

"""simple docstring"""






import unittest

from transformers import DebertaVaConfig, 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 (
     DebertaVaForMaskedLM,
     DebertaVaForMultipleChoice,
     DebertaVaForQuestionAnswering,
     DebertaVaForSequenceClassification,
     DebertaVaForTokenClassification,
     DebertaVaModel,
 )
 from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST







class 	__UpperCamelCase    (    a__						):

  def __init__(		self    ,_A    ,_A=13    ,_A=7    ,_A=True    ,_A=True    ,_A=True    ,_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=False    ,_A=True    ,_A="None"    ,_A=3    ,_A=4    ,_A=None    ,):
      '''simple docstring'''

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

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

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

      _lowerCAmelCase       :   Optional[Any]  =    None
      if self.use_input_mask:
          _lowerCAmelCase       :   Tuple  =    ids_tensor([self.batch_size, self.seq_length]    ,vocab_size=2      )

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

      _lowerCAmelCase       :   Optional[Any]  =    None
      _lowerCAmelCase       :   Tuple  =    None
      _lowerCAmelCase       :   Tuple  =    None
      if self.use_labels:
          _lowerCAmelCase       :   Optional[int]  =    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       :   Union[str, Any]  =    ids_tensor([self.batch_size]    ,self.num_choices      )

      _lowerCAmelCase       :   Tuple  =    self.get_config()

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

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

      return DebertaVaConfig(
          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    ,_A      ):
      '''simple docstring'''

      self.parent.assertListEqual(list(result.loss.size()      )    ,[]      )

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

      _lowerCAmelCase       :   Any  =    DebertaVaModel(config=_A      )
      model.to(_A      )
      model.eval()
      _lowerCAmelCase       :   Optional[Any]  =    model(_A    ,attention_mask=_A    ,token_type_ids=_A      )[0]
      _lowerCAmelCase       :   Tuple  =    model(_A    ,token_type_ids=_A      )[0]
      _lowerCAmelCase       :   Union[str, Any]  =    model(_A      )[0]

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

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

      _lowerCAmelCase       :   str  =    DebertaVaForMaskedLM(config=_A      )
      model.to(_A      )
      model.eval()
      _lowerCAmelCase       :   Union[str, Any]  =    model(_A    ,attention_mask=_A    ,token_type_ids=_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    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Any  =    self.num_labels
      _lowerCAmelCase       :   List[Any]  =    DebertaVaForSequenceClassification(_A      )
      model.to(_A      )
      model.eval()
      _lowerCAmelCase       :   List[Any]  =    model(_A    ,attention_mask=_A    ,token_type_ids=_A    ,labels=_A      )
      self.parent.assertListEqual(list(result.logits.size()      )    ,[self.batch_size, self.num_labels]      )
      self.check_loss_output(_A      )

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

      _lowerCAmelCase       :   str  =    self.num_labels
      _lowerCAmelCase       :   Dict  =    DebertaVaForTokenClassification(config=_A      )
      model.to(_A      )
      model.eval()
      _lowerCAmelCase       :   Union[str, Any]  =    model(_A    ,attention_mask=_A    ,token_type_ids=_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    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    DebertaVaForQuestionAnswering(config=_A      )
      model.to(_A      )
      model.eval()
      _lowerCAmelCase       :   str  =    model(
          _A    ,attention_mask=_A    ,token_type_ids=_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    ,_A      ):
      '''simple docstring'''

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







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

      _lowerCAmelCase       :   int  =    self.prepare_config_and_inputs()
      (
          _lowerCAmelCase					
      )       :   Tuple  =    config_and_inputs
      _lowerCAmelCase       :   List[str]  =    {'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						):
  _UpperCAmelCase          =		(
      (
          DebertaVaModel,
          DebertaVaForMaskedLM,
          DebertaVaForSequenceClassification,
          DebertaVaForTokenClassification,
          DebertaVaForQuestionAnswering,
          DebertaVaForMultipleChoice,
      )
      if is_torch_available()
      else ()
  )
  _UpperCAmelCase          =		(
      {
          "feature-extraction": DebertaVaModel,
          "fill-mask": DebertaVaForMaskedLM,
          "question-answering": DebertaVaForQuestionAnswering,
          "text-classification": DebertaVaForSequenceClassification,
          "token-classification": DebertaVaForTokenClassification,
          "zero-shot": DebertaVaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )

  _UpperCAmelCase          =		True
  _UpperCAmelCase          =		False
  _UpperCAmelCase          =		False
  _UpperCAmelCase          =		False
  _UpperCAmelCase          =		False

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

      _lowerCAmelCase       :   int  =    DebertaVaModelTester(self      )
      _lowerCAmelCase       :   List[str]  =    ConfigTester(self    ,config_class=_A    ,hidden_size=37      )

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

      self.config_tester.run_common_tests()

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

      _lowerCAmelCase       :   int  =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_model(*_A      )

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

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

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

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

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

      _lowerCAmelCase       :   int  =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_question_answering(*_A      )

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

      _lowerCAmelCase       :   List[Any]  =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_token_classification(*_A      )

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

      _lowerCAmelCase       :   Union[str, Any]  =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_multiple_choice(*_A      )







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

      for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _lowerCAmelCase       :   Union[str, Any]  =    DebertaVaModel.from_pretrained(_A      )
          self.assertIsNotNone(_A      )







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

  @unittest.skip(reason='Model not available yet'      )
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      pass







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

      _lowerCAmelCase       :   List[Any]  =    DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge'      )

      _lowerCAmelCase       :   Any  =    torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]      )
      _lowerCAmelCase       :   Optional[int]  =    torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
      with torch.no_grad():
          _lowerCAmelCase       :   Tuple  =    model(_A    ,attention_mask=_A      )[0]
      # compare the actual values for a slice.
      _lowerCAmelCase       :   Optional[Any]  =    torch.tensor(
          [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]]      )
      self.assertTrue(torch.allclose(output[:, 1:4, 1:4]    ,_A    ,atol=1E-4      )    ,F"""{output[:, 1:4, 1:4]}"""      )




"""simple docstring"""






import gc
import unittest

import numpy as np
import torch

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 	__UpperCamelCase    (    a__							,							unittest.TestCase						):
  _UpperCAmelCase          =		DanceDiffusionPipeline
  _UpperCAmelCase          =		UNCONDITIONAL_AUDIO_GENERATION_PARAMS
  _UpperCAmelCase          =		PipelineTesterMixin.required_optional_params - {
      "callback",
      "latents",
      "callback_steps",
      "output_type",
      "num_images_per_prompt",
  }
  _UpperCAmelCase          =		UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
  _UpperCAmelCase          =		False
  _UpperCAmelCase          =		False

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

      torch.manual_seed(0      )
      _lowerCAmelCase       :   List[Any]  =    UNetaDModel(
          block_out_channels=(32, 32, 64)    ,extra_in_channels=16    ,sample_size=512    ,sample_rate=1_6000    ,in_channels=2    ,out_channels=2    ,flip_sin_to_cos=_A    ,use_timestep_embedding=_A    ,time_embedding_type='fourier'    ,mid_block_type='UNetMidBlock1D'    ,down_block_types=('DownBlock1DNoSkip', 'DownBlock1D', 'AttnDownBlock1D')    ,up_block_types=('AttnUpBlock1D', 'UpBlock1D', 'UpBlock1DNoSkip')    ,)
      _lowerCAmelCase       :   int  =    IPNDMScheduler()

      _lowerCAmelCase       :   Union[str, Any]  =    {
          'unet': unet,
          'scheduler': scheduler,
      }
      return components

  def __lowerCamelCase		(		self    ,_A    ,_A=0      ):
      '''simple docstring'''

      if str(_A      ).startswith('mps'      ):
          _lowerCAmelCase       :   str  =    torch.manual_seed(_A      )
      else:
          _lowerCAmelCase       :   Optional[Any]  =    torch.Generator(device=_A      ).manual_seed(_A      )
      _lowerCAmelCase       :   int  =    {
          'batch_size': 1,
          'generator': generator,
          'num_inference_steps': 4,
      }
      return inputs

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

      _lowerCAmelCase       :   List[str]  =    'cpu'  # ensure determinism for the device-dependent torch.Generator
      _lowerCAmelCase       :   int  =    self.get_dummy_components()
      _lowerCAmelCase       :   Optional[Any]  =    DanceDiffusionPipeline(**_A      )
      _lowerCAmelCase       :   int  =    pipe.to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.get_dummy_inputs(_A      )
      _lowerCAmelCase       :   List[str]  =    pipe(**_A      )
      _lowerCAmelCase       :   List[Any]  =    output.audios

      _lowerCAmelCase       :   List[str]  =    audio[0, -3:, -3:]

      assert audio.shape == (1, 2, components["unet"].sample_size)
      _lowerCAmelCase       :   Optional[Any]  =    np.array([-0.7_2_6_5, 1.0_0_0_0, -0.8_3_8_8, 0.1_1_7_5, 0.9_4_9_8, -1.0_0_0_0]      )
      assert np.abs(audio_slice.flatten() - expected_slice      ).max() < 1E-2

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

      return super().test_save_load_local()

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

      return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3      )

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

      return super().test_save_load_optional_components()

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

      return super().test_attention_slicing_forward_pass()







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

      super().test_inference_batch_single_identical(expected_max_diff=3E-3      )







@slow
@require_torch_gpu
class 	__UpperCamelCase    (    unittest.TestCase						):

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

      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()

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

      _lowerCAmelCase       :   List[Any]  =    torch_device

      _lowerCAmelCase       :   int  =    DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k'      )
      _lowerCAmelCase       :   int  =    pipe.to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   Optional[int]  =    torch.manual_seed(0      )
      _lowerCAmelCase       :   str  =    pipe(generator=_A    ,num_inference_steps=100    ,audio_length_in_s=4.0_9_6      )
      _lowerCAmelCase       :   str  =    output.audios

      _lowerCAmelCase       :   List[str]  =    audio[0, -3:, -3:]

      assert audio.shape == (1, 2, pipe.unet.sample_size)
      _lowerCAmelCase       :   Union[str, Any]  =    np.array([-0.0_1_9_2, -0.0_2_3_1, -0.0_3_1_8, -0.0_0_5_9, 0.0_0_0_2, -0.0_0_2_0]      )

      assert np.abs(audio_slice.flatten() - expected_slice      ).max() < 1E-2







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

      _lowerCAmelCase       :   str  =    torch_device

      _lowerCAmelCase       :   Tuple  =    DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k'    ,torch_dtype=torch.floataa      )
      _lowerCAmelCase       :   Optional[int]  =    pipe.to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   Union[str, Any]  =    torch.manual_seed(0      )
      _lowerCAmelCase       :   Optional[int]  =    pipe(generator=_A    ,num_inference_steps=100    ,audio_length_in_s=4.0_9_6      )
      _lowerCAmelCase       :   Union[str, Any]  =    output.audios

      _lowerCAmelCase       :   int  =    audio[0, -3:, -3:]

      assert audio.shape == (1, 2, pipe.unet.sample_size)
      _lowerCAmelCase       :   List[str]  =    np.array([-0.0_3_6_7, -0.0_4_8_8, -0.0_7_7_1, -0.0_5_2_5, -0.0_4_4_4, -0.0_3_4_1]      )

      assert np.abs(audio_slice.flatten() - expected_slice      ).max() < 1E-2





