


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       lowerCamelCase__   (       lowerCAmelCase):





     def __init__(self		,     UpperCAmelCase		,     UpperCAmelCase=1_3		,     UpperCAmelCase=7		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=True		,     UpperCAmelCase=9_9		,     UpperCAmelCase=3_2		,     UpperCAmelCase=5		,     UpperCAmelCase=4		,     UpperCAmelCase=3_7		,     UpperCAmelCase="gelu"		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=5_1_2		,     UpperCAmelCase=1_6		,     UpperCAmelCase=2		,     UpperCAmelCase=0.02		,     UpperCAmelCase=False		,     UpperCAmelCase=True		,     UpperCAmelCase="None"		,     UpperCAmelCase=3		,     UpperCAmelCase=4		,     UpperCAmelCase=None		,     )     ->   Any:
      _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      __A					(self       )     ->   Any:
      _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      __A					(self       )     ->   Optional[int]:
      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      __A					(self       )     ->   List[Any]:
      _lowercase							      =self.get_config()
      _lowercase							      =3_0_0
      return config





     def      __A					(self		,     UpperCAmelCase       )     ->   Union[str, Any]:
      self.parent.assertListEqual(list(result.loss.size()       )		,     []       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   List[str]:
      _lowercase							      =DebertaModel(config=UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =model(UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     token_type_ids=UpperCAmelCase       )[0]
      _lowercase							      =model(UpperCAmelCase		,     token_type_ids=UpperCAmelCase       )[0]
      _lowercase							      =model(UpperCAmelCase       )[0]

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





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Dict:
      _lowercase							      =DebertaForMaskedLM(config=UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =model(UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     token_type_ids=UpperCAmelCase		,     labels=UpperCAmelCase       )

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





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   List[str]:
      _lowercase							      =self.num_labels
      _lowercase							      =DebertaForSequenceClassification(UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =model(UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     token_type_ids=UpperCAmelCase		,     labels=UpperCAmelCase       )
      self.parent.assertListEqual(list(result.logits.size()       )		,     [self.batch_size, self.num_labels]       )
      self.check_loss_output(UpperCAmelCase       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Optional[Any]:
      _lowercase							      =self.num_labels
      _lowercase							      =DebertaForTokenClassification(config=UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =model(UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     token_type_ids=UpperCAmelCase		,     labels=UpperCAmelCase       )
      self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.seq_length, self.num_labels)       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase       )     ->   Any:
      _lowercase							      =DebertaForQuestionAnswering(config=UpperCAmelCase       )
      model.to(UpperCAmelCase       )
      model.eval()
      _lowercase							      =model(
          UpperCAmelCase		,     attention_mask=UpperCAmelCase		,     token_type_ids=UpperCAmelCase		,     start_positions=UpperCAmelCase		,     end_positions=UpperCAmelCase		,     )
      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      __A					(self       )     ->   Any:
      _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       lowerCamelCase__   (       lowerCAmelCase   ,							lowerCAmelCase   ,							unittest.TestCase):
     SCREAMING_SNAKE_CASE__      			=			(
         (
             DebertaModel,
             DebertaForMaskedLM,
             DebertaForSequenceClassification,
             DebertaForTokenClassification,
             DebertaForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     SCREAMING_SNAKE_CASE__      			=			(
         {
             '''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__      			=			True
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False





     def      __A					(self       )     ->   List[str]:
      _lowercase							      =DebertaModelTester(self       )
      _lowercase							      =ConfigTester(self		,     config_class=UpperCAmelCase		,     hidden_size=3_7       )





     def      __A					(self       )     ->   Dict:
      self.config_tester.run_common_tests()





     def      __A					(self       )     ->   Optional[int]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_model(*UpperCAmelCase       )





     def      __A					(self       )     ->   Tuple:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_sequence_classification(*UpperCAmelCase       )





     def      __A					(self       )     ->   List[str]:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_masked_lm(*UpperCAmelCase       )





     def      __A					(self       )     ->   int:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_question_answering(*UpperCAmelCase       )





     def      __A					(self       )     ->   str:
      _lowercase							      =self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_token_classification(*UpperCAmelCase       )





     @slow
     def      __A					(self       )     ->   Optional[int]:
      for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       _lowercase							      =DebertaModel.from_pretrained(UpperCAmelCase       )
       self.assertIsNotNone(UpperCAmelCase       )







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





     @unittest.skip(reason='''Model not available yet'''       )
     def      __A					(self       )     ->   Optional[Any]:
      pass





     @slow
     def      __A					(self       )     ->   Any:
      _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(UpperCAmelCase		,     attention_mask=UpperCAmelCase       )[0]
      # compare the actual values for a slice.
      _lowercase							      =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]		,     UpperCAmelCase		,     atol=1e-4       )		,     f"{output[:, 1:4, 1:4]}"       )





from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput







class       lowerCamelCase__   (       nn.Module):





     def __init__(self		,     UpperCAmelCase = 1_6		,     UpperCAmelCase = 8_8		,     UpperCAmelCase = None		,     UpperCAmelCase = 1		,     UpperCAmelCase = 0.0		,     UpperCAmelCase = 3_2		,     UpperCAmelCase = None		,     UpperCAmelCase = False		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = "geglu"		,     UpperCAmelCase = None		,     )     ->   Any:
      super().__init__()
      _lowercase							      =nn.ModuleList(
          [
              TransformeraDModel(
                  num_attention_heads=UpperCAmelCase		,     attention_head_dim=UpperCAmelCase		,     in_channels=UpperCAmelCase		,     num_layers=UpperCAmelCase		,     dropout=UpperCAmelCase		,     norm_num_groups=UpperCAmelCase		,     cross_attention_dim=UpperCAmelCase		,     attention_bias=UpperCAmelCase		,     sample_size=UpperCAmelCase		,     num_vector_embeds=UpperCAmelCase		,     activation_fn=UpperCAmelCase		,     num_embeds_ada_norm=UpperCAmelCase		,     )
              for _ in range(2       )
          ]       )

      # Variables that can be set by a pipeline:

      # The ratio of transformer1 to transformer2's output states to be combined during inference
      _lowercase							      =0.5

      # The shape of `encoder_hidden_states` is expected to be
      # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
      _lowercase							      =[7_7, 2_5_7]

      # Which transformer to use to encode which condition.
      # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
      _lowercase							      =[1, 0]





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase = True		,     )     ->   str:
      _lowercase							      =hidden_states

      _lowercase							      =[]
      _lowercase							      =0
      # attention_mask is not used yet
      for i in range(2       ):
       # for each of the two transformers, pass the corresponding condition tokens
       _lowercase							      =encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
       _lowercase							      =self.transformer_index_for_condition[i]
       _lowercase							      =self.transformers[transformer_index](
           UpperCAmelCase		,     encoder_hidden_states=UpperCAmelCase		,     timestep=UpperCAmelCase		,     cross_attention_kwargs=UpperCAmelCase		,     return_dict=UpperCAmelCase		,     )[0]
       encoded_states.append(encoded_state - input_states       )
       tokens_start += self.condition_lengths[i]

      _lowercase							      =encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
      _lowercase							      =output_states + input_states

      if not return_dict:
       return (output_states,)

      return TransformeraDModelOutput(sample=UpperCAmelCase       )

