



'''simple docstring'''





import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           DebertaForMaskedLM,
           DebertaForQuestionAnswering,
           DebertaForSequenceClassification,
           DebertaForTokenClassification,
           DebertaModel,
       )
       from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    def __init__(    self					:     Optional[Any] ,     snake_case_					:     Any ,     snake_case_					:     Tuple=13 ,     snake_case_					:     List[Any]=7 ,     snake_case_					:     Tuple=True ,     snake_case_					:     str=True ,     snake_case_					:     List[Any]=True ,     snake_case_					:     str=True ,     snake_case_					:     str=99 ,     snake_case_					:     Any=32 ,     snake_case_					:     List[Any]=5 ,     snake_case_					:     int=4 ,     snake_case_					:     Optional[int]=37 ,     snake_case_					:     str="gelu" ,     snake_case_					:     str=0.1 ,     snake_case_					:     List[str]=0.1 ,     snake_case_					:     List[str]=512 ,     snake_case_					:     Any=16 ,     snake_case_					:     int=2 ,     snake_case_					:     List[Any]=0.02 ,     snake_case_					:     List[str]=False ,     snake_case_					:     int=True ,     snake_case_					:     Union[str, Any]="None" ,     snake_case_					:     List[Any]=3 ,     snake_case_					:     List[str]=4 ,     snake_case_					:     Any=None ,     ):
          snake_case__  :    List[str]    						=      parent
          snake_case__  :    Any    						=      batch_size
          snake_case__  :    List[Any]    						=      seq_length
          snake_case__  :    str    						=      is_training
          snake_case__  :    List[str]    						=      use_input_mask
          snake_case__  :    List[str]    						=      use_token_type_ids
          snake_case__  :    Optional[int]    						=      use_labels
          snake_case__  :    Dict    						=      vocab_size
          snake_case__  :    Tuple    						=      hidden_size
          snake_case__  :    Tuple    						=      num_hidden_layers
          snake_case__  :    List[str]    						=      num_attention_heads
          snake_case__  :    Optional[int]    						=      intermediate_size
          snake_case__  :    List[Any]    						=      hidden_act
          snake_case__  :    Union[str, Any]    						=      hidden_dropout_prob
          snake_case__  :    List[Any]    						=      attention_probs_dropout_prob
          snake_case__  :    List[str]    						=      max_position_embeddings
          snake_case__  :    Optional[Any]    						=      type_vocab_size
          snake_case__  :    Any    						=      type_sequence_label_size
          snake_case__  :    int    						=      initializer_range
          snake_case__  :    Union[str, Any]    						=      num_labels
          snake_case__  :    Optional[Any]    						=      num_choices
          snake_case__  :    List[Any]    						=      relative_attention
          snake_case__  :    List[Any]    						=      position_biased_input
          snake_case__  :    str    						=      pos_att_type
          snake_case__  :    Union[str, Any]    						=      scope




    def lowerCamelCase       (    self					:     int						):
          snake_case__  :    Optional[int]    						=      ids_tensor([self.batch_size, self.seq_length] ,     self.vocab_size						)

          snake_case__  :    int    						=      None
          if self.use_input_mask:
                snake_case__  :    Any    						=      ids_tensor([self.batch_size, self.seq_length] ,     vocab_size=2						)

          snake_case__  :    Optional[int]    						=      None
          if self.use_token_type_ids:
                snake_case__  :    str    						=      ids_tensor([self.batch_size, self.seq_length] ,     self.type_vocab_size						)

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

          snake_case__  :    List[Any]    						=      self.get_config()

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




    def lowerCamelCase       (    self					:     Tuple						):
          return DebertaConfig(
              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 ,     type_vocab_size=self.type_vocab_size ,     initializer_range=self.initializer_range ,     relative_attention=self.relative_attention ,     position_biased_input=self.position_biased_input ,     pos_att_type=self.pos_att_type ,     )




    def lowerCamelCase       (    self					:     List[Any]						):
          snake_case__  :    Any    						=      self.get_config()
          snake_case__  :    List[Any]    						=      300
          return config




    def lowerCamelCase       (    self					:     Dict ,     snake_case_					:     Optional[Any]						):
          self.parent.assertListEqual(list(result.loss.size()						) ,     []						)




    def lowerCamelCase       (    self					:     List[Any] ,     snake_case_					:     Optional[Any] ,     snake_case_					:     Tuple ,     snake_case_					:     int ,     snake_case_					:     List[str] ,     snake_case_					:     int ,     snake_case_					:     str ,     snake_case_					:     Optional[Any]						):
          snake_case__  :    Optional[int]    						=      DebertaModel(config=snake_case_						)
          model.to(snake_case_						)
          model.eval()
          snake_case__  :    Union[str, Any]    						=      model(snake_case_ ,     attention_mask=snake_case_ ,     token_type_ids=snake_case_						)[0]
          snake_case__  :    int    						=      model(snake_case_ ,     token_type_ids=snake_case_						)[0]
          snake_case__  :    int    						=      model(snake_case_						)[0]

          self.parent.assertListEqual(list(sequence_output.size()						) ,     [self.batch_size, self.seq_length, self.hidden_size]						)




    def lowerCamelCase       (    self					:     Dict ,     snake_case_					:     Tuple ,     snake_case_					:     Dict ,     snake_case_					:     Dict ,     snake_case_					:     Optional[Any] ,     snake_case_					:     Union[str, Any] ,     snake_case_					:     Dict ,     snake_case_					:     str						):
          snake_case__  :    List[str]    						=      DebertaForMaskedLM(config=snake_case_						)
          model.to(snake_case_						)
          model.eval()
          snake_case__  :    Dict    						=      model(snake_case_ ,     attention_mask=snake_case_ ,     token_type_ids=snake_case_ ,     labels=snake_case_						)

          self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.seq_length, self.vocab_size)						)




    def lowerCamelCase       (    self					:     Optional[int] ,     snake_case_					:     Optional[Any] ,     snake_case_					:     Tuple ,     snake_case_					:     Tuple ,     snake_case_					:     Union[str, Any] ,     snake_case_					:     str ,     snake_case_					:     Optional[int] ,     snake_case_					:     int						):
          snake_case__  :    int    						=      self.num_labels
          snake_case__  :    Dict    						=      DebertaForSequenceClassification(snake_case_						)
          model.to(snake_case_						)
          model.eval()
          snake_case__  :    List[str]    						=      model(snake_case_ ,     attention_mask=snake_case_ ,     token_type_ids=snake_case_ ,     labels=snake_case_						)
          self.parent.assertListEqual(list(result.logits.size()						) ,     [self.batch_size, self.num_labels]						)
          self.check_loss_output(snake_case_						)




    def lowerCamelCase       (    self					:     Optional[Any] ,     snake_case_					:     List[str] ,     snake_case_					:     Optional[Any] ,     snake_case_					:     List[Any] ,     snake_case_					:     Optional[Any] ,     snake_case_					:     Dict ,     snake_case_					:     int ,     snake_case_					:     str						):
          snake_case__  :    Optional[int]    						=      self.num_labels
          snake_case__  :    Tuple    						=      DebertaForTokenClassification(config=snake_case_						)
          model.to(snake_case_						)
          model.eval()
          snake_case__  :    Optional[int]    						=      model(snake_case_ ,     attention_mask=snake_case_ ,     token_type_ids=snake_case_ ,     labels=snake_case_						)
          self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.seq_length, self.num_labels)						)




    def lowerCamelCase       (    self					:     Dict ,     snake_case_					:     Optional[Any] ,     snake_case_					:     List[Any] ,     snake_case_					:     Optional[Any] ,     snake_case_					:     Optional[int] ,     snake_case_					:     List[Any] ,     snake_case_					:     Optional[int] ,     snake_case_					:     Optional[Any]						):
          snake_case__  :    str    						=      DebertaForQuestionAnswering(config=snake_case_						)
          model.to(snake_case_						)
          model.eval()
          snake_case__  :    Union[str, Any]    						=      model(
              snake_case_ ,     attention_mask=snake_case_ ,     token_type_ids=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 lowerCamelCase       (    self					:     str						):
          snake_case__  :    List[str]    						=      self.prepare_config_and_inputs()
          (
              (
              snake_case__
          )						,				(
              snake_case__
          )						,				(
              snake_case__
          )						,				(
              snake_case__
          )						,				(
              snake_case__
          )						,				(
              snake_case__
          )						,				(
              snake_case__
          )						,				
          )  :    str    						=      config_and_inputs
          snake_case__  :    int    						=      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
          return config, inputs_dict







@require_torch
class     UpperCAmelCase_       (       _a		,		_a		,		unittest.TestCase	):
    """simple docstring"""







    lowercase					=		(
        (
            DebertaModel,
            DebertaForMaskedLM,
            DebertaForSequenceClassification,
            DebertaForTokenClassification,
            DebertaForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    lowercase					=		(
        {
            "feature-extraction": DebertaModel,
            "fill-mask": DebertaForMaskedLM,
            "question-answering": DebertaForQuestionAnswering,
            "text-classification": DebertaForSequenceClassification,
            "token-classification": DebertaForTokenClassification,
            "zero-shot": DebertaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )

    lowercase					=		True
    lowercase					=		False
    lowercase					=		False
    lowercase					=		False
    lowercase					=		False




    def lowerCamelCase       (    self					:     int						):
          snake_case__  :    Union[str, Any]    						=      DebertaModelTester(self						)
          snake_case__  :    List[str]    						=      ConfigTester(self ,     config_class=snake_case_ ,     hidden_size=37						)




    def lowerCamelCase       (    self					:     int						):
          self.config_tester.run_common_tests()




    def lowerCamelCase       (    self					:     Optional[Any]						):
          snake_case__  :    List[Any]    						=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_model(*snake_case_						)




    def lowerCamelCase       (    self					:     int						):
          snake_case__  :    Any    						=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_sequence_classification(*snake_case_						)




    def lowerCamelCase       (    self					:     Optional[Any]						):
          snake_case__  :    Optional[int]    						=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_masked_lm(*snake_case_						)




    def lowerCamelCase       (    self					:     Dict						):
          snake_case__  :    Dict    						=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_question_answering(*snake_case_						)




    def lowerCamelCase       (    self					:     Dict						):
          snake_case__  :    List[str]    						=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_token_classification(*snake_case_						)




    @slow
    def lowerCamelCase       (    self					:     Optional[int]						):
          for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                snake_case__  :    Union[str, Any]    						=      DebertaModel.from_pretrained(snake_case_						)
                self.assertIsNotNone(snake_case_						)







@require_torch
@require_sentencepiece
@require_tokenizers
class     UpperCAmelCase_       (       unittest.TestCase	):
    """simple docstring"""







    @unittest.skip(reason="""Model not available yet"""						)
    def lowerCamelCase       (    self					:     int						):
          pass




    @slow
    def lowerCamelCase       (    self					:     Tuple						):
          snake_case__  :    List[str]    						=      DebertaModel.from_pretrained("""microsoft/deberta-base"""						)

          snake_case__  :    Optional[Any]    						=      torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]						)
          snake_case__  :    int    						=      torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
          with torch.no_grad():
                snake_case__  :    Any    						=      model(snake_case_ ,     attention_mask=snake_case_						)[0]
          # compare the actual values for a slice.
          snake_case__  :    Optional[int]    						=      torch.tensor(
              [[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]]						)
          self.assertTrue(torch.allclose(output[:, 1:4, 1:4] ,     snake_case_ ,     atol=1E-4						) ,     f"{output[:, 1:4, 1:4]}"						)





'''simple docstring'''





import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__a          =		logging.get_logger(__name__)







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    def __init__(    self					:     List[str] ,     *snake_case_					:     str ,     **snake_case_					:     List[str]						):
          warnings.warn(
              """The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
              """ use BeitImageProcessor instead.""" ,     snake_case_ ,     )
          super().__init__(*snake_case_ ,     **snake_case_						)
