





'''simple docstring'''







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						:







     def __init__( self    ,   A__    ,   A__=13    ,   A__=7    ,   A__=True    ,   A__=True    ,   A__=False    ,   A__=True    ,   A__=99    ,   A__=64    ,   A__=5    ,   A__=4    ,   A__=64    ,   A__="gelu"    ,   A__=0.1    ,   A__=0.1    ,   A__=5_12    ,   A__=16    ,   A__=2    ,   A__=0.0_2    ,   A__=3    ,   A__=4    ,   A__=None    ,   ) ->	Tuple:
      snake_case				=			parent
      snake_case				=			batch_size
      snake_case				=			seq_length
      snake_case				=			is_training
      snake_case				=			use_input_mask
      snake_case				=			use_token_type_ids
      snake_case				=			use_labels
      snake_case				=			vocab_size
      snake_case				=			hidden_size
      snake_case				=			num_hidden_layers
      snake_case				=			num_attention_heads
      snake_case				=			intermediate_size
      snake_case				=			hidden_act
      snake_case				=			hidden_dropout_prob
      snake_case				=			attention_probs_dropout_prob
      snake_case				=			max_position_embeddings
      snake_case				=			type_vocab_size
      snake_case				=			type_sequence_label_size
      snake_case				=			initializer_range
      snake_case				=			num_labels
      snake_case				=			num_choices
      snake_case				=			scope







     def     UpperCamelCase			( self    ) ->	Tuple:
      return MPNetConfig.from_pretrained('''microsoft/mpnet-base'''    )







     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			ids_tensor([self.batch_size, self.seq_length]    ,   self.vocab_size    )

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

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

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







     def     UpperCamelCase			( self    ) ->	List[Any]:
      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    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ) ->	Any:
      snake_case				=			MPNetModel(config=snake_case__    )
      model.to(snake_case__    )
      model.eval()
      snake_case				=			model(snake_case__    ,   snake_case__    )
      snake_case				=			model(snake_case__    )
      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    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ) ->	Dict:
      snake_case				=			MPNetForQuestionAnswering(config=snake_case__    )
      model.to(snake_case__    )
      model.eval()
      snake_case				=			model(
          snake_case__    ,   attention_mask=snake_case__    ,   start_positions=snake_case__    ,   end_positions=snake_case__    ,   )
      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    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ) ->	Optional[Any]:
      snake_case				=			self.num_labels
      snake_case				=			MPNetForSequenceClassification(snake_case__    )
      model.to(snake_case__    )
      model.eval()
      snake_case				=			model(snake_case__    ,   attention_mask=snake_case__    ,   labels=snake_case__    )
      self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.num_labels)    )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ) ->	Any:
      snake_case				=			self.num_choices
      snake_case				=			MPNetForMultipleChoice(config=snake_case__    )
      model.to(snake_case__    )
      model.eval()
      snake_case				=			input_ids.unsqueeze(1    ).expand(-1    ,   self.num_choices    ,   -1    ).contiguous()
      snake_case				=			input_mask.unsqueeze(1    ).expand(-1    ,   self.num_choices    ,   -1    ).contiguous()
      snake_case				=			model(
          snake_case__    ,   attention_mask=snake_case__    ,   labels=snake_case__    ,   )
      self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.num_choices)    )







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ,   A__    ) ->	Tuple:
      snake_case				=			self.num_labels
      snake_case				=			MPNetForTokenClassification(config=snake_case__    )
      model.to(snake_case__    )
      model.eval()
      snake_case				=			model(snake_case__    ,   attention_mask=snake_case__    ,   labels=snake_case__    )
      self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.seq_length, self.num_labels)    )







     def     UpperCamelCase			( self    ) ->	int:
      snake_case				=			self.prepare_config_and_inputs()
      (snake_case)				=			config_and_inputs
      snake_case				=			{"input_ids": input_ids, "attention_mask": input_mask}
      return config, inputs_dict




@require_torch
class 			_lowercase						(       SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		unittest.TestCase					):
     _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     UpperCamelCase			( self    ) ->	List[str]:
      snake_case				=			MPNetModelTester(self    )
      snake_case				=			ConfigTester(self    ,   config_class=snake_case__    ,   hidden_size=37    )







     def     UpperCamelCase			( self    ) ->	Optional[int]:
      self.config_tester.run_common_tests()







     def     UpperCamelCase			( self    ) ->	Union[str, Any]:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_model(*snake_case__    )







     def     UpperCamelCase			( self    ) ->	List[str]:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_sequence_classification(*snake_case__    )







     def     UpperCamelCase			( self    ) ->	Tuple:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_multiple_choice(*snake_case__    )







     def     UpperCamelCase			( self    ) ->	Optional[int]:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_token_classification(*snake_case__    )







     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_question_answering(*snake_case__    )




@require_torch
class 			_lowercase						(       unittest.TestCase					):







     @slow
     def     UpperCamelCase			( self    ) ->	List[Any]:
      snake_case				=			MPNetModel.from_pretrained('''microsoft/mpnet-base'''    )
      snake_case				=			torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]    )
      snake_case				=			model(snake_case__    )[0]
      snake_case				=			torch.Size((1, 11, 7_68)    )
      self.assertEqual(output.shape    ,   snake_case__    )
      snake_case				=			torch.tensor(
          [[[-0.0_5_5_0, 0.1_9_4_3, -0.0_7_4_0], [-0.0_5_6_2, 0.2_2_1_1, -0.0_5_7_9], [-0.0_4_3_7, 0.3_3_3_7, -0.0_6_4_1]]]    )
      # compare the actual values for a slice.
      self.assertTrue(torch.allclose(output[:, :3, :3]    ,   snake_case__    ,   atol=1e-4    )    )








'''simple docstring'''







from __future__ import annotations

from collections.abc import Iterator
class 			_lowercase						:







     def __init__( self    ,   A__    ) ->	None:
      snake_case				=			value
      snake_case				=			None
      snake_case				=			None




class 			_lowercase						:







     def __init__( self    ,   A__    ) ->	None:
      snake_case				=			tree







     def     UpperCamelCase			( self    ,   A__    ) ->	int:
      if node is None:
       return 0
      return node.value + (
          self.depth_first_search(node.left    ) + self.depth_first_search(node.right    )
      )







     def __iter__( self    ) ->	Iterator[int]:
      yield self.depth_first_search(self.tree    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()

