

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      UpperCamelCase :

    def __init__(			self :      Union[str, Any]							,    snake_case__ :      int							,    snake_case__ :      Union[str, Any]=1_3							,    snake_case__ :      int=7							,    snake_case__ :      Optional[int]=True							,    snake_case__ :      str=True							,    snake_case__ :      Dict=False							,    snake_case__ :      Tuple=True							,    snake_case__ :      List[str]=9_9							,    snake_case__ :      Tuple=6_4							,    snake_case__ :      int=5							,    snake_case__ :      Any=4							,    snake_case__ :      List[str]=6_4							,    snake_case__ :      int="gelu"							,    snake_case__ :      Any=0.1							,    snake_case__ :      str=0.1							,    snake_case__ :      str=5_1_2							,    snake_case__ :      str=1_6							,    snake_case__ :      Tuple=2							,    snake_case__ :      Any=0.02							,    snake_case__ :      str=3							,    snake_case__ :      Optional[int]=4							,    snake_case__ :      Tuple=None							,    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






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

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

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

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

    def UpperCamelCase			(			self :      str    ):

     """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 UpperCamelCase			(			self :      str							,    snake_case__ :      Dict							,    snake_case__ :      str							,    snake_case__ :      Any							,    snake_case__ :      int							,    snake_case__ :      List[Any]							,    snake_case__ :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			MPNetModel(config=__A    )
     model.to(__A    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(__A							,    __A    )
     SCREAMING_SNAKE_CASE								=			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 :      Tuple							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      List[str]							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      List[Any]							,    snake_case__ :      int							,    snake_case__ :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			MPNetForQuestionAnswering(config=__A    )
     model.to(__A    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			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 :      Tuple							,    snake_case__ :      int							,    snake_case__ :      Tuple							,    snake_case__ :      int							,    snake_case__ :      str							,    snake_case__ :      List[str]							,    snake_case__ :      str    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      Any							,    snake_case__ :      List[str]							,    snake_case__ :      Any							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      Any							,    snake_case__ :      Optional[Any]							,    snake_case__ :      List[Any]    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      str							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      List[str]							,    snake_case__ :      Optional[int]							,    snake_case__ :      List[Any]							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      List[str]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.num_labels
     SCREAMING_SNAKE_CASE								=			MPNetForTokenClassification(config=__A    )
     model.to(__A    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			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 :      Any    ):

     """simple docstring"""






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




@require_torch
class      UpperCamelCase (     UpperCamelCase_	,			UpperCamelCase_	,			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]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			MPNetModelTester(self    )
     SCREAMING_SNAKE_CASE								=			ConfigTester(self							,    config_class=__A							,    hidden_size=3_7    )

    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     self.config_tester.run_common_tests()

    def UpperCamelCase			(			self :      Optional[int]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_model(*__A    )

    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_sequence_classification(*__A    )

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_multiple_choice(*__A    )

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_token_classification(*__A    )




    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_mpnet_for_question_answering(*__A    )




@require_torch
class      UpperCamelCase (     unittest.TestCase   ):

    @slow
    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			MPNetModel.from_pretrained('microsoft/mpnet-base'    )
     SCREAMING_SNAKE_CASE								=			torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]    )
     SCREAMING_SNAKE_CASE								=			model(__A    )[0]
     SCREAMING_SNAKE_CASE								=			torch.Size((1, 1_1, 7_6_8)    )
     self.assertEqual(output.shape							,    __A    )
     SCREAMING_SNAKE_CASE								=			torch.tensor(
         [[[-0.0_550, 0.1_943, -0.0_740], [-0.0_562, 0.2_211, -0.0_579], [-0.0_437, 0.3_337, -0.0_641]]]    )
     # compare the actual values for a slice.
     self.assertTrue(torch.allclose(output[:, :3, :3]							,    __A							,    atol=1E-4    )    )


def        __lowerCAmelCase    (       _UpperCamelCase		:  int	)		->							int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			abs(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			0
 while n > 0:
  res += n % 10
  n //= 10
 return res


def        __lowerCAmelCase    (       _UpperCamelCase		:  int	)		->							int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			abs(_UpperCamelCase	)
 return n if n < 10 else n % 10 + sum_of_digits(n // 10	)


def        __lowerCAmelCase    (       _UpperCamelCase		:  int	)		->							int:
 '''simple docstring'''


 return sum(int(_UpperCamelCase	) for c in str(abs(_UpperCamelCase	)	)	)


def        __lowerCAmelCase    (       )		->							None:
 '''simple docstring'''


 from collections.abc import Callable
 from timeit import timeit

 def benchmark_a_function(_UpperCamelCase		:  Callable		,							_UpperCamelCase		:  int	) -> None:
  SCREAMING_SNAKE_CASE								=			f"""{func.__name__}({value})"""
  SCREAMING_SNAKE_CASE								=			timeit(f"""__main__.{call}"""		,							setup='import __main__'	)
  print(f"""{call:56} = {func(_UpperCamelCase	)} -- {timing:.4f} seconds"""	)

 for value in (26_21_44, 11_25_89_99_06_84_26_24, 1_26_76_50_60_02_28_22_94_01_49_67_03_20_53_76):
  for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
   benchmark_a_function(_UpperCamelCase		,							_UpperCamelCase	)
  print()


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()

