





from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import (
         TFDebertaVaForMaskedLM,
         TFDebertaVaForQuestionAnswering,
         TFDebertaVaForSequenceClassification,
         TFDebertaVaForTokenClassification,
         TFDebertaVaModel,
     )



class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		Any ,  lowerCAmelCase_			:		Dict ,  lowerCAmelCase_			:		Tuple=1_3 ,  lowerCAmelCase_			:		Tuple=7 ,  lowerCAmelCase_			:		str=True ,  lowerCAmelCase_			:		List[str]=True ,  lowerCAmelCase_			:		int=True ,  lowerCAmelCase_			:		Union[str, Any]=True ,  lowerCAmelCase_			:		Optional[Any]=9_9 ,  lowerCAmelCase_			:		Dict=3_2 ,  lowerCAmelCase_			:		int=2 ,  lowerCAmelCase_			:		List[str]=4 ,  lowerCAmelCase_			:		Union[str, Any]=3_7 ,  lowerCAmelCase_			:		List[str]="gelu" ,  lowerCAmelCase_			:		Optional[int]=0.1 ,  lowerCAmelCase_			:		List[str]=0.1 ,  lowerCAmelCase_			:		Tuple=5_1_2 ,  lowerCAmelCase_			:		Union[str, Any]=1_6 ,  lowerCAmelCase_			:		str=2 ,  lowerCAmelCase_			:		int=0.02 ,  lowerCAmelCase_			:		Any=False ,  lowerCAmelCase_			:		List[str]=True ,  lowerCAmelCase_			:		str="None" ,  lowerCAmelCase_			:		str=3 ,  lowerCAmelCase_			:		Any=4 ,  lowerCAmelCase_			:		str=None ,  )							->						Optional[Any]:
     __lowerCAmelCase							     =     parent
     __lowerCAmelCase							     =     batch_size
     __lowerCAmelCase							     =     seq_length
     __lowerCAmelCase							     =     is_training
     __lowerCAmelCase							     =     use_input_mask
     __lowerCAmelCase							     =     use_token_type_ids
     __lowerCAmelCase							     =     use_labels
     __lowerCAmelCase							     =     vocab_size
     __lowerCAmelCase							     =     hidden_size
     __lowerCAmelCase							     =     num_hidden_layers
     __lowerCAmelCase							     =     num_attention_heads
     __lowerCAmelCase							     =     intermediate_size
     __lowerCAmelCase							     =     hidden_act
     __lowerCAmelCase							     =     hidden_dropout_prob
     __lowerCAmelCase							     =     attention_probs_dropout_prob
     __lowerCAmelCase							     =     max_position_embeddings
     __lowerCAmelCase							     =     type_vocab_size
     __lowerCAmelCase							     =     type_sequence_label_size
     __lowerCAmelCase							     =     initializer_range
     __lowerCAmelCase							     =     num_labels
     __lowerCAmelCase							     =     num_choices
     __lowerCAmelCase							     =     relative_attention
     __lowerCAmelCase							     =     position_biased_input
     __lowerCAmelCase							     =     pos_att_type
     __lowerCAmelCase							     =     scope






  def     lowercase		(    self			:		Union[str, Any]					)							->						Any:
     __lowerCAmelCase							     =     ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size					)

     __lowerCAmelCase							     =     None
     if self.use_input_mask:
        __lowerCAmelCase							     =     random_attention_mask([self.batch_size, self.seq_length]					)

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

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

     __lowerCAmelCase							     =     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 ,  relative_attention=self.relative_attention ,  position_biased_input=self.position_biased_input ,  initializer_range=self.initializer_range ,  return_dict=lowerCAmelCase_ ,  )

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






  def     lowercase		(    self			:		List[Any] ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		Any ,  lowerCAmelCase_			:		Dict ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		Any ,  lowerCAmelCase_			:		List[Any]					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     TFDebertaVaModel(config=lowerCAmelCase_					)
     __lowerCAmelCase							     =     {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

     __lowerCAmelCase							     =     [input_ids, input_mask]
     __lowerCAmelCase							     =     model(lowerCAmelCase_					)

     __lowerCAmelCase							     =     model(lowerCAmelCase_					)

     self.parent.assertEqual(result.last_hidden_state.shape ,  (self.batch_size, self.seq_length, self.hidden_size)					)






  def     lowercase		(    self			:		int ,  lowerCAmelCase_			:		str ,  lowerCAmelCase_			:		Union[str, Any] ,  lowerCAmelCase_			:		Any ,  lowerCAmelCase_			:		Union[str, Any] ,  lowerCAmelCase_			:		Any ,  lowerCAmelCase_			:		Optional[Any] ,  lowerCAmelCase_			:		Any					)							->						List[str]:
     __lowerCAmelCase							     =     TFDebertaVaForMaskedLM(config=lowerCAmelCase_					)
     __lowerCAmelCase							     =     {
         'input_ids': input_ids,
         'attention_mask': input_mask,
         'token_type_ids': token_type_ids,
     }
     __lowerCAmelCase							     =     model(lowerCAmelCase_					)
     self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.seq_length, self.vocab_size)					)






  def     lowercase		(    self			:		int ,  lowerCAmelCase_			:		Any ,  lowerCAmelCase_			:		List[Any] ,  lowerCAmelCase_			:		List[str] ,  lowerCAmelCase_			:		Dict ,  lowerCAmelCase_			:		Optional[Any] ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		List[Any]					)							->						Optional[int]:
     __lowerCAmelCase							     =     self.num_labels
     __lowerCAmelCase							     =     TFDebertaVaForSequenceClassification(config=lowerCAmelCase_					)
     __lowerCAmelCase							     =     {
         'input_ids': input_ids,
         'attention_mask': input_mask,
         'token_type_ids': token_type_ids,
     }

     __lowerCAmelCase							     =     model(lowerCAmelCase_					)
     self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_labels)					)






  def     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		Dict ,  lowerCAmelCase_			:		Union[str, Any] ,  lowerCAmelCase_			:		List[Any] ,  lowerCAmelCase_			:		List[Any] ,  lowerCAmelCase_			:		Any ,  lowerCAmelCase_			:		Tuple ,  lowerCAmelCase_			:		Any					)							->						str:
     __lowerCAmelCase							     =     self.num_labels
     __lowerCAmelCase							     =     TFDebertaVaForTokenClassification(config=lowerCAmelCase_					)
     __lowerCAmelCase							     =     {
         'input_ids': input_ids,
         'attention_mask': input_mask,
         'token_type_ids': token_type_ids,
     }
     __lowerCAmelCase							     =     model(lowerCAmelCase_					)
     self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.seq_length, self.num_labels)					)






  def     lowercase		(    self			:		List[str] ,  lowerCAmelCase_			:		List[Any] ,  lowerCAmelCase_			:		List[Any] ,  lowerCAmelCase_			:		Tuple ,  lowerCAmelCase_			:		Tuple ,  lowerCAmelCase_			:		List[Any] ,  lowerCAmelCase_			:		int ,  lowerCAmelCase_			:		Any					)							->						int:
     __lowerCAmelCase							     =     TFDebertaVaForQuestionAnswering(config=lowerCAmelCase_					)
     __lowerCAmelCase							     =     {
         'input_ids': input_ids,
         'attention_mask': input_mask,
         'token_type_ids': token_type_ids,
     }

     __lowerCAmelCase							     =     model(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     lowercase		(    self			:		Union[str, Any]					)							->						Any:
     __lowerCAmelCase							     =     self.prepare_config_and_inputs()
     (
         (
         __lowerCAmelCase
     )     ,     (
         __lowerCAmelCase
     )     ,     (
         __lowerCAmelCase
     )     ,     (
         __lowerCAmelCase
     )     ,     (
         __lowerCAmelCase
     )     ,     (
         __lowerCAmelCase
     )     ,     (
         __lowerCAmelCase
     )     ,     
     )							     =     config_and_inputs
     __lowerCAmelCase							     =     {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
     return config, inputs_dict





@require_tf
class     _UpperCAmelCase   (   _UpperCamelCase		,   _UpperCamelCase		,   unittest.TestCase   ):

  """simple docstring"""


  a_      =			(
      (
          TFDebertaVaModel,
          TFDebertaVaForMaskedLM,
          TFDebertaVaForQuestionAnswering,
          TFDebertaVaForSequenceClassification,
          TFDebertaVaForTokenClassification,
      )
      if is_tf_available()
      else ()
  )
  a_      =			(
      {
          """feature-extraction""": TFDebertaVaModel,
          """fill-mask""": TFDebertaVaForMaskedLM,
          """question-answering""": TFDebertaVaForQuestionAnswering,
          """text-classification""": TFDebertaVaForSequenceClassification,
          """token-classification""": TFDebertaVaForTokenClassification,
          """zero-shot""": TFDebertaVaForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )

  a_      =			False
  a_      =			False






  def     lowercase		(    self			:		Optional[int]					)							->						Tuple:
     __lowerCAmelCase							     =     TFDebertaVaModelTester(self					)
     __lowerCAmelCase							     =     ConfigTester(self ,  config_class=lowerCAmelCase_ ,  hidden_size=3_7					)






  def     lowercase		(    self			:		Optional[int]					)							->						int:
     self.config_tester.run_common_tests()






  def     lowercase		(    self			:		Optional[int]					)							->						Optional[int]:
     __lowerCAmelCase							     =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCAmelCase_					)






  def     lowercase		(    self			:		int					)							->						List[str]:
     __lowerCAmelCase							     =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase_					)






  def     lowercase		(    self			:		Optional[Any]					)							->						Tuple:
     __lowerCAmelCase							     =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase_					)






  def     lowercase		(    self			:		Optional[int]					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase_					)






  def     lowercase		(    self			:		int					)							->						int:
     __lowerCAmelCase							     =     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase_					)






  @slow
  def     lowercase		(    self			:		int					)							->						Optional[int]:
     __lowerCAmelCase							     =     TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'					)
     self.assertIsNotNone(lowerCAmelCase_					)





@require_tf
class     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  @unittest.skip(reason='Model not available yet'					)
  def     lowercase		(    self			:		Dict					)							->						int:
     pass






  @slow
  def     lowercase		(    self			:		int					)							->						int:
     __lowerCAmelCase							     =     TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'					)
     __lowerCAmelCase							     =     tf.constant([[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]]					)
     __lowerCAmelCase							     =     tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]					)
     __lowerCAmelCase							     =     model(lowerCAmelCase_ ,  attention_mask=lowerCAmelCase_					)[0]

     __lowerCAmelCase							     =     tf.constant(
         [[[0.23_56, 0.19_48, 0.03_69], [-0.10_63, 0.35_86, -0.51_52], [-0.63_99, -0.02_59, -0.25_25]]]					)
     tf.debugging.assert_near(output[:, 1:4, 1:4] ,  lowerCAmelCase_ ,  atol=1e-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     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  @property
  def     lowercase		(    self			:		Union[str, Any]					)							->						Tuple:
     return (
         "CUDAExecutionProvider",
         {
             "gpu_mem_limit": "15000000000",  # 15GB
             "arena_extend_strategy": "kSameAsRequested",
         },
     )






  @property
  def     lowercase		(    self			:		List[Any]					)							->						int:
     __lowerCAmelCase							     =     ort.SessionOptions()
     __lowerCAmelCase							     =     False
     return options






  def     lowercase		(    self			:		Tuple					)							->						List[Any]:
     __lowerCAmelCase							     =     load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo.png'					)
     __lowerCAmelCase							     =     load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'					)
     __lowerCAmelCase							     =     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
     __lowerCAmelCase							     =     OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
         'CompVis/stable-diffusion-v1-4' ,  revision='onnx' ,  safety_checker=lowerCAmelCase_ ,  feature_extractor=lowerCAmelCase_ ,  provider=self.gpu_provider ,  sess_options=self.gpu_options ,  )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     'A red cat sitting on a park bench'

     __lowerCAmelCase							     =     np.random.RandomState(0					)
     __lowerCAmelCase							     =     pipe(
         prompt=lowerCAmelCase_ ,  image=lowerCAmelCase_ ,  mask_image=lowerCAmelCase_ ,  strength=0.75 ,  guidance_scale=7.5 ,  num_inference_steps=1_5 ,  generator=lowerCAmelCase_ ,  output_type='np' ,  )

     __lowerCAmelCase							     =     output.images[0]

     assert image.shape == (5_1_2, 5_1_2, 3)
     assert np.abs(expected_image - image					).max() < 1e-2

