           )
           __lowerCAmelCase  :      Tuple    =						temp, tempa

           __lowerCAmelCase  :      Dict    =						self.get_position(positions[smallest_child]   )
           self.set_position(
               positions[smallest_child]	,							self.get_position(positions[start]   )   )
           self.set_position(positions[start]	,							lowerCAmelCase   )

           self.top_to_bottom(lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   List[Any]   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						position[index]

     while index != 0:
        __lowerCAmelCase  :      str    =						int((index - 2) / 2   ) if index % 2 == 0 else int((index - 1) / 2   )

        if val < heap[parent]:
           __lowerCAmelCase  :      Union[str, Any]    =						heap[parent]
           __lowerCAmelCase  :      int    =						position[parent]
           self.set_position(position[parent]	,							lowerCAmelCase   )
        else:
           __lowerCAmelCase  :      str    =						val
           __lowerCAmelCase  :      Tuple    =						temp
           self.set_position(lowerCAmelCase	,							lowerCAmelCase   )
           break
        __lowerCAmelCase  :      Union[str, Any]    =						parent
     else:
        __lowerCAmelCase  :      str    =						val
        __lowerCAmelCase  :      List[Any]    =						temp
        self.set_position(lowerCAmelCase	,							0   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Tuple   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						len(lowerCAmelCase   ) // 2 - 1
     for i in range(lowerCAmelCase	,							-1	,							-1   ):
        self.top_to_bottom(lowerCAmelCase	,							lowerCAmelCase	,							len(lowerCAmelCase   )	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Tuple   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						positions[0]
     __lowerCAmelCase  :      int    =						sys.maxsize
     self.top_to_bottom(lowerCAmelCase	,							0	,							len(lowerCAmelCase   )	,							lowerCAmelCase   )
     return temp






def      snake_case_				(__A					:						List[Any]					)      ->     Dict:
   __lowerCAmelCase  :      Optional[Any]    =						Heap()

   __lowerCAmelCase  :      Optional[int]    =						[0] * len(__A					)
   __lowerCAmelCase  :      Any    =						[-1] * len(__A					)  # Neighboring Tree Vertex of selected vertex
   # Minimum Distance of explored vertex with neighboring vertex of partial tree
   # formed in graph
   __lowerCAmelCase  :      int    =						[]  # Heap of Distance of vertices from their neighboring vertex
   __lowerCAmelCase  :      int    =						[]

   for vertex in range(len(__A					)					):
      distance_tv.append(sys.maxsize					)
      positions.append(__A					)
      heap.node_position.append(__A					)

   __lowerCAmelCase  :      str    =						[]
   __lowerCAmelCase  :      Any    =						1
   __lowerCAmelCase  :      str    =						sys.maxsize
   for neighbor, distance in adjacency_list[0]:
      __lowerCAmelCase  :      str    =						0
      __lowerCAmelCase  :      Optional[Any]    =						distance
   heap.heapify(__A ,       __A					)

   for _ in range(1 ,       len(__A					)					):
      __lowerCAmelCase  :      int    =						heap.delete_minimum(__A ,       __A					)
      if visited[vertex] == 0:
         tree_edges.append((nbr_tv[vertex], vertex)					)
         __lowerCAmelCase  :      Any    =						1
         for neighbor, distance in adjacency_list[vertex]:
            if (
                visited[neighbor] == 0
                and distance < distance_tv[heap.get_position(__A					)]
            ):
               __lowerCAmelCase  :      int    =						distance
               heap.bottom_to_top(
                   __A ,       heap.get_position(__A					) ,       __A ,       __A					)
               __lowerCAmelCase  :      List[str]    =						vertex
   return tree_edges


if __name__ == "__main__":  # pragma: no cover
   # < --------- Prims Algorithm --------- >
   __UpperCAmelCase								=			int(input("""Enter number of edges: """).strip())
   __UpperCAmelCase								=			defaultdict(list)
   for _ in range(edges_number):
      __UpperCAmelCase								=			[int(x) for x in input().strip().split()]
      adjacency_list[edge[0]].append([edge[1], edge[2]])
      adjacency_list[edge[1]].append([edge[0], edge[2]])
   print(prisms_algorithm(adjacency_list))





import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import (
       MODEL_FOR_PRETRAINING_MAPPING,
       MobileBertForMaskedLM,
       MobileBertForMultipleChoice,
       MobileBertForNextSentencePrediction,
       MobileBertForPreTraining,
       MobileBertForQuestionAnswering,
       MobileBertForSequenceClassification,
       MobileBertForTokenClassification,
       MobileBertModel,
   )



class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  def __init__(      self			:   Union[str, Any]	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   Dict=13	,							lowerCAmelCase			:   int=7	,							lowerCAmelCase			:   Any=True	,							lowerCAmelCase			:   str=True	,							lowerCAmelCase			:   Optional[Any]=True	,							lowerCAmelCase			:   str=True	,							lowerCAmelCase			:   Tuple=99	,							lowerCAmelCase			:   int=64	,							lowerCAmelCase			:   Any=32	,							lowerCAmelCase			:   str=5	,							lowerCAmelCase			:   List[str]=4	,							lowerCAmelCase			:   str=37	,							lowerCAmelCase			:   Union[str, Any]="gelu"	,							lowerCAmelCase			:   Dict=0.1	,							lowerCAmelCase			:   Dict=0.1	,							lowerCAmelCase			:   Optional[int]=5_12	,							lowerCAmelCase			:   List[str]=16	,							lowerCAmelCase			:   str=2	,							lowerCAmelCase			:   Union[str, Any]=0.02	,							lowerCAmelCase			:   Dict=3	,							lowerCAmelCase			:   int=4	,							lowerCAmelCase			:   Union[str, Any]=None	,							)							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						parent
     __lowerCAmelCase  :      Tuple    =						batch_size
     __lowerCAmelCase  :      Dict    =						seq_length
     __lowerCAmelCase  :      List[str]    =						is_training
     __lowerCAmelCase  :      Dict    =						use_input_mask
     __lowerCAmelCase  :      Optional[int]    =						use_token_type_ids
     __lowerCAmelCase  :      List[str]    =						use_labels
     __lowerCAmelCase  :      Dict    =						vocab_size
     __lowerCAmelCase  :      List[str]    =						hidden_size
     __lowerCAmelCase  :      Optional[int]    =						embedding_size
     __lowerCAmelCase  :      Optional[int]    =						num_hidden_layers
     __lowerCAmelCase  :      Optional[Any]    =						num_attention_heads
     __lowerCAmelCase  :      Optional[Any]    =						intermediate_size
     __lowerCAmelCase  :      Optional[int]    =						hidden_act
     __lowerCAmelCase  :      Any    =						hidden_dropout_prob
     __lowerCAmelCase  :      Optional[int]    =						attention_probs_dropout_prob
     __lowerCAmelCase  :      List[str]    =						max_position_embeddings
     __lowerCAmelCase  :      Optional[Any]    =						type_vocab_size
     __lowerCAmelCase  :      Optional[Any]    =						type_sequence_label_size
     __lowerCAmelCase  :      Optional[Any]    =						initializer_range
     __lowerCAmelCase  :      Optional[Any]    =						num_labels
     __lowerCAmelCase  :      Union[str, Any]    =						num_choices
     __lowerCAmelCase  :      Union[str, Any]    =						scope







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     List[str]:






     """simple docstring"""







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

     __lowerCAmelCase  :      Dict    =						None
     if self.use_input_mask:
        __lowerCAmelCase  :      Union[str, Any]    =						random_attention_mask([self.batch_size, self.seq_length]   )

     __lowerCAmelCase  :      List[str]    =						None
     if self.use_token_type_ids:
        __lowerCAmelCase  :      Tuple    =						ids_tensor([self.batch_size, self.seq_length]	,							self.type_vocab_size   )

     __lowerCAmelCase  :      List[Any]    =						None
     __lowerCAmelCase  :      Tuple    =						None
     __lowerCAmelCase  :      int    =						None
     if self.use_labels:
        __lowerCAmelCase  :      Tuple    =						ids_tensor([self.batch_size]	,							self.type_sequence_label_size   )
        __lowerCAmelCase  :      List[str]    =						ids_tensor([self.batch_size, self.seq_length]	,							self.num_labels   )
        __lowerCAmelCase  :      str    =						ids_tensor([self.batch_size]	,							self.num_choices   )

     __lowerCAmelCase  :      Optional[int]    =						self.get_config()

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







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Union[str, Any]:






     """simple docstring"""







     return MobileBertConfig(
         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	,							embedding_size=self.embedding_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	,							is_decoder=lowerCAmelCase	,							initializer_range=self.initializer_range	,							)







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   List[Any]   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						MobileBertModel(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      Union[str, Any]    =						model(lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							token_type_ids=lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						model(lowerCAmelCase	,							token_type_ids=lowerCAmelCase   )
     __lowerCAmelCase  :      Tuple    =						model(lowerCAmelCase   )

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







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Optional[int]   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						MobileBertForMaskedLM(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      List[Any]    =						model(lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							token_type_ids=lowerCAmelCase	,							labels=lowerCAmelCase   )
     self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.seq_length, self.vocab_size)   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   Optional[int]   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						MobileBertForNextSentencePrediction(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      List[Any]    =						model(
         lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							token_type_ids=lowerCAmelCase	,							labels=lowerCAmelCase	,							)
     self.parent.assertEqual(result.logits.shape	,							(self.batch_size, 2)   )







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






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						MobileBertForPreTraining(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      Optional[int]    =						model(
         lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							token_type_ids=lowerCAmelCase	,							labels=lowerCAmelCase	,							next_sentence_label=lowerCAmelCase	,							)
     self.parent.assertEqual(result.prediction_logits.shape	,							(self.batch_size, self.seq_length, self.vocab_size)   )
     self.parent.assertEqual(result.seq_relationship_logits.shape	,							(self.batch_size, 2)   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Any   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						MobileBertForQuestionAnswering(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      int    =						model(
         lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							token_type_ids=lowerCAmelCase	,							start_positions=lowerCAmelCase	,							end_positions=lowerCAmelCase	,							)
     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			:   Dict	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Union[str, Any]   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.num_labels
     __lowerCAmelCase  :      int    =						MobileBertForSequenceClassification(lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      Optional[int]    =						model(lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							token_type_ids=lowerCAmelCase	,							labels=lowerCAmelCase   )
     self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.num_labels)   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Optional[int]   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.num_labels
     __lowerCAmelCase  :      Dict    =						MobileBertForTokenClassification(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      Tuple    =						model(lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							token_type_ids=lowerCAmelCase	,							labels=lowerCAmelCase   )
     self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.seq_length, self.num_labels)   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Optional[Any]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   List[Any]   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						self.num_choices
     __lowerCAmelCase  :      List[Any]    =						MobileBertForMultipleChoice(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     __lowerCAmelCase  :      Dict    =						input_ids.unsqueeze(1   ).expand(-1	,							self.num_choices	,							-1   ).contiguous()
     __lowerCAmelCase  :      Union[str, Any]    =						token_type_ids.unsqueeze(1   ).expand(-1	,							self.num_choices	,							-1   ).contiguous()
     __lowerCAmelCase  :      Optional[int]    =						input_mask.unsqueeze(1   ).expand(-1	,							self.num_choices	,							-1   ).contiguous()
     __lowerCAmelCase  :      Optional[int]    =						model(
         lowerCAmelCase	,							attention_mask=lowerCAmelCase	,							token_type_ids=lowerCAmelCase	,							labels=lowerCAmelCase	,							)
     self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.num_choices)   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						self.prepare_config_and_inputs()
     (
         (
         __lowerCAmelCase
     )   ,(
         __lowerCAmelCase
     )   ,(
         __lowerCAmelCase
     )   ,(
         __lowerCAmelCase
     )   ,(
         __lowerCAmelCase
     )   ,(
         __lowerCAmelCase
     )   ,(
         __lowerCAmelCase
     )   ,
     )  :      List[Any]    =						config_and_inputs
     __lowerCAmelCase  :      List[str]    =						{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
     return config, inputs_dict






@require_torch
class SCREAMING_SNAKE_CASE     (  a_       ,							a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      str										=(
      (
          MobileBertModel,
          MobileBertForMaskedLM,
          MobileBertForMultipleChoice,
          MobileBertForNextSentencePrediction,
          MobileBertForPreTraining,
          MobileBertForQuestionAnswering,
          MobileBertForSequenceClassification,
          MobileBertForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  lowerCamelCase   :      Optional[int]										=(
      {
          "feature-extraction": MobileBertModel,
          "fill-mask": MobileBertForMaskedLM,
          "question-answering": MobileBertForQuestionAnswering,
          "text-classification": MobileBertForSequenceClassification,
          "token-classification": MobileBertForTokenClassification,
          "zero-shot": MobileBertForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  lowerCamelCase   :      Union[str, Any]										=True







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]	,							lowerCAmelCase			:   str	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   List[Any]=False   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Union[str, Any]    =						super()._prepare_for_class(lowerCAmelCase	,							lowerCAmelCase	,							return_labels=lowerCAmelCase   )

     if return_labels:
        if model_class in get_values(lowerCAmelCase   ):
           __lowerCAmelCase  :      Tuple    =						torch.zeros(
               (self.model_tester.batch_size, self.model_tester.seq_length)	,							dtype=torch.long	,							device=lowerCAmelCase   )
           __lowerCAmelCase  :      Union[str, Any]    =						torch.zeros(
               self.model_tester.batch_size	,							dtype=torch.long	,							device=lowerCAmelCase   )
     return inputs_dict







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      int    =						MobileBertModelTester(self   )
     __lowerCAmelCase  :      Optional[Any]    =						ConfigTester(self	,							config_class=lowerCAmelCase	,							hidden_size=37   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Optional[Any]:






     """simple docstring"""







     self.config_tester.run_common_tests()







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_model(*lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_masked_lm(*lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_multiple_choice(*lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_pretraining(*lowerCAmelCase   )







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






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_question_answering(*lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_sequence_classification(*lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mobilebert_for_token_classification(*lowerCAmelCase   )






def      snake_case_				(__A					:						Any					)      ->     Optional[Any]:
   return torch.tensor(
       __A ,       dtype=torch.long ,       device=__A ,       )


__UpperCAmelCase								=			1e-3



@require_torch
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						MobileBertModel.from_pretrained("""google/mobilebert-uncased"""   ).to(lowerCAmelCase   )
     __lowerCAmelCase  :      int    =						_long_tensor([[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]]   )
     with torch.no_grad():
        __lowerCAmelCase  :      List[str]    =						model(lowerCAmelCase   )[0]
     __lowerCAmelCase  :      List[Any]    =						torch.Size((1, 9, 5_12)   )
     self.assertEqual(output.shape	,							lowerCAmelCase   )
     __lowerCAmelCase  :      int    =						torch.tensor(
         [
             [
                 [-2.473_6526e07, 8.269_1656e04, 1.652_1838e05],
                 [-5.754_1704e-01, 3.905_6022e00, 4.401_1507e00],
                 [2.604_7359e00, 1.567_7652e00, -1.732_4188e-01],
             ]
         ]	,							device=lowerCAmelCase	,							)

     # MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
     # ~1 difference, it's therefore not a good idea to measure using addition.
     # Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
     # result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
     __lowerCAmelCase  :      Union[str, Any]    =						torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE   )
     __lowerCAmelCase  :      Union[str, Any]    =						torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE   )

     self.assertTrue(lower_bound and upper_bound   )


