



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 				lowercase__       :







       '''simple docstring'''


       def __init__(							self      :  Dict		,    _UpperCAmelCase      :  Any		,    _UpperCAmelCase      :  Optional[Any]=13		,    _UpperCAmelCase      :  str=7		,    _UpperCAmelCase      :  str=True		,    _UpperCAmelCase      :  Dict=True		,    _UpperCAmelCase      :  Optional[int]=False		,    _UpperCAmelCase      :  Any=True		,    _UpperCAmelCase      :  Any=99		,    _UpperCAmelCase      :  Union[str, Any]=64		,    _UpperCAmelCase      :  Any=5		,    _UpperCAmelCase      :  int=4		,    _UpperCAmelCase      :  Tuple=64		,    _UpperCAmelCase      :  str="gelu"		,    _UpperCAmelCase      :  List[Any]=0.1		,    _UpperCAmelCase      :  Union[str, Any]=0.1		,    _UpperCAmelCase      :  int=512		,    _UpperCAmelCase      :  List[Any]=16		,    _UpperCAmelCase      :  Union[str, Any]=2		,    _UpperCAmelCase      :  Union[str, Any]=0.02		,    _UpperCAmelCase      :  Optional[Any]=3		,    _UpperCAmelCase      :  Union[str, Any]=4		,    _UpperCAmelCase      :  Any=None		,    )			->						Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_      			=							parent
             UpperCAmelCase_      			=							batch_size
             UpperCAmelCase_      			=							seq_length
             UpperCAmelCase_      			=							is_training
             UpperCAmelCase_      			=							use_input_mask
             UpperCAmelCase_      			=							use_token_type_ids
             UpperCAmelCase_      			=							use_labels
             UpperCAmelCase_      			=							vocab_size
             UpperCAmelCase_      			=							hidden_size
             UpperCAmelCase_      			=							num_hidden_layers
             UpperCAmelCase_      			=							num_attention_heads
             UpperCAmelCase_      			=							intermediate_size
             UpperCAmelCase_      			=							hidden_act
             UpperCAmelCase_      			=							hidden_dropout_prob
             UpperCAmelCase_      			=							attention_probs_dropout_prob
             UpperCAmelCase_      			=							max_position_embeddings
             UpperCAmelCase_      			=							type_vocab_size
             UpperCAmelCase_      			=							type_sequence_label_size
             UpperCAmelCase_      			=							initializer_range
             UpperCAmelCase_      			=							num_labels
             UpperCAmelCase_      			=							num_choices
             UpperCAmelCase_      			=							scope



       def 					lowercase__    (							self      :  Tuple   )			->						Any:


             '''simple docstring'''




             return MPNetConfig.from_pretrained("microsoft/mpnet-base"   )



       def 					lowercase__    (							self      :  Any   )			->						int:


             '''simple docstring'''




             UpperCAmelCase_      			=							ids_tensor([self.batch_size, self.seq_length]		,    self.vocab_size   )

             UpperCAmelCase_      			=							None
             if self.use_input_mask:
                   UpperCAmelCase_      			=							random_attention_mask([self.batch_size, self.seq_length]   )

             UpperCAmelCase_      			=							None
             UpperCAmelCase_      			=							None
             UpperCAmelCase_      			=							None
             if self.use_labels:
                   UpperCAmelCase_      			=							ids_tensor([self.batch_size]		,    self.type_sequence_label_size   )
                   UpperCAmelCase_      			=							ids_tensor([self.batch_size, self.seq_length]		,    self.num_labels   )
                   UpperCAmelCase_      			=							ids_tensor([self.batch_size]		,    self.num_choices   )

             UpperCAmelCase_      			=							self.get_config()
             return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



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


             '''simple docstring'''




             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 					lowercase__    (							self      :  Dict		,    _UpperCAmelCase      :  Optional[Any]		,    _UpperCAmelCase      :  Any		,    _UpperCAmelCase      :  str		,    _UpperCAmelCase      :  List[Any]		,    _UpperCAmelCase      :  Dict		,    _UpperCAmelCase      :  Tuple   )			->						Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							MPNetModel(config=lowerCAmelCase__   )
             model.to(lowerCAmelCase__   )
             model.eval()
             UpperCAmelCase_      			=							model(lowerCAmelCase__		,    lowerCAmelCase__   )
             UpperCAmelCase_      			=							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 					lowercase__    (							self      :  Dict		,    _UpperCAmelCase      :  Tuple		,    _UpperCAmelCase      :  Tuple		,    _UpperCAmelCase      :  Optional[Any]		,    _UpperCAmelCase      :  List[Any]		,    _UpperCAmelCase      :  str		,    _UpperCAmelCase      :  int   )			->						int:


             '''simple docstring'''




             UpperCAmelCase_      			=							MPNetForQuestionAnswering(config=lowerCAmelCase__   )
             model.to(lowerCAmelCase__   )
             model.eval()
             UpperCAmelCase_      			=							model(
                 lowerCAmelCase__		,    attention_mask=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 					lowercase__    (							self      :  List[str]		,    _UpperCAmelCase      :  List[str]		,    _UpperCAmelCase      :  List[str]		,    _UpperCAmelCase      :  Any		,    _UpperCAmelCase      :  Optional[Any]		,    _UpperCAmelCase      :  List[str]		,    _UpperCAmelCase      :  int   )			->						Any:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.num_labels
             UpperCAmelCase_      			=							MPNetForSequenceClassification(lowerCAmelCase__   )
             model.to(lowerCAmelCase__   )
             model.eval()
             UpperCAmelCase_      			=							model(lowerCAmelCase__		,    attention_mask=lowerCAmelCase__		,    labels=lowerCAmelCase__   )
             self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.num_labels)   )



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


             '''simple docstring'''




             UpperCAmelCase_      			=							self.num_choices
             UpperCAmelCase_      			=							MPNetForMultipleChoice(config=lowerCAmelCase__   )
             model.to(lowerCAmelCase__   )
             model.eval()
             UpperCAmelCase_      			=							input_ids.unsqueeze(1   ).expand(-1		,    self.num_choices		,    -1   ).contiguous()
             UpperCAmelCase_      			=							input_mask.unsqueeze(1   ).expand(-1		,    self.num_choices		,    -1   ).contiguous()
             UpperCAmelCase_      			=							model(
                 lowerCAmelCase__		,    attention_mask=lowerCAmelCase__		,    labels=lowerCAmelCase__		,    )
             self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.num_choices)   )



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


             '''simple docstring'''




             UpperCAmelCase_      			=							self.num_labels
             UpperCAmelCase_      			=							MPNetForTokenClassification(config=lowerCAmelCase__   )
             model.to(lowerCAmelCase__   )
             model.eval()
             UpperCAmelCase_      			=							model(lowerCAmelCase__		,    attention_mask=lowerCAmelCase__		,    labels=lowerCAmelCase__   )
             self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.seq_length, self.num_labels)   )







       def 					lowercase__    (							self      :  str   )			->						Dict:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.prepare_config_and_inputs()
             (UpperCAmelCase_)      			=							config_and_inputs
             UpperCAmelCase_      			=							{"input_ids": input_ids, "attention_mask": input_mask}
             return config, inputs_dict



@require_torch
class 				lowercase__       (   SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , unittest.TestCase				):







       '''simple docstring'''


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



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


             '''simple docstring'''




             UpperCAmelCase_      			=							MPNetModelTester(self   )
             UpperCAmelCase_      			=							ConfigTester(self		,    config_class=lowerCAmelCase__		,    hidden_size=37   )



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


             '''simple docstring'''




             self.config_tester.run_common_tests()



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


             '''simple docstring'''




             UpperCAmelCase_      			=							self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_mpnet_model(*lowerCAmelCase__   )



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


             '''simple docstring'''




             UpperCAmelCase_      			=							self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowerCAmelCase__   )



       def 					lowercase__    (							self      :  Dict   )			->						Dict:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowerCAmelCase__   )



       def 					lowercase__    (							self      :  Any   )			->						Dict:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_mpnet_for_token_classification(*lowerCAmelCase__   )







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


             '''simple docstring'''




             UpperCAmelCase_      			=							self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_mpnet_for_question_answering(*lowerCAmelCase__   )



@require_torch
class 				lowercase__       (   unittest.TestCase				):







       '''simple docstring'''


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


             '''simple docstring'''




             UpperCAmelCase_      			=							MPNetModel.from_pretrained("microsoft/mpnet-base"   )
             UpperCAmelCase_      			=							torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]   )
             UpperCAmelCase_      			=							model(lowerCAmelCase__   )[0]
             UpperCAmelCase_      			=							torch.Size((1, 11, 768)   )
             self.assertEqual(output.shape		,    lowerCAmelCase__   )
             UpperCAmelCase_      			=							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]		,    lowerCAmelCase__		,    atol=1e-4   )   )









"""simple docstring"""





from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass
@dataclass
class 				lowercase__       :







       '''simple docstring'''


       UpperCamelCase						=					42
       UpperCamelCase						=					None
       UpperCamelCase						=					None



lowerCamelCase       	=						namedtuple("""CoinsDistribResult""", """moves excess""")
def        a__     (				lowerCAmelCase__				):

      if root is None:
            return 0

      # Validation
      def count_nodes(lowerCAmelCase__				) -> int:
            if node is None:
                  return 0

            return count_nodes(node.left				) + count_nodes(node.right				) + 1

      def count_coins(lowerCAmelCase__				) -> int:
            if node is None:
                  return 0

            return count_coins(node.left				) + count_coins(node.right				) + node.data

      if count_nodes(lowerCAmelCase__				) != count_coins(lowerCAmelCase__				):
            raise ValueError("The nodes number should be same as the number of coins"				)

      # Main calculation
      def get_distrib(lowerCAmelCase__				) -> CoinsDistribResult:

            if node is None:
                  return CoinsDistribResult(0     ,      1				)

            UpperCAmelCase_			,   UpperCAmelCase_      			=							get_distrib(node.left				)
            UpperCAmelCase_			,   UpperCAmelCase_      			=							get_distrib(node.right				)

            UpperCAmelCase_      			=							1 - left_distrib_excess
            UpperCAmelCase_      			=							1 - right_distrib_excess

            UpperCAmelCase_      			=							(
                left_distrib_moves
                + right_distrib_moves
                + abs(lowerCAmelCase__				)
                + abs(lowerCAmelCase__				)
            )
            UpperCAmelCase_      			=							node.data - coins_to_left - coins_to_right

            return CoinsDistribResult(lowerCAmelCase__     ,      lowerCAmelCase__				)

      return get_distrib(lowerCAmelCase__				)[0]


if __name__ == "__main__":
  import doctest

  doctest.testmod()




