





import unittest

from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import 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 (
          MPNetForMaskedLM,
          MPNetForMultipleChoice,
          MPNetForQuestionAnswering,
          MPNetForSequenceClassification,
          MPNetForTokenClassification,
          MPNetModel,
      )







class     __snake_case       :





       '''simple docstring'''







       def __init__(  self :    int							,				A :    int							,				A :    Union[str, Any]=13							,				A :    Tuple=7							,				A :    List[Any]=True							,				A :    Tuple=True							,				A :    Dict=False							,				A :    str=True							,				A :    List[str]=99							,				A :    List[Any]=64							,				A :    int=5							,				A :    Optional[int]=4							,				A :    Union[str, Any]=64							,				A :    List[str]="gelu"							,				A :    str=0.1							,				A :    Union[str, Any]=0.1							,				A :    Tuple=512							,				A :    Dict=16							,				A :    int=2							,				A :    Any=0.02							,				A :    Optional[Any]=3							,				A :    Dict=4							,				A :    Union[str, Any]=None							,				):
         __snake_case:						str   =						parent
         __snake_case:						List[Any]   =						batch_size
         __snake_case:						Optional[Any]   =						seq_length
         __snake_case:						List[Any]   =						is_training
         __snake_case:						str   =						use_input_mask
         __snake_case:						int   =						use_token_type_ids
         __snake_case:						Optional[Any]   =						use_labels
         __snake_case:						Optional[int]   =						vocab_size
         __snake_case:						int   =						hidden_size
         __snake_case:						int   =						num_hidden_layers
         __snake_case:						List[str]   =						num_attention_heads
         __snake_case:						str   =						intermediate_size
         __snake_case:						int   =						hidden_act
         __snake_case:						str   =						hidden_dropout_prob
         __snake_case:						Optional[int]   =						attention_probs_dropout_prob
         __snake_case:						str   =						max_position_embeddings
         __snake_case:						int   =						type_vocab_size
         __snake_case:						Optional[Any]   =						type_sequence_label_size
         __snake_case:						Optional[int]   =						initializer_range
         __snake_case:						List[Any]   =						num_labels
         __snake_case:						List[Any]   =						num_choices
         __snake_case:						Dict   =						scope




       def 							UpperCAmelCase__ (  self :    List[Any]		):
         return MPNetConfig.from_pretrained("""microsoft/mpnet-base"""		)




       def 							UpperCAmelCase__ (  self :    str		):
         __snake_case:						str   =						ids_tensor([self.batch_size, self.seq_length]							,				self.vocab_size		)

         __snake_case:						int   =						None
         if self.use_input_mask:
           __snake_case:						Optional[Any]   =						random_attention_mask([self.batch_size, self.seq_length]		)

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

         __snake_case:						Optional[Any]   =						self.get_config()
         return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




       def 							UpperCAmelCase__ (  self :    str		):
         return MPNetConfig(
             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							,				initializer_range=self.initializer_range							,				)




       def 							UpperCAmelCase__ (  self :    List[str]							,				A :    str							,				A :    Dict							,				A :    Any							,				A :    Any							,				A :    Optional[int]							,				A :    List[Any]		):
         __snake_case:						Optional[int]   =						MPNetModel(config=A		)
         model.to(A		)
         model.eval()
         __snake_case:						Union[str, Any]   =						model(A							,				A		)
         __snake_case:						List[str]   =						model(A		)
         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 							UpperCAmelCase__ (  self :    int							,				A :    Optional[int]							,				A :    str							,				A :    Any							,				A :    Optional[Any]							,				A :    List[Any]							,				A :    Dict		):
         __snake_case:						Dict   =						MPNetForQuestionAnswering(config=A		)
         model.to(A		)
         model.eval()
         __snake_case:						Dict   =						model(
             A							,				attention_mask=A							,				start_positions=A							,				end_positions=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 							UpperCAmelCase__ (  self :    List[str]							,				A :    Optional[int]							,				A :    Tuple							,				A :    Dict							,				A :    str							,				A :    Tuple							,				A :    Tuple		):
         __snake_case:						Optional[int]   =						self.num_labels
         __snake_case:						Any   =						MPNetForSequenceClassification(A		)
         model.to(A		)
         model.eval()
         __snake_case:						Optional[Any]   =						model(A							,				attention_mask=A							,				labels=A		)
         self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.num_labels)		)




       def 							UpperCAmelCase__ (  self :    Union[str, Any]							,				A :    List[Any]							,				A :    Dict							,				A :    Tuple							,				A :    List[str]							,				A :    Tuple							,				A :    str		):
         __snake_case:						Optional[int]   =						self.num_choices
         __snake_case:						Optional[int]   =						MPNetForMultipleChoice(config=A		)
         model.to(A		)
         model.eval()
         __snake_case:						Dict   =						input_ids.unsqueeze(1		).expand(-1							,				self.num_choices							,				-1		).contiguous()
         __snake_case:						Optional[Any]   =						input_mask.unsqueeze(1		).expand(-1							,				self.num_choices							,				-1		).contiguous()
         __snake_case:						str   =						model(
             A							,				attention_mask=A							,				labels=A							,				)
         self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.num_choices)		)




       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    Any							,				A :    int							,				A :    Tuple							,				A :    Optional[int]							,				A :    Optional[Any]							,				A :    Optional[Any]		):
         __snake_case:						Dict   =						self.num_labels
         __snake_case:						str   =						MPNetForTokenClassification(config=A		)
         model.to(A		)
         model.eval()
         __snake_case:						Dict   =						model(A							,				attention_mask=A							,				labels=A		)
         self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.seq_length, self.num_labels)		)






       def 							UpperCAmelCase__ (  self :    str		):
         __snake_case:						List[Any]   =						self.prepare_config_and_inputs()
         ((__snake_case)  ,   (__snake_case)  ,   (__snake_case)  ,   (__snake_case)  ,   (__snake_case)  ,   (__snake_case)):						Union[str, Any]   =						config_and_inputs
         __snake_case:						Any   =						{"""input_ids""": input_ids, """attention_mask""": input_mask}
         return config, inputs_dict







@require_torch
class     __snake_case       (							__lowerCamelCase   ,			__lowerCamelCase   ,			unittest.TestCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     (
           (
               MPNetForMaskedLM,
               MPNetForMultipleChoice,
               MPNetForQuestionAnswering,
               MPNetForSequenceClassification,
               MPNetForTokenClassification,
               MPNetModel,
           )
           if is_torch_available()
           else ()
       )
       lowerCAmelCase__      		=     (
           {
               """feature-extraction""": MPNetModel,
               """fill-mask""": MPNetForMaskedLM,
               """question-answering""": MPNetForQuestionAnswering,
               """text-classification""": MPNetForSequenceClassification,
               """token-classification""": MPNetForTokenClassification,
               """zero-shot""": MPNetForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       lowerCAmelCase__      		=     False
       lowerCAmelCase__      		=     True




       def 							UpperCAmelCase__ (  self :    List[Any]		):
         __snake_case:						Optional[Any]   =						MPNetModelTester(self		)
         __snake_case:						Union[str, Any]   =						ConfigTester(self							,				config_class=A							,				hidden_size=37		)




       def 							UpperCAmelCase__ (  self :    Dict		):
         self.config_tester.run_common_tests()




       def 							UpperCAmelCase__ (  self :    Optional[Any]		):
         __snake_case:						Optional[int]   =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_model(*A		)




       def 							UpperCAmelCase__ (  self :    int		):
         __snake_case:						Union[str, Any]   =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_sequence_classification(*A		)




       def 							UpperCAmelCase__ (  self :    List[Any]		):
         __snake_case:						List[Any]   =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_multiple_choice(*A		)




       def 							UpperCAmelCase__ (  self :    List[str]		):
         __snake_case:						Optional[Any]   =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_token_classification(*A		)






       def 							UpperCAmelCase__ (  self :    List[str]		):
         __snake_case:						List[str]   =						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_question_answering(*A		)







@require_torch
class     __snake_case       (							unittest.TestCase			):





       '''simple docstring'''







       @slow
       def 							UpperCAmelCase__ (  self :    Any		):
         __snake_case:						str   =						MPNetModel.from_pretrained("""microsoft/mpnet-base"""		)
         __snake_case:						Optional[Any]   =						torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]		)
         __snake_case:						Optional[int]   =						model(A		)[0]
         __snake_case:						int   =						torch.Size((1, 11, 768)		)
         self.assertEqual(output.shape							,				A		)
         __snake_case:						int   =						torch.tensor(
             [[[-0.0550, 0.1943, -0.0740], [-0.0562, 0.2211, -0.0579], [-0.0437, 0.3337, -0.0641]]]		)
         # compare the actual values for a slice.
         self.assertTrue(torch.allclose(output[:, :3, :3]							,				A							,				atol=1E-4		)		)













from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

__UpperCAmelCase       :   str				       =							TypeVar("T")







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    int:
  return (position - 1) // 2







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    int:
  return (2 * position) + 1







def 			A__							(    SCREAMING_SNAKE_CASE__)   ->    int:
  return (2 * position) + 2







class     __snake_case       (							Generic[T]			):





       '''simple docstring'''







       def __init__(  self :    Tuple		):
         __snake_case:						list[tuple[T, int]]   =						[]
         __snake_case:						dict[T, int]   =						{}
         __snake_case:						int   =						0




       def __len__(  self :    Tuple		):
         return self.elements




       def __repr__(  self :    Optional[int]		):
         return str(self.heap		)




       def 							UpperCAmelCase__ (  self :    List[Any]		):
         # Check if the priority queue is empty
         return self.elements == 0




       def 							UpperCAmelCase__ (  self :    List[Any]							,				A :    T							,				A :    int		):
         # Add an element with given priority to the queue
         self.heap.append((elem, weight)		)
         __snake_case:						Optional[Any]   =						self.elements
         self.elements += 1
         self._bubble_up(A		)




       def 							UpperCAmelCase__ (  self :    List[Any]		):
         # Remove and return the element with lowest weight (highest priority)
         if self.elements > 1:
           self._swap_nodes(0							,				self.elements - 1		)
         __snake_case  ,   __snake_case:						Union[str, Any]   =						self.heap.pop()
         del self.position_map[elem]
         self.elements -= 1
         if self.elements > 0:
           __snake_case  ,   __snake_case:						int   =						self.heap[0]
           self._bubble_down(A		)
         return elem




       def 							UpperCAmelCase__ (  self :    Any							,				A :    T							,				A :    int		):
         # Update the weight of the given key
         __snake_case:						Dict   =						self.position_map[elem]
         __snake_case:						Optional[Any]   =						(elem, weight)
         if position > 0:
           __snake_case:						Optional[int]   =						get_parent_position(A		)
           __snake_case  ,   __snake_case:						Dict   =						self.heap[parent_position]
           if parent_weight > weight:
             self._bubble_up(A		)
           else:
             self._bubble_down(A		)
         else:
           self._bubble_down(A		)




       def 							UpperCAmelCase__ (  self :    int							,				A :    T		):
         # Place a node at the proper position (upward movement) [to be used internally
         # only]
         __snake_case:						int   =						self.position_map[elem]
         if curr_pos == 0:
           return None
         __snake_case:						Optional[Any]   =						get_parent_position(A		)
         __snake_case  ,   __snake_case:						Tuple   =						self.heap[curr_pos]
         __snake_case  ,   __snake_case:						Optional[Any]   =						self.heap[parent_position]
         if parent_weight > weight:
           self._swap_nodes(A							,				A		)
           return self._bubble_up(A		)
         return None




       def 							UpperCAmelCase__ (  self :    List[str]							,				A :    T		):
         # Place a node at the proper position (downward movement) [to be used
         # internally only]
         __snake_case:						Any   =						self.position_map[elem]
         __snake_case  ,   __snake_case:						int   =						self.heap[curr_pos]
         __snake_case:						Union[str, Any]   =						get_child_left_position(A		)
         __snake_case:						Dict   =						get_child_right_position(A		)
         if child_left_position < self.elements and child_right_position < self.elements:
           __snake_case  ,   __snake_case:						Any   =						self.heap[child_left_position]
           __snake_case  ,   __snake_case:						List[str]   =						self.heap[child_right_position]
           if child_right_weight < child_left_weight and child_right_weight < weight:
             self._swap_nodes(A							,				A		)
             return self._bubble_down(A		)
         if child_left_position < self.elements:
           __snake_case  ,   __snake_case:						List[Any]   =						self.heap[child_left_position]
           if child_left_weight < weight:
             self._swap_nodes(A							,				A		)
             return self._bubble_down(A		)
         else:
           return None
         if child_right_position < self.elements:
           __snake_case  ,   __snake_case:						List[Any]   =						self.heap[child_right_position]
           if child_right_weight < weight:
             self._swap_nodes(A							,				A		)
             return self._bubble_down(A		)
         return None






       def 							UpperCAmelCase__ (  self :    int							,				A :    int							,				A :    int		):
         # Swap the nodes at the given positions
         __snake_case:						Optional[int]   =						self.heap[nodea_pos][0]
         __snake_case:						Optional[Any]   =						self.heap[nodea_pos][0]
         __snake_case  ,   __snake_case:						Tuple   =						(
             self.heap[nodea_pos],
             self.heap[nodea_pos],
         )
         __snake_case:						Any   =						nodea_pos
         __snake_case:						str   =						nodea_pos







class     __snake_case       (							Generic[T]			):





       '''simple docstring'''







       def __init__(  self :    Any		):
         __snake_case:						dict[T, dict[T, int]]   =						{}
         __snake_case:						int   =						0




       def __repr__(  self :    Dict		):
         return str(self.connections		)




       def __len__(  self :    List[str]		):
         return self.nodes




       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    T		):
         # Add a node in the graph if it is not in the graph
         if node not in self.connections:
           __snake_case:						List[str]   =						{}
           self.nodes += 1






       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    T							,				A :    T							,				A :    int		):
         # Add an edge between 2 nodes in the graph
         self.add_node(A		)
         self.add_node(A		)
         __snake_case:						Optional[Any]   =						weight
         __snake_case:						str   =						weight







def 			A__							(    SCREAMING_SNAKE_CASE__					,					)   ->    tuple[dict[T, int], dict[T, T | None]]:
  __snake_case:						dict[T, int]   =						{node: maxsize for node in graph.connections}
  __snake_case:						dict[T, T | None]   =						{node: None for node in graph.connections}

  __snake_case:						MinPriorityQueue[T]   =						MinPriorityQueue()
  for node, weight in dist.items():
    priority_queue.push(SCREAMING_SNAKE_CASE__					,					SCREAMING_SNAKE_CASE__)

  if priority_queue.is_empty():
    return dist, parent

  # initialization
  __snake_case:						Optional[int]   =						priority_queue.extract_min()
  __snake_case:						Any   =						0
  for neighbour in graph.connections[node]:
    if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
      __snake_case:						Tuple   =						dist[node] + graph.connections[node][neighbour]
      priority_queue.update_key(SCREAMING_SNAKE_CASE__					,					dist[neighbour])
      __snake_case:						Optional[int]   =						node

    # running prim's algorithm
  while not priority_queue.is_empty():
    __snake_case:						Optional[int]   =						priority_queue.extract_min()
    for neighbour in graph.connections[node]:
      if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
        __snake_case:						Tuple   =						dist[node] + graph.connections[node][neighbour]
        priority_queue.update_key(SCREAMING_SNAKE_CASE__					,					dist[neighbour])
        __snake_case:						List[Any]   =						node
  return dist, parent






