
'''simple docstring'''
import unittest

from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    MPNetForMaskedLM,
				    MPNetForMultipleChoice,
				    MPNetForQuestionAnswering,
				    MPNetForSequenceClassification,
				    MPNetForTokenClassification,
				    MPNetModel,
				)


class       _lowerCAmelCase   :







							'''simple docstring'''

							def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase=13  ,  UpperCAmelCase=7  ,  UpperCAmelCase=True  ,  UpperCAmelCase=True  ,  UpperCAmelCase=False  ,  UpperCAmelCase=True  ,  UpperCAmelCase=99  ,  UpperCAmelCase=64  ,  UpperCAmelCase=5  ,  UpperCAmelCase=4  ,  UpperCAmelCase=64  ,  UpperCAmelCase="gelu"  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=0.1  ,  UpperCAmelCase=512  ,  UpperCAmelCase=16  ,  UpperCAmelCase=2  ,  UpperCAmelCase=0.02  ,  UpperCAmelCase=3  ,  UpperCAmelCase=4  ,  UpperCAmelCase=None  ,  )					->       Optional[int]:
														_snake_case       =  parent
														_snake_case       =  batch_size
														_snake_case       =  seq_length
														_snake_case       =  is_training
														_snake_case       =  use_input_mask
														_snake_case       =  use_token_type_ids
														_snake_case       =  use_labels
														_snake_case       =  vocab_size
														_snake_case       =  hidden_size
														_snake_case       =  num_hidden_layers
														_snake_case       =  num_attention_heads
														_snake_case       =  intermediate_size
														_snake_case       =  hidden_act
														_snake_case       =  hidden_dropout_prob
														_snake_case       =  attention_probs_dropout_prob
														_snake_case       =  max_position_embeddings
														_snake_case       =  type_vocab_size
														_snake_case       =  type_sequence_label_size
														_snake_case       =  initializer_range
														_snake_case       =  num_labels
														_snake_case       =  num_choices
														_snake_case       =  scope

							def 	lowercase							(self  )					->       Optional[Any]:
														return MPNetConfig.from_pretrained("""microsoft/mpnet-base"""  )

							def 	lowercase							(self  )					->       List[str]:
														_snake_case       =  ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size  )

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

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

														_snake_case       =  self.get_config()
														return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

							def 	lowercase							(self  )					->       Union[str, Any]:
														return MPNetConfig(
														    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  ,  initializer_range=self.initializer_range  ,  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Dict:
														_snake_case       =  MPNetModel(config=UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  model(UpperCAmelCase  ,  UpperCAmelCase  )
														_snake_case       =  model(UpperCAmelCase  )
														self.parent.assertEqual(result.last_hidden_state.shape  ,  (self.batch_size, self.seq_length, self.hidden_size)  )
														self.parent.assertEqual(result.pooler_output.shape  ,  (self.batch_size, self.hidden_size)  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Any:
														_snake_case       =  MPNetForQuestionAnswering(config=UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  model(
														    UpperCAmelCase  ,  attention_mask=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 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Dict:
														_snake_case       =  self.num_labels
														_snake_case       =  MPNetForSequenceClassification(UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  model(UpperCAmelCase  ,  attention_mask=UpperCAmelCase  ,  labels=UpperCAmelCase  )
														self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.num_labels)  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       Tuple:
														_snake_case       =  self.num_choices
														_snake_case       =  MPNetForMultipleChoice(config=UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  input_ids.unsqueeze(1  ).expand(-1  ,  self.num_choices  ,  -1  ).contiguous()
														_snake_case       =  input_mask.unsqueeze(1  ).expand(-1  ,  self.num_choices  ,  -1  ).contiguous()
														_snake_case       =  model(
														    UpperCAmelCase  ,  attention_mask=UpperCAmelCase  ,  labels=UpperCAmelCase  ,  )
														self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.num_choices)  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )					->       int:
														_snake_case       =  self.num_labels
														_snake_case       =  MPNetForTokenClassification(config=UpperCAmelCase  )
														model.to(UpperCAmelCase  )
														model.eval()
														_snake_case       =  model(UpperCAmelCase  ,  attention_mask=UpperCAmelCase  ,  labels=UpperCAmelCase  )
														self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.seq_length, self.num_labels)  )





							def 	lowercase							(self  )					->       Dict:
														_snake_case       =  self.prepare_config_and_inputs()
														((_snake_case),							(_snake_case),							(_snake_case),							(_snake_case),							(_snake_case),							(_snake_case))       =  config_and_inputs
														_snake_case       =  {"""input_ids""": input_ids, """attention_mask""": input_mask}
														return config, inputs_dict


@require_torch
class       _lowerCAmelCase   (   snake_case_					,							snake_case_					,							unittest.TestCase				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						(
							    (
							        MPNetForMaskedLM,
							        MPNetForMultipleChoice,
							        MPNetForQuestionAnswering,
							        MPNetForSequenceClassification,
							        MPNetForTokenClassification,
							        MPNetModel,
							    )
							    if is_torch_available()
							    else ()
							)
							lowerCAmelCase_ 						=						(
							    {
							        "feature-extraction": MPNetModel,
							        "fill-mask": MPNetForMaskedLM,
							        "question-answering": MPNetForQuestionAnswering,
							        "text-classification": MPNetForSequenceClassification,
							        "token-classification": MPNetForTokenClassification,
							        "zero-shot": MPNetForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							lowerCAmelCase_ 						=						False
							lowerCAmelCase_ 						=						True

							def 	lowercase							(self  )					->       Union[str, Any]:
														_snake_case       =  MPNetModelTester(self  )
														_snake_case       =  ConfigTester(self  ,  config_class=UpperCAmelCase  ,  hidden_size=37  )

							def 	lowercase							(self  )					->       List[str]:
														self.config_tester.run_common_tests()

							def 	lowercase							(self  )					->       List[Any]:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_mpnet_model(*UpperCAmelCase  )

							def 	lowercase							(self  )					->       str:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_mpnet_for_sequence_classification(*UpperCAmelCase  )

							def 	lowercase							(self  )					->       str:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_mpnet_for_multiple_choice(*UpperCAmelCase  )

							def 	lowercase							(self  )					->       str:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_mpnet_for_token_classification(*UpperCAmelCase  )





							def 	lowercase							(self  )					->       Optional[int]:
														_snake_case       =  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_mpnet_for_question_answering(*UpperCAmelCase  )


@require_torch
class       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							@slow
							def 	lowercase							(self  )					->       Tuple:
														_snake_case       =  MPNetModel.from_pretrained("""microsoft/mpnet-base"""  )
														_snake_case       =  torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]  )
														_snake_case       =  model(UpperCAmelCase  )[0]
														_snake_case       =  torch.Size((1, 11, 768)  )
														self.assertEqual(output.shape  ,  UpperCAmelCase  )
														_snake_case       =  torch.tensor(
														    [[[-0.0550, 0.1943, -0.0740], [-0.0562, 0.2211, -0.0579], [-0.0437, 0.3337, -0.0641]]]  )
														# compare the actual values for a slice.
														self.assertTrue(torch.allclose(output[:, :3, :3]  ,  UpperCAmelCase  ,  atol=1e-4  )  )








'''simple docstring'''
import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionInpaintPipeline` instead.'
)