

"""simple docstring"""






import os
import tempfile
import unittest

from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, 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 (
     DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
     DistilBertForMaskedLM,
     DistilBertForMultipleChoice,
     DistilBertForQuestionAnswering,
     DistilBertForSequenceClassification,
     DistilBertForTokenClassification,
     DistilBertModel,
 )







class 	__UpperCamelCase    (    a__						):

  def __init__(		self    ,_A    ,_A=13    ,_A=7    ,_A=True    ,_A=True    ,_A=False    ,_A=True    ,_A=99    ,_A=32    ,_A=5    ,_A=4    ,_A=37    ,_A="gelu"    ,_A=0.1    ,_A=0.1    ,_A=512    ,_A=16    ,_A=2    ,_A=0.0_2    ,_A=3    ,_A=4    ,_A=None    ,):
      '''simple docstring'''

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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

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

      _lowerCAmelCase       :   List[str]  =    None
      if self.use_input_mask:
          _lowerCAmelCase       :   Dict  =    random_attention_mask([self.batch_size, self.seq_length]      )

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

      _lowerCAmelCase       :   Optional[Any]  =    self.get_config()

      return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return DistilBertConfig(
          vocab_size=self.vocab_size    ,dim=self.hidden_size    ,n_layers=self.num_hidden_layers    ,n_heads=self.num_attention_heads    ,hidden_dim=self.intermediate_size    ,hidden_act=self.hidden_act    ,dropout=self.hidden_dropout_prob    ,attention_dropout=self.attention_probs_dropout_prob    ,max_position_embeddings=self.max_position_embeddings    ,initializer_range=self.initializer_range    ,)

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    DistilBertModel(config=_A      )
      model.to(_A      )
      model.eval()
      _lowerCAmelCase       :   Optional[int]  =    model(_A    ,_A      )
      _lowerCAmelCase       :   List[Any]  =    model(_A      )
      self.parent.assertEqual(result.last_hidden_state.shape    ,(self.batch_size, self.seq_length, self.hidden_size)      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    DistilBertForMaskedLM(config=_A      )
      model.to(_A      )
      model.eval()
      _lowerCAmelCase       :   str  =    model(_A    ,attention_mask=_A    ,labels=_A      )
      self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.seq_length, self.vocab_size)      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    DistilBertForQuestionAnswering(config=_A      )
      model.to(_A      )
      model.eval()
      _lowerCAmelCase       :   List[Any]  =    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 __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    self.num_labels
      _lowerCAmelCase       :   Any  =    DistilBertForSequenceClassification(_A      )
      model.to(_A      )
      model.eval()
      _lowerCAmelCase       :   Tuple  =    model(_A    ,attention_mask=_A    ,labels=_A      )
      self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.num_labels)      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    self.num_labels
      _lowerCAmelCase       :   Any  =    DistilBertForTokenClassification(config=_A      )
      model.to(_A      )
      model.eval()

      _lowerCAmelCase       :   str  =    model(_A    ,attention_mask=_A    ,labels=_A      )
      self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.seq_length, self.num_labels)      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

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







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    self.prepare_config_and_inputs()
      ((_lowerCAmelCase),					(_lowerCAmelCase),					(_lowerCAmelCase),					(_lowerCAmelCase),					(_lowerCAmelCase),					(_lowerCAmelCase))       :   Union[str, Any]  =    config_and_inputs
      _lowerCAmelCase       :   Union[str, Any]  =    {'input_ids': input_ids, 'attention_mask': input_mask}
      return config, inputs_dict







@require_torch
class 	__UpperCamelCase    (    a__							,							a__							,							unittest.TestCase						):
  _UpperCAmelCase          =		(
      (
          DistilBertModel,
          DistilBertForMaskedLM,
          DistilBertForMultipleChoice,
          DistilBertForQuestionAnswering,
          DistilBertForSequenceClassification,
          DistilBertForTokenClassification,
      )
      if is_torch_available()
      else None
  )
  _UpperCAmelCase          =		(
      {
          "feature-extraction": DistilBertModel,
          "fill-mask": DistilBertForMaskedLM,
          "question-answering": DistilBertForQuestionAnswering,
          "text-classification": DistilBertForSequenceClassification,
          "token-classification": DistilBertForTokenClassification,
          "zero-shot": DistilBertForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  _UpperCAmelCase          =		True
  _UpperCAmelCase          =		True
  _UpperCAmelCase          =		True
  _UpperCAmelCase          =		True

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    DistilBertModelTester(self      )
      _lowerCAmelCase       :   List[Any]  =    ConfigTester(self    ,config_class=_A    ,dim=37      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      self.config_tester.run_common_tests()

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_distilbert_model(*_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_distilbert_for_masked_lm(*_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_distilbert_for_question_answering(*_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_distilbert_for_sequence_classification(*_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_distilbert_for_token_classification(*_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_distilbert_for_multiple_choice(*_A      )

  @slow
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _lowerCAmelCase       :   List[str]  =    DistilBertModel.from_pretrained(_A      )
          self.assertIsNotNone(_A      )







  @slow
  @require_torch_gpu
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase,					_lowerCAmelCase       :   Union[str, Any]  =    self.model_tester.prepare_config_and_inputs_for_common()
      for model_class in self.all_model_classes:
          # BertForMultipleChoice behaves incorrectly in JIT environments.
          if model_class == DistilBertForMultipleChoice:
              return

          _lowerCAmelCase       :   Dict  =    True
          _lowerCAmelCase       :   Tuple  =    model_class(config=_A      )

          _lowerCAmelCase       :   List[Any]  =    self._prepare_for_class(_A    ,_A      )
          _lowerCAmelCase       :   Dict  =    torch.jit.trace(
              _A    ,(inputs_dict['input_ids'].to('cpu'      ), inputs_dict['attention_mask'].to('cpu'      ))      )

          with tempfile.TemporaryDirectory() as tmp:
              torch.jit.save(_A    ,os.path.join(_A    ,'traced_model.pt'      )      )
              _lowerCAmelCase       :   int  =    torch.jit.load(os.path.join(_A    ,'traced_model.pt'      )    ,map_location=_A      )
              loaded(inputs_dict['input_ids'].to(_A      )    ,inputs_dict['attention_mask'].to(_A      )      )







@require_torch
class 	__UpperCamelCase    (    unittest.TestCase						):

  @slow
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    DistilBertModel.from_pretrained('distilbert-base-uncased'      )
      _lowerCAmelCase       :   Tuple  =    torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]      )
      _lowerCAmelCase       :   int  =    torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
      with torch.no_grad():
          _lowerCAmelCase       :   Union[str, Any]  =    model(_A    ,attention_mask=_A      )[0]
      _lowerCAmelCase       :   Tuple  =    torch.Size((1, 11, 768)      )
      self.assertEqual(output.shape    ,_A      )
      _lowerCAmelCase       :   int  =    torch.tensor(
          [[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]]      )

      self.assertTrue(torch.allclose(output[:, 1:4, 1:4]    ,_A    ,atol=1E-4      )      )








"""simple docstring"""






from __future__ import annotations

from collections.abc import Callable

_lowerCAmelCase						=		list[list[float | int]]

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   int  =    len(_lowerCamelCase   )
    _lowerCAmelCase       :   Matrix  =    [[0 for _ in range(size + 1   )] for _ in range(_lowerCamelCase   )]
    _lowerCAmelCase       :   int
    _lowerCAmelCase       :   int
    _lowerCAmelCase       :   int
    _lowerCAmelCase       :   int
    _lowerCAmelCase       :   int
    _lowerCAmelCase       :   float

    for row in range(_lowerCamelCase   ):
        for col in range(_lowerCamelCase   ):
            _lowerCAmelCase       :   Union[str, Any]  =    matrix[row][col]

        _lowerCAmelCase       :   Tuple  =    vector[row][0]

    _lowerCAmelCase       :   Dict  =    0
    _lowerCAmelCase       :   Any  =    0
    while row < size and col < size:
        # pivoting
        _lowerCAmelCase       :   Optional[int]  =    max((abs(augmented[rowa][col]   ), rowa) for rowa in range(_lowerCamelCase ,   _lowerCamelCase   )   )[
            1
        ]
        if augmented[pivot_row][col] == 0:
            col += 1
            continue
        else:
            _lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    augmented[pivot_row], augmented[row]

        for rowa in range(row + 1 ,   _lowerCamelCase   ):
            _lowerCAmelCase       :   Dict  =    augmented[rowa][col] / augmented[row][col]
            _lowerCAmelCase       :   Optional[Any]  =    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 ,   _lowerCamelCase   ):
        for row in range(_lowerCamelCase   ):
            _lowerCAmelCase       :   int  =    augmented[row][col] / augmented[col][col]
            for cola in range(_lowerCamelCase ,   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(_lowerCamelCase   )
    ]

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   int  =    len(_lowerCamelCase   )
    _lowerCAmelCase       :   Matrix  =    [[0 for _ in range(_lowerCamelCase   )] for _ in range(_lowerCamelCase   )]
    _lowerCAmelCase       :   Matrix  =    [[0] for _ in range(_lowerCamelCase   )]
    _lowerCAmelCase       :   Matrix
    _lowerCAmelCase       :   int
    _lowerCAmelCase       :   int
    _lowerCAmelCase       :   int

    for x_val, y_val in enumerate(_lowerCamelCase   ):
        for col in range(_lowerCamelCase   ):
            _lowerCAmelCase       :   Union[str, Any]  =    (x_val + 1) ** (size - col - 1)
        _lowerCAmelCase       :   Optional[int]  =    y_val

    _lowerCAmelCase       :   List[Any]  =    solve(_lowerCamelCase ,   _lowerCamelCase   )

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

    return interpolated_func

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    return (
        1
        - variable
        + variable**2
        - variable**3
        + variable**4
        - variable**5
        + variable**6
        - variable**7
        + variable**8
        - variable**9
        + variable**10
    )

def 		lowerCamelCase__		(					_lowerCamelCase = question_function ,   _lowerCamelCase = 10   ):

    '''simple docstring'''



    _lowerCAmelCase       :   list[int]  =    [func(_lowerCamelCase   ) for x_val in range(1 ,   order + 1   )]

    _lowerCAmelCase       :   list[Callable[[int], int]]  =    [
        interpolate(data_points[:max_coeff]   ) for max_coeff in range(1 ,   order + 1   )
    ]

    _lowerCAmelCase       :   int  =    0
    _lowerCAmelCase       :   Callable[[int], int]
    _lowerCAmelCase       :   int

    for poly in polynomials:
        _lowerCAmelCase       :   Any  =    1
        while func(_lowerCamelCase   ) == poly(_lowerCamelCase   ):
            x_val += 1

        ret += poly(_lowerCamelCase   )

    return ret


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





