

'''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 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  def __init__(      self   :     Optional[Any]					,lowercase__   :     Optional[Any]					,lowercase__   :     Optional[int]=1_3					,lowercase__   :     Any=7					,lowercase__   :     Union[str, Any]=True					,lowercase__   :     Optional[int]=True					,lowercase__   :     List[str]=True					,lowercase__   :     str=True					,lowercase__   :     Dict=9_9					,lowercase__   :     Union[str, Any]=3_2					,lowercase__   :     List[str]=5					,lowercase__   :     int=4					,lowercase__   :     Dict=3_7					,lowercase__   :     Union[str, Any]="gelu"					,lowercase__   :     str=0.1					,lowercase__   :     List[str]=0.1					,lowercase__   :     Any=5_1_2					,lowercase__   :     Optional[int]=1_6					,lowercase__   :     Optional[int]=2					,lowercase__   :     Optional[int]=0.0_2					,lowercase__   :     Dict=False					,lowercase__   :     Optional[int]=True					,lowercase__   :     str="None"					,lowercase__   :     Optional[int]=3					,lowercase__   :     List[Any]=4					,lowercase__   :     Union[str, 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							  = relative_attention
   __lowercase							  = position_biased_input
   __lowercase							  = pos_att_type
   __lowercase							  = scope






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

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

   __lowercase							  = None
   if self.use_token_type_ids:
    __lowercase							  = ids_tensor([self.batch_size, self.seq_length]					,self.type_vocab_size			)

   __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, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   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 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   __lowercase							  = self.get_config()
   __lowercase							  = 3_0_0
   return config






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,lowercase__   :     Optional[Any]			):
   self.parent.assertListEqual(list(result.loss.size()			)					,[]			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,lowercase__   :     List[Any]					,lowercase__   :     Dict					,lowercase__   :     List[Any]					,lowercase__   :     Tuple					,lowercase__   :     Dict					,lowercase__   :     str					,lowercase__   :     Union[str, Any]			):
   __lowercase							  = DebertaModel(config=lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__					,attention_mask=lowercase__					,token_type_ids=lowercase__			)[0]
   __lowercase							  = model(lowercase__					,token_type_ids=lowercase__			)[0]
   __lowercase							  = model(lowercase__			)[0]

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






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple					,lowercase__   :     str					,lowercase__   :     Tuple					,lowercase__   :     Optional[Any]					,lowercase__   :     Dict					,lowercase__   :     int					,lowercase__   :     Tuple					,lowercase__   :     int			):
   __lowercase							  = DebertaForMaskedLM(config=lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__					,attention_mask=lowercase__					,token_type_ids=lowercase__					,labels=lowercase__			)

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






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






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






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     Any					,lowercase__   :     Any					,lowercase__   :     Dict					,lowercase__   :     Any					,lowercase__   :     Optional[Any]					,lowercase__   :     int					,lowercase__   :     List[str]			):
   __lowercase							  = DebertaForQuestionAnswering(config=lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(
       lowercase__					,attention_mask=lowercase__					,token_type_ids=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   :     Dict			):
   __lowercase							  = self.prepare_config_and_inputs()
   (
       (
       __lowercase
   )  ,	(
       __lowercase
   )  ,	(
       __lowercase
   )  ,	(
       __lowercase
   )  ,	(
       __lowercase
   )  ,	(
       __lowercase
   )  ,	(
       __lowercase
   )  ,	
   )							  = config_and_inputs
   __lowercase							  = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
   return config, inputs_dict






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




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Tuple							=			(
      (
          DebertaModel,
          DebertaForMaskedLM,
          DebertaForSequenceClassification,
          DebertaForTokenClassification,
          DebertaForQuestionAnswering,
      )
      if is_torch_available()
      else ()
  )
  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			(
      {
          'feature-extraction': DebertaModel,
          'fill-mask': DebertaForMaskedLM,
          'question-answering': DebertaForQuestionAnswering,
          'text-classification': DebertaForSequenceClassification,
          'token-classification': DebertaForTokenClassification,
          'zero-shot': DebertaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )

  SCREAMING_SNAKE_CASE    :  Tuple							=			True
  SCREAMING_SNAKE_CASE    :  List[str]							=			False
  SCREAMING_SNAKE_CASE    :  List[str]							=			False
  SCREAMING_SNAKE_CASE    :  Any							=			False
  SCREAMING_SNAKE_CASE    :  List[Any]							=			False






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   __lowercase							  = DebertaModelTester(self			)
   __lowercase							  = ConfigTester(self					,config_class=lowercase__					,hidden_size=3_7			)






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






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






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






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_deberta_for_masked_lm(*lowercase__			)






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






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_deberta_for_token_classification(*lowercase__			)






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __lowercase							  = DebertaModel.from_pretrained(lowercase__			)
    self.assertIsNotNone(lowercase__			)






@require_torch
@require_sentencepiece
@require_tokenizers
class 			lowercase_	(unittest.TestCase      ):




  """simple docstring"""




  @unittest.skip(reason='''Model not available yet'''			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   pass






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase							  = DebertaModel.from_pretrained('''microsoft/deberta-base'''			)

   __lowercase							  = torch.tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_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]
   # compare the actual values for a slice.
   __lowercase							  = torch.tensor(
       [[[-0.5_9_8_6, -0.8_0_5_5, -0.8_4_6_2], [1.4_4_8_4, -0.9_3_4_8, -0.8_0_5_9], [0.3_1_2_3, 0.0_0_3_2, -1.4_1_3_1]]]			)
   self.assertTrue(torch.allclose(output[:, 1:4, 1:4]					,lowercase__					,atol=1e-4			)					,F"{output[:, 1:4, 1:4]}"			)




'''simple docstring'''






import torch
from torch import nn






class 			lowercase_	(nn.Module      ):




  """simple docstring"""




  def __init__(      self   :     Optional[int]					,lowercase__   :     List[str]					,lowercase__   :     Any					,lowercase__   :     Union[str, Any]					,lowercase__   :     Optional[int]					,lowercase__   :     Optional[int]=1					,lowercase__   :     Optional[Any]=False			):
   super().__init__()

   __lowercase							  = n_token
   __lowercase							  = d_embed
   __lowercase							  = d_proj

   __lowercase							  = cutoffs + [n_token]
   __lowercase							  = [0] + self.cutoffs
   __lowercase							  = div_val

   __lowercase							  = self.cutoffs[0]
   __lowercase							  = len(self.cutoffs			) - 1
   __lowercase							  = self.shortlist_size + self.n_clusters

   if self.n_clusters > 0:
    __lowercase							  = nn.Parameter(torch.zeros(self.n_clusters					,self.d_embed			)			)
    __lowercase							  = nn.Parameter(torch.zeros(self.n_clusters			)			)

   __lowercase							  = nn.ModuleList()
   __lowercase							  = nn.ParameterList()

   if div_val == 1:
    for i in range(len(self.cutoffs			)			):
     if d_proj != d_embed:
      self.out_projs.append(nn.Parameter(torch.FloatTensor(lowercase__					,lowercase__			)			)			)
     else:
      self.out_projs.append(lowercase__			)

    self.out_layers.append(nn.Linear(lowercase__					,lowercase__			)			)
   else:
    for i in range(len(self.cutoffs			)			):
     __lowercase  ,	__lowercase							  = self.cutoff_ends[i], self.cutoff_ends[i + 1]
     __lowercase							  = d_embed // (div_val**i)

     self.out_projs.append(nn.Parameter(torch.FloatTensor(lowercase__					,lowercase__			)			)			)

     self.out_layers.append(nn.Linear(lowercase__					,r_idx - l_idx			)			)

   __lowercase							  = keep_order






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]					,lowercase__   :     List[Any]					,lowercase__   :     Dict					,lowercase__   :     List[Any]					,lowercase__   :     Any			):
   if proj is None:
    __lowercase							  = nn.functional.linear(lowercase__					,lowercase__					,bias=lowercase__			)
   else:
    # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
    __lowercase							  = nn.functional.linear(lowercase__					,proj.t().contiguous()			)
    __lowercase							  = nn.functional.linear(lowercase__					,lowercase__					,bias=lowercase__			)
    # else:
    #     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
    #     if bias is not None:
    #         logit = logit + bias

   return logit






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,lowercase__   :     Optional[Any]					,lowercase__   :     Any=None					,lowercase__   :     List[str]=False			):

   if labels is not None:
    # Shift so that tokens < n predict n
    __lowercase							  = hidden[..., :-1, :].contiguous()
    __lowercase							  = labels[..., 1:].contiguous()
    __lowercase							  = hidden.view(-1					,hidden.size(-1			)			)
    __lowercase							  = labels.view(-1			)
    if hidden.size(0			) != labels.size(0			):
     raise RuntimeError('''Input and labels should have the same size in the batch dimension.'''			)
   else:
    __lowercase							  = hidden.view(-1					,hidden.size(-1			)			)

   if self.n_clusters == 0:
    __lowercase							  = self._compute_logit(lowercase__					,self.out_layers[0].weight					,self.out_layers[0].bias					,self.out_projs[0]			)
    if labels is not None:
     __lowercase							  = labels != -1_0_0
     __lowercase							  = torch.zeros_like(lowercase__					,dtype=hidden.dtype					,device=hidden.device			)
     __lowercase							  = (
         -nn.functional.log_softmax(lowercase__					,dim=-1			)[mask].gather(1					,labels[mask].unsqueeze(1			)			).squeeze(1			)
     )
    else:
     __lowercase							  = nn.functional.log_softmax(lowercase__					,dim=-1			)
   else:
    # construct weights and biases
    __lowercase  ,	__lowercase							  = [], []
    for i in range(len(self.cutoffs			)			):
     if self.div_val == 1:
      __lowercase  ,	__lowercase							  = self.cutoff_ends[i], self.cutoff_ends[i + 1]
      __lowercase							  = self.out_layers[0].weight[l_idx:r_idx]
      __lowercase							  = self.out_layers[0].bias[l_idx:r_idx]
     else:
      __lowercase							  = self.out_layers[i].weight
      __lowercase							  = self.out_layers[i].bias

     if i == 0:
      __lowercase							  = torch.cat([weight_i, self.cluster_weight]					,dim=0			)
      __lowercase							  = torch.cat([bias_i, self.cluster_bias]					,dim=0			)

     weights.append(lowercase__			)
     biases.append(lowercase__			)

    __lowercase  ,	__lowercase  ,	__lowercase							  = weights[0], biases[0], self.out_projs[0]

    __lowercase							  = self._compute_logit(lowercase__					,lowercase__					,lowercase__					,lowercase__			)
    __lowercase							  = nn.functional.log_softmax(lowercase__					,dim=1			)

    if labels is None:
     __lowercase							  = hidden.new_empty((head_logit.size(0			), self.n_token)			)
    else:
     __lowercase							  = torch.zeros_like(lowercase__					,dtype=hidden.dtype					,device=hidden.device			)

    __lowercase							  = 0
    __lowercase							  = [0] + self.cutoffs
    for i in range(len(lowercase__			) - 1			):
     __lowercase  ,	__lowercase							  = cutoff_values[i], cutoff_values[i + 1]

     if labels is not None:
      __lowercase							  = (labels >= l_idx) & (labels < r_idx)
      __lowercase							  = mask_i.nonzero().squeeze()

      if indices_i.numel() == 0:
       continue

      __lowercase							  = labels.index_select(0					,lowercase__			) - l_idx
      __lowercase							  = head_logprob.index_select(0					,lowercase__			)
      __lowercase							  = hidden.index_select(0					,lowercase__			)
     else:
      __lowercase							  = hidden

     if i == 0:
      if labels is not None:
       __lowercase							  = head_logprob_i.gather(1					,target_i[:, None]			).squeeze(1			)
      else:
       __lowercase							  = head_logprob[:, : self.cutoffs[0]]
     else:
      __lowercase  ,	__lowercase  ,	__lowercase							  = weights[i], biases[i], self.out_projs[i]

      __lowercase							  = self._compute_logit(lowercase__					,lowercase__					,lowercase__					,lowercase__			)
      __lowercase							  = nn.functional.log_softmax(lowercase__					,dim=1			)
      __lowercase							  = self.cutoffs[0] + i - 1  # No probability for the head cluster
      if labels is not None:
       __lowercase							  = head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
           1					,target_i[:, None]			).squeeze(1			)
      else:
       __lowercase							  = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
       __lowercase							  = logprob_i

     if labels is not None:
      if (hasattr(self					,'''keep_order'''			) and self.keep_order) or keep_order:
       out.index_copy_(0					,lowercase__					,-logprob_i			)
      else:
       out[offset : offset + logprob_i.size(0			)].copy_(-logprob_i			)
      offset += logprob_i.size(0			)

   return out






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     Union[str, Any]			):
   if self.n_clusters == 0:
    __lowercase							  = self._compute_logit(lowercase__					,self.out_layers[0].weight					,self.out_layers[0].bias					,self.out_projs[0]			)
    return nn.functional.log_softmax(lowercase__					,dim=-1			)
   else:
    # construct weights and biases
    __lowercase  ,	__lowercase							  = [], []
    for i in range(len(self.cutoffs			)			):
     if self.div_val == 1:
      __lowercase  ,	__lowercase							  = self.cutoff_ends[i], self.cutoff_ends[i + 1]
      __lowercase							  = self.out_layers[0].weight[l_idx:r_idx]
      __lowercase							  = self.out_layers[0].bias[l_idx:r_idx]
     else:
      __lowercase							  = self.out_layers[i].weight
      __lowercase							  = self.out_layers[i].bias

     if i == 0:
      __lowercase							  = torch.cat([weight_i, self.cluster_weight]					,dim=0			)
      __lowercase							  = torch.cat([bias_i, self.cluster_bias]					,dim=0			)

     weights.append(lowercase__			)
     biases.append(lowercase__			)

    __lowercase  ,	__lowercase  ,	__lowercase							  = weights[0], biases[0], self.out_projs[0]
    __lowercase							  = self._compute_logit(lowercase__					,lowercase__					,lowercase__					,lowercase__			)

    __lowercase							  = hidden.new_empty((head_logit.size(0			), self.n_token)			)
    __lowercase							  = nn.functional.log_softmax(lowercase__					,dim=1			)

    __lowercase							  = [0] + self.cutoffs
    for i in range(len(lowercase__			) - 1			):
     __lowercase  ,	__lowercase							  = cutoff_values[i], cutoff_values[i + 1]

     if i == 0:
      __lowercase							  = head_logprob[:, : self.cutoffs[0]]
     else:
      __lowercase  ,	__lowercase  ,	__lowercase							  = weights[i], biases[i], self.out_projs[i]

      __lowercase							  = self._compute_logit(lowercase__					,lowercase__					,lowercase__					,lowercase__			)
      __lowercase							  = nn.functional.log_softmax(lowercase__					,dim=1			)

      __lowercase							  = head_logprob[:, -i] + tail_logprob_i
      __lowercase							  = logprob_i

    return out

