


'''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__  (					lowercase_		):






  def __init__(					self      :					List[Any]		,						_lowercase      :					Optional[int]		,						_lowercase      :					int=13		,						_lowercase      :					Dict=7		,						_lowercase      :					Tuple=True		,						_lowercase      :					Dict=True		,						_lowercase      :					Optional[int]=True		,						_lowercase      :					Dict=True		,						_lowercase      :					Any=99		,						_lowercase      :					Optional[Any]=32		,						_lowercase      :					Tuple=5		,						_lowercase      :					Any=4		,						_lowercase      :					Tuple=37		,						_lowercase      :					Optional[Any]="gelu"		,						_lowercase      :					List[str]=0.1		,						_lowercase      :					List[str]=0.1		,						_lowercase      :					Dict=5_12		,						_lowercase      :					List[str]=16		,						_lowercase      :					Any=2		,						_lowercase      :					str=0.0_2		,						_lowercase      :					Any=False		,						_lowercase      :					Tuple=True		,						_lowercase      :					str="None"		,						_lowercase      :					Optional[Any]=3		,						_lowercase      :					int=4		,						_lowercase      :					List[str]=None		,						):




         """simple docstring"""







         UpperCAmelCase__										=     parent
         UpperCAmelCase__										=     batch_size
         UpperCAmelCase__										=     seq_length
         UpperCAmelCase__										=     is_training
         UpperCAmelCase__										=     use_input_mask
         UpperCAmelCase__										=     use_token_type_ids
         UpperCAmelCase__										=     use_labels
         UpperCAmelCase__										=     vocab_size
         UpperCAmelCase__										=     hidden_size
         UpperCAmelCase__										=     num_hidden_layers
         UpperCAmelCase__										=     num_attention_heads
         UpperCAmelCase__										=     intermediate_size
         UpperCAmelCase__										=     hidden_act
         UpperCAmelCase__										=     hidden_dropout_prob
         UpperCAmelCase__										=     attention_probs_dropout_prob
         UpperCAmelCase__										=     max_position_embeddings
         UpperCAmelCase__										=     type_vocab_size
         UpperCAmelCase__										=     type_sequence_label_size
         UpperCAmelCase__										=     initializer_range
         UpperCAmelCase__										=     num_labels
         UpperCAmelCase__										=     num_choices
         UpperCAmelCase__										=     relative_attention
         UpperCAmelCase__										=     position_biased_input
         UpperCAmelCase__										=     pos_att_type
         UpperCAmelCase__										=     scope






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         UpperCAmelCase__										=     ids_tensor([self.batch_size, self.seq_length]		,						self.vocab_size )

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

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

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

         UpperCAmelCase__										=     self.get_config()

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






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         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 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.get_config()
         UpperCAmelCase__										=     3_00
         return config






  def 			_UpperCAmelCase  (					self      :					Dict		,						_lowercase      :					Tuple ):




         """simple docstring"""







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






  def 			_UpperCAmelCase  (					self      :					Tuple		,						_lowercase      :					Any		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					Optional[int]		,						_lowercase      :					Optional[int]		,						_lowercase      :					List[Any]		,						_lowercase      :					Tuple		,						_lowercase      :					Dict ):




         """simple docstring"""







         UpperCAmelCase__										=     DebertaModel(config=lowerCamelCase_ )
         model.to(lowerCamelCase_ )
         model.eval()
         UpperCAmelCase__										=     model(lowerCamelCase_		,						attention_mask=lowerCamelCase_		,						token_type_ids=lowerCamelCase_ )[0]
         UpperCAmelCase__										=     model(lowerCamelCase_		,						token_type_ids=lowerCamelCase_ )[0]
         UpperCAmelCase__										=     model(lowerCamelCase_ )[0]

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






  def 			_UpperCAmelCase  (					self      :					str		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					Optional[int]		,						_lowercase      :					List[str]		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					Optional[Any]		,						_lowercase      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     DebertaForMaskedLM(config=lowerCamelCase_ )
         model.to(lowerCamelCase_ )
         model.eval()
         UpperCAmelCase__										=     model(lowerCamelCase_		,						attention_mask=lowerCamelCase_		,						token_type_ids=lowerCamelCase_		,						labels=lowerCamelCase_ )

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






  def 			_UpperCAmelCase  (					self      :					List[Any]		,						_lowercase      :					Any		,						_lowercase      :					Any		,						_lowercase      :					Optional[Any]		,						_lowercase      :					int		,						_lowercase      :					Optional[int]		,						_lowercase      :					Tuple		,						_lowercase      :					List[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.num_labels
         UpperCAmelCase__										=     DebertaForSequenceClassification(lowerCamelCase_ )
         model.to(lowerCamelCase_ )
         model.eval()
         UpperCAmelCase__										=     model(lowerCamelCase_		,						attention_mask=lowerCamelCase_		,						token_type_ids=lowerCamelCase_		,						labels=lowerCamelCase_ )
         self.parent.assertListEqual(list(result.logits.size() )		,						[self.batch_size, self.num_labels] )
         self.check_loss_output(lowerCamelCase_ )






  def 			_UpperCAmelCase  (					self      :					str		,						_lowercase      :					str		,						_lowercase      :					List[Any]		,						_lowercase      :					Dict		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					Any		,						_lowercase      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.num_labels
         UpperCAmelCase__										=     DebertaForTokenClassification(config=lowerCamelCase_ )
         model.to(lowerCamelCase_ )
         model.eval()
         UpperCAmelCase__										=     model(lowerCamelCase_		,						attention_mask=lowerCamelCase_		,						token_type_ids=lowerCamelCase_		,						labels=lowerCamelCase_ )
         self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.seq_length, self.num_labels) )






  def 			_UpperCAmelCase  (					self      :					str		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					str		,						_lowercase      :					Union[str, Any]		,						_lowercase      :					int		,						_lowercase      :					int		,						_lowercase      :					Optional[int]		,						_lowercase      :					Union[str, Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     DebertaForQuestionAnswering(config=lowerCamelCase_ )
         model.to(lowerCamelCase_ )
         model.eval()
         UpperCAmelCase__										=     model(
             lowerCamelCase_		,						attention_mask=lowerCamelCase_		,						token_type_ids=lowerCamelCase_		,						start_positions=lowerCamelCase_		,						end_positions=lowerCamelCase_		,						)
         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 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.prepare_config_and_inputs()
         (
             UpperCAmelCase__       
         )										=     config_and_inputs
         UpperCAmelCase__										=     {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
         return config, inputs_dict




@require_torch
class     lowercase__  (					lowercase_					,							lowercase_					,							unittest.TestCase		):
  A__=  (
      (
          DebertaModel,
          DebertaForMaskedLM,
          DebertaForSequenceClassification,
          DebertaForTokenClassification,
          DebertaForQuestionAnswering,
      )
      if is_torch_available()
      else ()
  )
  A__=  (
      {
          'feature-extraction': DebertaModel,
          'fill-mask': DebertaForMaskedLM,
          'question-answering': DebertaForQuestionAnswering,
          'text-classification': DebertaForSequenceClassification,
          'token-classification': DebertaForTokenClassification,
          'zero-shot': DebertaForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )

  A__=  True
  A__=  False
  A__=  False
  A__=  False
  A__=  False






  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         UpperCAmelCase__										=     DebertaModelTester(self )
         UpperCAmelCase__										=     ConfigTester(self		,						config_class=lowerCamelCase_		,						hidden_size=37 )






  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         self.config_tester.run_common_tests()






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_model(*lowerCamelCase_ )






  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCamelCase_ )






  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCamelCase_ )






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_question_answering(*lowerCamelCase_ )






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_token_classification(*lowerCamelCase_ )






  @slow
  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase__										=     DebertaModel.from_pretrained(lowerCamelCase_ )
                self.assertIsNotNone(lowerCamelCase_ )




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






  @unittest.skip(reason="Model not available yet" )
  def 			_UpperCAmelCase  (					self      :					List[Any] ):




         """simple docstring"""







         pass






  @slow
  def 			_UpperCAmelCase  (					self      :					Any ):




         """simple docstring"""







         UpperCAmelCase__										=     DebertaModel.from_pretrained("microsoft/deberta-base" )

         UpperCAmelCase__										=     torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] )
         UpperCAmelCase__										=     torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
         with torch.no_grad():
                UpperCAmelCase__										=     model(lowerCamelCase_		,						attention_mask=lowerCamelCase_ )[0]
         # compare the actual values for a slice.
         UpperCAmelCase__										=     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]		,						lowerCamelCase_		,						atol=1E-4 )		,						F"""{output[:, 1:4, 1:4]}""" )









import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
    import onnxruntime as ort


@nightly
@require_onnxruntime
@require_torch_gpu
class     lowercase__  (					unittest.TestCase		):






  @property
  def 			_UpperCAmelCase  (					self      :					Union[str, Any] ):




         """simple docstring"""







         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )






  @property
  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__										=     ort.SessionOptions()
         UpperCAmelCase__										=     False
         return options






  def 			_UpperCAmelCase  (					self      :					Dict ):




         """simple docstring"""







         UpperCAmelCase__										=     load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/in_paint/overture-creations-5sI6fQgYIuo.png" )
         UpperCAmelCase__										=     load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
         UpperCAmelCase__										=     load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy" )

         # using the PNDM scheduler by default
         UpperCAmelCase__										=     OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
             "CompVis/stable-diffusion-v1-4"		,						revision="onnx"		,						safety_checker=_lowercase		,						feature_extractor=_lowercase		,						provider=self.gpu_provider		,						sess_options=self.gpu_options		,						)
         pipe.set_progress_bar_config(disable=_lowercase )

         UpperCAmelCase__										=     "A red cat sitting on a park bench"

         UpperCAmelCase__										=     np.random.RandomState(0 )
         UpperCAmelCase__										=     pipe(
             prompt=_lowercase		,						image=_lowercase		,						mask_image=_lowercase		,						strength=0.7_5		,						guidance_scale=7.5		,						num_inference_steps=15		,						generator=_lowercase		,						output_type="np"		,						)

         UpperCAmelCase__										=     output.images[0]

         assert image.shape == (5_12, 5_12, 3)
         assert np.abs(expected_image - image ).max() < 1E-2





