

'''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 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  def __init__(      self   :     Optional[Any]					,lowercase__   :     Union[str, Any]					,lowercase__   :     int=1_3					,lowercase__   :     Optional[int]=7					,lowercase__   :     List[Any]=True					,lowercase__   :     Tuple=True					,lowercase__   :     List[Any]=False					,lowercase__   :     Dict=True					,lowercase__   :     List[str]=9_9					,lowercase__   :     List[str]=3_2					,lowercase__   :     Union[str, Any]=5					,lowercase__   :     int=4					,lowercase__   :     Tuple=3_7					,lowercase__   :     List[str]="gelu"					,lowercase__   :     Dict=0.1					,lowercase__   :     List[Any]=0.1					,lowercase__   :     Any=5_1_2					,lowercase__   :     Tuple=1_6					,lowercase__   :     Optional[Any]=2					,lowercase__   :     str=0.0_2					,lowercase__   :     List[Any]=3					,lowercase__   :     Any=4					,lowercase__   :     List[Any]=None					,):
   __lowercase							  = parent
   __lowercase							  = batch_size
   __lowercase							  = seq_length
   __lowercase							  = is_training
   __lowercase							  = use_input_mask
   __lowercase							  = use_token_type_ids
   __lowercase							  = use_labels
   __lowercase							  = vocab_size
   __lowercase							  = hidden_size
   __lowercase							  = num_hidden_layers
   __lowercase							  = num_attention_heads
   __lowercase							  = intermediate_size
   __lowercase							  = hidden_act
   __lowercase							  = hidden_dropout_prob
   __lowercase							  = attention_probs_dropout_prob
   __lowercase							  = max_position_embeddings
   __lowercase							  = type_vocab_size
   __lowercase							  = type_sequence_label_size
   __lowercase							  = initializer_range
   __lowercase							  = num_labels
   __lowercase							  = num_choices
   __lowercase							  = scope






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]			):
   __lowercase							  = ids_tensor([self.batch_size, self.seq_length]					,self.vocab_size			)

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

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

   __lowercase							  = self.get_config()

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






  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   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 					SCREAMING_SNAKE_CASE      (      self   :     Dict					,lowercase__   :     Optional[int]					,lowercase__   :     Tuple					,lowercase__   :     Tuple					,lowercase__   :     Union[str, Any]					,lowercase__   :     Any					,lowercase__   :     int			):
   __lowercase							  = DistilBertModel(config=lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__					,lowercase__			)
   __lowercase							  = model(lowercase__			)
   self.parent.assertEqual(result.last_hidden_state.shape					,(self.batch_size, self.seq_length, self.hidden_size)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]					,lowercase__   :     Optional[int]					,lowercase__   :     Optional[Any]					,lowercase__   :     Tuple					,lowercase__   :     Optional[int]					,lowercase__   :     List[Any]					,lowercase__   :     Tuple			):
   __lowercase							  = DistilBertForMaskedLM(config=lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__					,attention_mask=lowercase__					,labels=lowercase__			)
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.seq_length, self.vocab_size)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]					,lowercase__   :     Tuple					,lowercase__   :     int					,lowercase__   :     List[Any]					,lowercase__   :     int					,lowercase__   :     Dict					,lowercase__   :     Union[str, Any]			):
   __lowercase							  = DistilBertForQuestionAnswering(config=lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(
       lowercase__					,attention_mask=lowercase__					,start_positions=lowercase__					,end_positions=lowercase__			)
   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 					SCREAMING_SNAKE_CASE      (      self   :     Tuple					,lowercase__   :     Dict					,lowercase__   :     Tuple					,lowercase__   :     str					,lowercase__   :     Dict					,lowercase__   :     Optional[int]					,lowercase__   :     Tuple			):
   __lowercase							  = self.num_labels
   __lowercase							  = DistilBertForSequenceClassification(lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__					,attention_mask=lowercase__					,labels=lowercase__			)
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.num_labels)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]					,lowercase__   :     Optional[Any]					,lowercase__   :     Optional[Any]					,lowercase__   :     List[Any]					,lowercase__   :     Tuple					,lowercase__   :     Union[str, Any]					,lowercase__   :     Optional[int]			):
   __lowercase							  = self.num_labels
   __lowercase							  = DistilBertForTokenClassification(config=lowercase__			)
   model.to(lowercase__			)
   model.eval()

   __lowercase							  = model(lowercase__					,attention_mask=lowercase__					,labels=lowercase__			)
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.seq_length, self.num_labels)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]					,lowercase__   :     List[str]					,lowercase__   :     Dict					,lowercase__   :     Tuple					,lowercase__   :     List[Any]					,lowercase__   :     Optional[Any]					,lowercase__   :     Any			):
   __lowercase							  = self.num_choices
   __lowercase							  = DistilBertForMultipleChoice(config=lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = input_ids.unsqueeze(1			).expand(-1					,self.num_choices					,-1			).contiguous()
   __lowercase							  = input_mask.unsqueeze(1			).expand(-1					,self.num_choices					,-1			).contiguous()
   __lowercase							  = model(
       lowercase__					,attention_mask=lowercase__					,labels=lowercase__					,)
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.num_choices)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = self.prepare_config_and_inputs()
   ((__lowercase)  ,	(__lowercase)  ,	(__lowercase)  ,	(__lowercase)  ,	(__lowercase)  ,	(__lowercase))							  = config_and_inputs
   __lowercase							  = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
   return config, inputs_dict






@require_torch
class 			lowercase_	(lowerCamelCase__				,       lowerCamelCase__				,       unittest.TestCase      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  List[Any]							=			(
      (
          DistilBertModel,
          DistilBertForMaskedLM,
          DistilBertForMultipleChoice,
          DistilBertForQuestionAnswering,
          DistilBertForSequenceClassification,
          DistilBertForTokenClassification,
      )
      if is_torch_available()
      else None
  )
  SCREAMING_SNAKE_CASE    :  Dict							=			(
      {
          'feature-extraction': DistilBertModel,
          'fill-mask': DistilBertForMaskedLM,
          'question-answering': DistilBertForQuestionAnswering,
          'text-classification': DistilBertForSequenceClassification,
          'token-classification': DistilBertForTokenClassification,
          'zero-shot': DistilBertForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			True
  SCREAMING_SNAKE_CASE    :  Dict							=			True
  SCREAMING_SNAKE_CASE    :  Optional[Any]							=			True
  SCREAMING_SNAKE_CASE    :  int							=			True






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   __lowercase							  = DistilBertModelTester(self			)
   __lowercase							  = ConfigTester(self					,config_class=lowercase__					,dim=3_7			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   self.config_tester.run_common_tests()






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_distilbert_model(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_distilbert_for_masked_lm(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_distilbert_for_question_answering(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_distilbert_for_sequence_classification(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_distilbert_for_token_classification(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_distilbert_for_multiple_choice(*lowercase__			)






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __lowercase							  = DistilBertModel.from_pretrained(lowercase__			)
    self.assertIsNotNone(lowercase__			)






  @slow
  @require_torch_gpu
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase  ,	__lowercase							  = 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

    __lowercase							  = True
    __lowercase							  = model_class(config=lowercase__			)

    __lowercase							  = self._prepare_for_class(lowercase__					,lowercase__			)
    __lowercase							  = torch.jit.trace(
        lowercase__					,(inputs_dict['''input_ids'''].to('''cpu'''			), inputs_dict['''attention_mask'''].to('''cpu'''			))			)

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






@require_torch
class 			lowercase_	(unittest.TestCase      ):




  """simple docstring"""




  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase							  = DistilBertModel.from_pretrained('''distilbert-base-uncased'''			)
   __lowercase							  = 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]]			)
   __lowercase							  = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
   with torch.no_grad():
    __lowercase							  = model(lowercase__					,attention_mask=lowercase__			)[0]
   __lowercase							  = torch.Size((1, 1_1, 7_6_8)			)
   self.assertEqual(output.shape					,lowercase__			)
   __lowercase							  = 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]					,lowercase__					,atol=1e-4			)			)








"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


SCREAMING_SNAKE_CASE__					=   {}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__					=   ["BartphoTokenizer"]

if TYPE_CHECKING:
       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_bartpho import BartphoTokenizer

else:
       import sys

       SCREAMING_SNAKE_CASE__					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






