





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 				__lowerCAmelCase       :







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__=1_3		,      lowerCAmelCase__=7		,      lowerCAmelCase__=True		,      lowerCAmelCase__=True		,      lowerCAmelCase__=False		,      lowerCAmelCase__=True		,      lowerCAmelCase__=9_9		,      lowerCAmelCase__=6_4		,      lowerCAmelCase__=5		,      lowerCAmelCase__=4		,      lowerCAmelCase__=6_4		,      lowerCAmelCase__="gelu"		,      lowerCAmelCase__=0.1		,      lowerCAmelCase__=0.1		,      lowerCAmelCase__=5_1_2		,      lowerCAmelCase__=1_6		,      lowerCAmelCase__=2		,      lowerCAmelCase__=0.02		,      lowerCAmelCase__=3		,      lowerCAmelCase__=4		,      lowerCAmelCase__=None		,      )   ->						Union[str, Any]:



        '''simple docstring'''




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







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




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







      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        a__	:					Tuple										=ids_tensor([self.batch_size, self.seq_length]		,      self.vocab_size							)

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

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

        a__	:					Dict										=self.get_config()
        return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels







      def    _lowercase  (    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    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					List[Any]										=MPNetModel(config=lowerCAmelCase__							)
        model.to(lowerCAmelCase__							)
        model.eval()
        a__	:					Dict										=model(lowerCAmelCase__		,      lowerCAmelCase__							)
        a__	:					int										=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		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					Any										=MPNetForQuestionAnswering(config=lowerCAmelCase__							)
        model.to(lowerCAmelCase__							)
        model.eval()
        a__	:					Dict										=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		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						Tuple:



        '''simple docstring'''




        a__	:					int										=self.num_labels
        a__	:					Optional[int]										=MPNetForSequenceClassification(lowerCAmelCase__							)
        model.to(lowerCAmelCase__							)
        model.eval()
        a__	:					int										=model(lowerCAmelCase__		,      attention_mask=lowerCAmelCase__		,      labels=lowerCAmelCase__							)
        self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_labels)							)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						Dict:



        '''simple docstring'''




        a__	:					Any										=self.num_choices
        a__	:					str										=MPNetForMultipleChoice(config=lowerCAmelCase__							)
        model.to(lowerCAmelCase__							)
        model.eval()
        a__	:					List[Any]										=input_ids.unsqueeze(1							).expand(-1		,      self.num_choices		,      -1							).contiguous()
        a__	:					int										=input_mask.unsqueeze(1							).expand(-1		,      self.num_choices		,      -1							).contiguous()
        a__	:					int										=model(
            lowerCAmelCase__		,      attention_mask=lowerCAmelCase__		,      labels=lowerCAmelCase__		,      )
        self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_choices)							)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						int:



        '''simple docstring'''




        a__	:					Optional[Any]										=self.num_labels
        a__	:					List[Any]										=MPNetForTokenClassification(config=lowerCAmelCase__							)
        model.to(lowerCAmelCase__							)
        model.eval()
        a__	:					Tuple										=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							)   ->						Tuple:



        '''simple docstring'''




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







@require_torch
class 				__lowerCAmelCase       (    UpperCamelCase__						,				UpperCamelCase__						,				unittest.TestCase):
      _lowercase				:					Optional[int]						=			(
          (
              MPNetForMaskedLM,
              MPNetForMultipleChoice,
              MPNetForQuestionAnswering,
              MPNetForSequenceClassification,
              MPNetForTokenClassification,
              MPNetModel,
          )
          if is_torch_available()
          else ()
      )
      _lowercase				:					Tuple						=			(
          {
              """feature-extraction""": MPNetModel,
              """fill-mask""": MPNetForMaskedLM,
              """question-answering""": MPNetForQuestionAnswering,
              """text-classification""": MPNetForSequenceClassification,
              """token-classification""": MPNetForTokenClassification,
              """zero-shot""": MPNetForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      _lowercase				:					Dict						=			False
      _lowercase				:					Optional[Any]						=			True







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					Union[str, Any]										=MPNetModelTester(self							)
        a__	:					Any										=ConfigTester(self		,      config_class=lowerCAmelCase__		,      hidden_size=3_7							)







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        self.config_tester.run_common_tests()







      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        a__	:					List[Any]										=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_model(*lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        a__	:					List[str]										=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        a__	:					Optional[Any]										=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowerCAmelCase__							)







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



        '''simple docstring'''




        a__	:					Tuple										=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_for_token_classification(*lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					Optional[Any]										=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mpnet_for_question_answering(*lowerCAmelCase__							)







@require_torch
class 				__lowerCAmelCase       (    unittest.TestCase):







      @slow
      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        a__	:					List[str]										=MPNetModel.from_pretrained("microsoft/mpnet-base"							)
        a__	:					List[Any]										=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]]							)
        a__	:					int										=model(lowerCAmelCase__							)[0]
        a__	:					Tuple										=torch.Size((1, 1_1, 7_6_8)							)
        self.assertEqual(output.shape		,      lowerCAmelCase__							)
        a__	:					Optional[Any]										=torch.tensor(
            [[[-0.05_50, 0.19_43, -0.07_40], [-0.05_62, 0.22_11, -0.05_79], [-0.04_37, 0.33_37, -0.06_41]]]							)
        # compare the actual values for a slice.
        self.assertTrue(torch.allclose(output[:, :3, :3]		,      lowerCAmelCase__		,      atol=1E-4							)							)










from __future__ import annotations

from collections.abc import Callable

UpperCAmelCase__   	=							list[list[float | int]]
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			Matrix:







 """simple docstring"""


 _lowercase							      =len(__snake_case						)
 _lowercase							      =[[0 for _ in range(size + 1						)] for _ in range(__snake_case						)]
 _lowercase							      =42
 _lowercase							      =42
 _lowercase							      =42
 _lowercase							      =42
 _lowercase							      =42
 _lowercase							      =42

 for row in range(__snake_case						):
  for col in range(__snake_case						):
   _lowercase							      =matrix[row][col]

  _lowercase							      =vector[row][0]

 _lowercase							      =0
 _lowercase							      =0
 while row < size and col < size:
  # pivoting
  _lowercase							      =max((abs(augmented[rowa][col]						), rowa) for rowa in range(__snake_case     ,						__snake_case						)						)[
      1
  ]
  if augmented[pivot_row][col] == 0:
   col += 1
   continue
  else:
   _lowercase      ,    _lowercase							      =augmented[pivot_row], augmented[row]

  for rowa in range(row + 1     ,						__snake_case						):
   _lowercase							      =augmented[rowa][col] / augmented[row][col]
   _lowercase							      =0
   for cola in range(col + 1     ,						size + 1						):
    augmented[rowa][cola] -= augmented[row][cola] * ratio

  row += 1
  col += 1

 # back substitution
 for col in range(1     ,						__snake_case						):
  for row in range(__snake_case						):
   _lowercase							      =augmented[row][col] / augmented[col][col]
   for cola in range(__snake_case     ,						size + 1						):
    augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
 return [
     [round(augmented[row][size] / augmented[row][row]     ,						10						)] for row in range(__snake_case						)
 ]
def 				UpperCAmelCase_					(							__snake_case						)     ->			Callable[[int], int]:







 """simple docstring"""


 _lowercase							      =len(__snake_case						)
 _lowercase							      =[[0 for _ in range(__snake_case						)] for _ in range(__snake_case						)]
 _lowercase							      =[[0] for _ in range(__snake_case						)]
 _lowercase							      =42
 _lowercase							      =42
 _lowercase							      =42
 _lowercase							      =42

 for x_val, y_val in enumerate(__snake_case						):
  for col in range(__snake_case						):
   _lowercase							      =(x_val + 1) ** (size - col - 1)
  _lowercase							      =y_val

 _lowercase							      =solve(__snake_case     ,						__snake_case						)

 def interpolated_func(__snake_case						) -> int:
  return sum(
      round(coeffs[x_val][0]						) * (var ** (size - x_val - 1))
      for x_val in range(__snake_case						)						)

 return interpolated_func
def 				UpperCAmelCase_					(							__snake_case						)     ->			int:







 """simple docstring"""


 return (
     1
     - variable
     + variable**2
     - variable**3
     + variable**4
     - variable**5
     + variable**6
     - variable**7
     + variable**8
     - variable**9
     + variable**10
 )
def 				UpperCAmelCase_					(							__snake_case = question_function     ,						__snake_case = 10						)     ->			int:







 """simple docstring"""


 _lowercase							      =[func(__snake_case						) for x_val in range(1     ,						order + 1						)]

 _lowercase							      =[
     interpolate(data_points[:max_coeff]						) for max_coeff in range(1     ,						order + 1						)
 ]

 _lowercase							      =0
 _lowercase							      =42
 _lowercase							      =42

 for poly in polynomials:
  _lowercase							      =1
  while func(__snake_case						) == poly(__snake_case						):
   x_val += 1

  ret += poly(__snake_case						)

 return ret


if __name__ == "__main__":
  print(f'''{solution() = }''')

